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SHILPI MONDAL| DATE: JANUARY 26, 2026 For decades, the corporate security playbook was simple: wait for a breach, identify the culprit, and initiate a "forensic-heavy" investigation to clean up the mess. It was a reactive game of whack-a-mole that treated employees as inherent liabilities. But here’s the problem with that approach: by the time you’re calling in the forensics team, the damage is already done. In an era of hybrid work and rapid data exfiltration, the old perimeter-based defense is obsolete. Leading enterprises are now shifting toward Logical Commander's ethical, proactive approach , moving away from catching "bad guys" and toward a model of support and rehabilitation. The goal isn't just to stop a data leak; it’s to intervene before a stressed employee becomes a malicious insider. At IronQlad, we believe the most resilient organizations don't just monitor their people—they build systems that turn potential risks into their strongest security allies. The Science of "Why": Understanding the Path to Risk   To really fix this, we have to look at the person behind the screen. It’s easy to write off insider threats as bad actors who joined just to steal data. But studies from the Software Engineering Institute (SEI) and the Defense Personnel and Security Research Center suggest otherwise.   The Critical Pathway Model shows us that risk is usually a progression, not a sudden event. It often begins with Personal Predispositions -maybe a history of bending the rules. Then come the stressors—real- life pressures like divorce, debt, or feeling stuck in a career. Eventually, these manifest as concerning behaviors, the visible red flags like logging in at 3 AM or getting into conflicts with the team.   If an organization meets a stressed employee with aggression or cold surveillance, it can inadvertently validate their grievances. As noted in CDSE’s mitigation guidelines , a problematic organizational response can actually push a disgruntled employee toward sabotage. Conversely, an empathetic intervention can off-ramp them before they ever touch sensitive data. Modeling Latent Intent This isn't just psychological theory; it's measurable data. By leveraging causal modeling and Bayesian Networks , modern Security Operations Centers (SOCs) can map the subtle interdependencies between human distress and digital actions.   For example, a sequence that begins with a negative performance review (stressor), moves to late-night VPN access (behavior), and ends with a spike in removable media usage is no longer just "noise." It is a clear signal of latent intent. The "First Do No Harm" Approach to Intervention The most effective insider risk programs operate on a medical principle: First, do no harm. When a potential risk indicator (PRI) lights up, the knee-jerk reaction is often to increase monitoring or prepare for termination. However, MITRE’s research on insider threat solutions  suggests that BDL Bi-Directional Loyalty is a far better predictor of risk. When employees feel the organization is loyal to them, they reciprocate. The Role of Last Chance Agreements (LCAs) So, what happens when an employee has already crossed a line? Rather than immediate termination-which can trigger a "nothing left to lose" mentality savvy organizations are using Last Chance Agreements (LCAs).   As described by the National Security Law Firm , an LCA serves as a structured "second chance." It is a contract where the employer suspends severe discipline in exchange for the employee agreeing to specific behavioral or performance terms.   This isn't just about being nice; it's about risk reduction.   For the Employee:  It offers a dignified path to rehabilitation or a transition to a "clean SF-50" personnel record. For the Organization:  This approach avoids the nightmare of prolonged litigation and keeps valuable institutional knowledge inside the building. But here is the catch: the paperwork has to be solid. According to NALC guidelines , an enforceable LCA needs crystal-clear terms, proof that management actually explained them to the employee, and a realistic timeframe-usually capping out at two years. Technology as a Coach, Not a Spy There is a fine line between protective monitoring and "creepy" surveillance. The latter destroys trust; the former builds it. The new generation of tools, such as Exposure Assessment Platforms , focuses on behavioral baselining rather than invasive snooping. The goal is to establish what "normal" looks like so that deviations stand out. But the real game-changer is automation used for coaching. Imagine an employee tries to upload a sensitive document to a personal Dropbox. Instead of silently logging the event for a future HR hearing, the system triggers a pop-up. As noted in Proofpoint’s analysis of insider threat solutions , this "in-the-moment coaching" can educate the user on secure alternatives immediately. It turns a potential breach into a micro-training session, reinforcing the culture without shaming the individual. Breaking the Stigma of Help-Seeking One of the biggest barriers to rehabilitation is fear. Data from defense studies on cleared employees  shows that many workers avoid Employee Assistance Programs (EAPs) because they fear losing their security clearance. This is a dangerous misconception. The NIST research on science-based commandments for insider threat makes it clear: voluntary help-seeking is a positive mitigating factor. Organizations must actively market EAPs not as a punishment, but as a hallmark of professional reliability. Cultivating a "Security Advocate" Culture The ultimate metric of success isn't how many threats you catch-it's how many you prevent through culture.We’re aiming for a workforce of 'Security Advocates.' As DLT Solutions suggests, that tone has to be set by leadership. When C-suite execs are open about security and mental health, it signals to the rest of the staff that it’s okay to speak up. This culture shift depends on transparency. If we are going to monitor behavior, we need strong governance. By using Data Sharing Agreements (DSAs) , HR, Legal, and Security can collaborate without stepping on privacy laws. It ensures that only the people with a strict 'need to know' see that sensitive data, protecting the firm without treating the employee unfairly. Measuring What Matters Finally, how do we know if rehabilitation works? We have to look beyond simple activity logs. Recidivism Rates:  Are employees on LCAs re-offending? MTTD (Mean Time to Detect):  Are we catching stressors early? Phishing Click-Throughs:  Is our training sticking? As Mimecast’s guide on leveraging metrics points out, tracking the reduction in insider risk incidents over time provides the tangible ROI that boards demand. Conclusion: The Sustainable Path The data from the Resource Exfiltration Project is haunting: 78% of perpetrators exhibited concerning behaviors that colleagues noticed but ignored. We simply can’t afford to ignore these warning signs, but we also have to avoid the trap of viewing every stressed-out employee as a suspect in waiting. The answer lies in blending the insights from the Critical Pathway Model with restorative tools like LCAs and privacy-first technology. This approach allows us to build a security posture that is rigorous without losing its humanity. IronQlad is ready to help enterprises master that delicate balance between technology, psychology, and governance. Because ultimately, transforming risky employees into security allies isn't just the ethical move; it is the only sustainable path to long-term resilience. KEY TAKEAWAYS Context Over Crime:  Most insider threats stem from personal stressors and poor organizational responses, not inherent malice.   Restorative Justice Works:  Last Chance Agreements (LCAs) provide a legal and cultural framework to retain talent while enforcing strict boundaries.   Tech Should Teach:  Use automation for "in-the-moment coaching" to correct behavior instantly rather than just logging it for punishment.   Loyalty is a Two-Way Street: Bi-Directional Loyalty (BDL) is a superior risk metric compared to simple monitoring; employees protect companies that protect them.

SHILPI MONDAL| DATE: JANUARY 27, 2026 For the longest time, industrial security hung its hat on one undeniable physical fact: you can’t hack a network that isn’t connected. We called it the "air gap," and it served as a reliable moat keeping the digital chaos away from our power grids, water plants, and factories. But let’s face reality—that moat is effectively gone. We didn't just build a bridge over it; we paved right through it in our rush for predictive maintenance, real-time analytics, and operational efficiency. TeckPath's 2025 infrastructure  risk analysis shows how the integration of IT and Operational Technology (OT) has created unified Cyber-Physical Systems (CPS). This convergence powers the modern economy, yes, but it also introduces a sobering reality: we now have a shared attack surface where lines of code can trigger physical destruction. The Clash of Cultures: CIA vs. SRP The real challenge here isn't just the technology, though-it’s a clash of mindsets. If you've ever sat in a room with both IT and OT leaders, you have definitely felt that tension. You have IT teams who live and die by the CIA triad: Confidentiality, Integrity, and Availability. Their world revolves around locking down data, and they are used to patching servers every other Tuesday without a second thought. OT engineers, however, operate in a different universe defined by the SRP triad: Safety, Reliability, and Productivity. As noted in research by Palo Alto Networks , an OT asset might have a lifecycle of 30 years, compared to the rapid 3-to-5-year refresh rate of IT hardware. Here’s the rub: in IT, a system reboot to install a patch is a minor inconvenience. In OT, a reboot can mean halting a production line or shutting down a city’s water supply. The Conference on IT Management highlights that this cultural disconnect  often leads to dangerous security gaps, as neither side fully grasps the risks inherent in the other's domain. Feature Information Technology (IT) Operational Technology (OT) Primary Driver Data Integrity & Confidentiality Focus is on protecting information flow and business processes. Safety & Reliability Focus is on protecting physical processes, equipment, and human life. Asset Lifecycle 3–5 Years Rapid refresh cycles driven by software updates and speed. 15–30 Years Legacy stability is prized; equipment often runs for decades. Consequence of Failure Data Breach / Financial Loss Loss of IP, privacy violations, or temporary service disruption. Physical Damage / Safety Hazards Equipment destruction, environmental disaster, or loss of life. The New Attack Playbook: Agents and Interdiction Adversaries particularly state-sponsored groups have realized they don’t need to brute-force their way through a firewall if they can outsmart the system from the inside. AI-Driven Reconnaissance: The days of manual network mapping are behind us. According to TeckPath , threat actors have started using agentic AI frameworks-tools comparable to "Claude Code"-to automate the intrusion cycle. These autonomous agents scan for exposed industrial devices and flag unpatched firmware far faster than any human operator could. This lets attackers scale their operations, hitting hundreds of smaller utilities at once instead of zeroing in on a single high-profile target. The Supply Chain Trojan Horse:   Perhaps more insidious is the shift toward hardware. Why hack a network when you can compromise the device before it even arrives? Microsoft’s security research team describes "interdiction" tactics  where hardware is intercepted in transit for physical tampering. Furthermore, The Cybersecurity Institute warns , allowing attackers to insert backdoors into legitimate, digitally signed updates. When your engineers install that "security patch," they might actually be handing over the keys to the kingdom. Sector Spotlights: Where the Risk is Real The theoretical risks are manifesting in tangible, frightening ways across our critical infrastructure. Water: Target Rich, Cyber Poor: The water sector is in a precarious position. Forescout’s 2025 utility analysis describes the sector as "target rich but cyber poor,"  pointing to fragmented ownership and limited budgets across 150,000 public facilities. We saw this play out in late 2024 with the American Water breach. IBM reported that the utility had to shut down customer portals  to contain unauthorized activity. The attackers didn't use sophisticated zero-days; they often exploited basic flaws like default passwords on PLCs, a vulnerability that the EPA notes persists in over 70% of water systems . Energy: The Inverter Weakness: Our shift toward renewables means we're replacing heavy, spinning generators with digital inverters. BaxEnergy points  to a critical weakness in Inverter-Based Resources (IBRs): they don't have physical inertia. A coordinated attack targeting the digital interfaces of solar inverters could trigger frequency fluctuations severe enough to bring down an entire grid. Moving Defense Strategy "Left of Boom" So, how do we defend systems that can’t be patched and can’t go offline? The industry is moving toward a "Resilient by Design" paradigm. Causal Digital Twins (CDT): Traditional anomaly detection is noisy. It flags every blip, leading to alert fatigue. A promising solution lies in Causal Digital Twins. Researchers publishing via arXiv propose using Structural Causal Models to distinguish between benign operational noise and malicious intent. By understanding the cause  of a sensor reading rather than just its correlation to other data, studies on the Secure Water Treatment  dataset showed a 74% reduction in false positives. It’s about giving operators the context they need to react confidently. Zero Trust in the Factory: We have to stop trusting devices just because they are inside the building. The DoD CIO’s office advocates for applying Zero Trust principles to OT , but with a twist. You can't just authenticate every packet in a real-time system without adding latency. Instead, we use micro-segmentation. As outlined in the ISA/IEC 62443 standards , this involves creating strict zones. If a workstation in the billing department gets infected, the malware shouldn't be able to "pivot" via Modbus protocols to reach the turbine controls. Hardware-in-the-Loop (HIL) Testing: Don't test in production. It sounds obvious, but in OT, it’s rarely followed. EdgeTunePower explains the value of Hardware-in-the-Loop testing , where physical controllers are connected to a real-time digital simulator. This allows engineers to subject their systems to extreme cyber-physical fault scenarios-things you couldn't safely replicate in the real world-to reveal design flaws before they become liabilities. The Future Threat Horizon Looking ahead, two technologies loom large: 5G and Quantum Computing. The rollout of 5G brings edge computing, which decentralizes processing but explodes the attack surface. MDPI research on 5G slicing warns  that millions of IoT sensors could become entry points for DDoS attacks if not properly isolated. Then there is the "Harvest Now, Decrypt Later" threat. SSH Communications Security warns that adversaries are stealing encrypted data today , waiting for quantum computers powerful enough to break RSA and ECC encryption. It’s a ticking time bomb for infrastructure with long-term secrets, necessitating an immediate look at Post-Quantum Cryptography. Resilience is a Culture, Not a Tool Technology controls are vital, but people remain the perimeter. Salvador Technologies reminds us that the human element -from the USB drive plugged in by a well-meaning technician to the click on a phishing link-is often the catalyst for these hybrid attacks. At IronQlad, we believe that securing the converged enterprise requires bridging the cultural divide between IT and OT.   Ultimately, this isn't just a discussion about configuring better firewalls; it’s about creating a unified culture of risk. You have to ask yourself: are your digital security teams and your physical plant managers actually speaking the same language? That is exactly where IronQlad’s integrated cyber-physical practice steps in to help you engineer true resilience. KEY TAKEAWAYS The Air Gap is a Relic:   We have thoroughly blended IT and OT into these complex Cyber-Physical Systems (CPS), and the result is that a digital threat today creates real-world, physical damage tomorrow. The Culture War:   You cannot secure what you don't understand. True resilience requires bridging the gap between IT’s obsession with data privacy and OT’s non-negotiable need for safety and uptime. AI & Supply Chain Threats:   The threat landscape has shifted; attackers are now using autonomous AI agents to speed up reconnaissance and are "seeding" malware into firmware before the hardware even arrives at your loading dock.   Sector-Specific Risks:   We are seeing distinct vulnerabilities everywhere; water utilities are battling legacy budgets and default passwords, while the energy grid is facing new instability risks from digital inverters and coordinated hybrid attacks. Rethinking Defense:   We need to stop chasing perfect security and start building "Resilience by Design." That means deploying Causal Digital Twins to filter out the noise of false alarms and using Zero Trust micro-segmentation to keep the blast radius contained when—not if—an intruder gets in

SHILPI MONDAL| DATE: JANUARY 28, 2026 If you still think of a cybercriminal as someone who works alone that idea is old. The truth is that cybercrime is now like a big business. It is getting bigger and more organized. That is really scary. What is happening in 2026 is that cybercrime is changing in a way. It is moving away from people who're super good at it and towards a system where cybercrime is sold as a service. Cybercrime is becoming like a company, with a platform that makes it easy for people to use. This new way of doing cybercrime is what we call a platform-centric ", as-a-service" architecture. It is making cybercrime a lot easier to do. Cybercrime is becoming more corporate. That is a really bad thing.   According to Darktrace’s 2025 AI and Cybersecurity Predictions , the total addressable market of cybercrime has ballooned to an unprecedented $8 trillion annually. This is a lot of money even when you compare it to what the biggest tech companies make. The reason for this growth is not just because prices are going up. It is because cybercrime is becoming a business. The thing that is making this business grow fast is something called Dark LLMs. These are computer programs that use language but they do not have the same rules and safety features as other programs, like ChatGPT or Gemini. Dark LLMs are language models that are used for bad things and this is what is driving the growth of cybercrime. Dark LLMs and cybercrime are becoming a problem.   For enterprise leaders, this creates a dual-track threat: high-level exploitation is now democratized for low-skilled actors, while sophisticated state-aligned groups can scale their campaign velocity exponentially.   The Genesis of "Uncensored" Intelligence   The transition of generative AI from a productivity tool to a weaponized asset didn't happen overnight, but the pivot point is clear. It began mid-2023 with the release of tools like WormGPT and FraudGPT. Before this, threat actors had to rely on "jailbreaking" commercial models essentially tricking the AI into breaking its own rules. But as LevelBlue notes in their analysis of malicious LLMs , these exploits were volatile and frequently patched.   Developers, such as the creator known as "Last," built these early malicious models on open-source frameworks like GPT-J-6B. As reported by CCGroup’s analysis of the dark side of AI , these models were trained on a concentrated diet of malware-related datasets and illicit dumps. Crucially, the developers stripped away the Reinforcement Learning from Human Feedback (RLHF) layers. Think of RLHF as the conscience of an AI. Without it, you have a "limit-free" environment where the system will happily write code for a polymorphic virus or draft a CEO fraud email without a second thought. By 2025, we moved past these rudimentary tools to Dark LLMs boasting over 80 billion parameters, hosted on self-managed infrastructure to resist takedowns.   The "Evil Twin" Phenomenon and Real-Time Context What keeps me up at night isn't just that these models exist; it's how intelligent they've become. The early versions were static and predictable. This new generation? It adapts. It learns from every interaction. And that's what makes it so dangerous.   Take "DarkBard," for instance. Marketed as the "evil twin" of Google’s legitimate tool, this system introduced a game-changing capability: live internet access. According to Barracuda Networks’ breakdown of DarkBard , this real-time connectivity allows threat actors to craft phishing emails that reference breaking news or specific corporate announcements minutes after they happen. This shifts the threat landscape from static, pre-trained attacks to adaptive, context-aware campaigns. If your CFO speaks at a conference in the morning, a targeted spear-phishing campaign referencing their specific keynote points can be in your employees' inboxes by lunch.   The Economics of the Underground: AI-as-a-Service   The most striking aspect of this new underground is how strikingly normal  the business model looks. It mirrors the legitimate SaaS (Software-as-a-Service) world we operate in every day. Vendors on forums like XSS or BreachForums offer subscription-based access. As Outpost24’s research on Dark AI tools highlights, pricing is tiered: entry-level criminals can purchase a FraudGPT subscription for as little as $200 a month, while elite groups might pay $5,000 for private, dedicated server setups. This has professionalized the value chain. A secondary underground market has emerged around “prompt engineering” itself, with threat actors selling and sharing optimized jailbreak prompts across Telegram channels and cybercrime forums. Security researchers have documented thousand s of malicious prompts being traded, allowing low-skill actors to reliably generate phishing content, scam scripts, and malware with minimal effort. Threat-intelligence analysts have also observed active discussion and distribution of jailbreak techniques within criminal Telegram communities, further lowering the technical barrier to AI-enabled cybercrime  .   The New Attack Vectors: Deception at Scale   So, where is this hitting us the hardest? Right now, deception is the most immediate impact.   Social Engineering 2.0: Generative AI makes it possible to create personalized tricks that can fool people. The European Union Agency for Cybersecurity’s ENISA Threat  Landscape 2025 report says that by 2025 Generative AI was used in more than 80 percent of phishing attacks around the world. This shows that attackers are using Generative AI to send fake messages that are very believable and targeted at specific people. These messages are much better than the way of sending fake messages to lots of people and hoping someone falls for it. Generative AI is helping attackers create messages that're very relevant, to the person they are trying to trick. These AI-enabled lures are culturally nuanced and grammatically perfect, adapting to the target’s role and industry.   Deepfakes and Identity Fraud: The rise of synthetic media has introduced "Deepfake-as-a-Service." As noted by Group-IB’s study on AI cybercrime use cases , underground forums now offer turnkey solutions for real-time face-swapping during video calls. We’ve seen deepfake-related damages reach $350 million in a single quarter of 2025. This technology is being used to impersonate high-level executives to facilitate unauthorized wire transfers, a terrifying evolution of Business Email Compromise (BEC).   Polymorphic Evasion: Perhaps most concerning for the technical teams is the use of AI to create "polymorphic malware." Sify’s analysis  explains that this code dynamically adapts its structure to its environment. Here's what makes this so challenging: by generating fresh payloads on demand, these algorithms can make a single malware strain appear as thousands of different signatures. Traditional signature-based defences become almost useless.   The Future: Agentic Warfare and the Autonomous SOC Looking ahead to 2026 and 2027, the paradigm is shifting again. We are moving from "assisted" AI, where a human drives the tool, to "autonomous" AI agents. Fortinet’s 2026 Cyberthreat Predictions  suggest that purpose-built, autonomous cybercrime agents will soon execute entire segments of the attack chain without human intervention. These agents will hunt down targets, exploit vulnerabilities, and move through internal networks entirely on their own. autonomously.   To fight back, we need to meet AI with AI. That's why the traditional Security Operations Center is evolving into what's being called an "Agentic SOC." As described in Google Cloud’s Cybersecurity Forecast for 2026 , human analysts will soon direct a "swarm" of autonomous AI agents that handle data correlation and initial response.   This is the only way to match the speed of the adversary. It’s no longer human vs. human; it’s algorithm vs. algorithm.   Key Takeaways   The Barrier to Entry is Gone:   For roughly $200 a month: less than a decent SaaS subscription, someone with no technical background can launch the kind of sophisticated attacks that used to require nation-state resources.   Context is King:   Tools like DarkBard don't just send generic phishing emails. They're scraping the web in real-time to make every lure timely, relevant, and almost impossible to distinguish from legitimate communication.   Identity is the New Perimeter:   When deepfakes and AI agents can convincingly impersonate executives or employees, the question isn't just "is my network secure?" anymore. It's "do I actually know who's on it?"   Defence Must Be Automated:   Manual intervention is too slow. The future of defense lies in "Agentic SOCs" where AI manages the initial detection and response layers.   Securing the Future   The underground trade of malicious algorithms is not a temporary trend; it is the new baseline of digital risk. As these tools become cheaper and more autonomous, the stability of the global digital economy depends on our ability to out-innovate the developers of malicious code.   At  IronQlad, and through our partnerships with specialized arms like AmeriSOURCE and AJA Labs, we are helping enterprises build the "Agentic" defense systems required for this new era.   Explore how IronQlad  can support your journey toward a resilient, AI-integrated security posture.

SWARNALI GHOSH | DATE: JANUARY 29, 2026 Introduction We’re all seeing the headlines, but it increasingly seems as if field reality is shifting faster than the news cycle can handle. Ah, the good ole days of when ransomware was just about locking up your files and demanding a Bitcoin payment for the key. We are getting a little beyond those days. In 2026, the age of Reputationware has dawned – a mercenary pivot in which data plunderers care far less about your encrypted backups than they do the sensitive secrets they can weaponize against your brand. If you’ve spent the last year beefing up your recovery protocols, you’re on the right track, but the goalposts just moved. According to Cobalt.io ’s Top Cybersecurity Statistics for 2026 , ransomware attacks are on track to increase by 40% this year compared to 2024. But here’s the kicker: the "2026 Paradox" shows that while attackers are becoming more selective, the average cost per incident has jumped because the targeting is now surgically precise. The AI Catalyst: From Triage to Automated Extortion The biggest game-changer we’re seeing at IronQlad isn’t just more malware; it’s the rise of "Agentic AI." These aren't your standard chatbots. These are autonomous systems capable of managing the entire "kill chain" without a human pulling the strings. It used to be that hackers had a “triage problem.” They would hijack terabytes of information and then sift through it by hand for weeks to find the “good stuff.” Now, they leverage Large Language Models (LLMs) for scanning ex-filtrated data in minutes. They’re not searching for every file; they are seeking specific cues such as “internal audit failure,” “pending litigation” or “whistleblower report.” "Nearly nine in ten organizations (87%) say AI-generated methods, such as deepfakes and automated attack chains, are making threats more convincing than ever," according to CrowdStrike’s latest research cited by Cobalt . What’s even more sobering? As noted in Trend Micro’s 2026 Security Predictions , the "AI-fication" of these threats means that the same tools we use for innovation are being flipped to find vulnerabilities at a speed that simply overwhelms traditional, human-led Security Operations Centers (SOCs). Industry Exposure: Who is in the Crosshairs? In this new landscape, your risk level is often dictated by your industry’s "shame factor" or regulatory burden. At IronQlad, we’ve analyzed how different sectors are being squeezed: Manufacturing:  This remains the top global target. Why? Because downtime is a literal dollar-per-second calculation. IBM’s Threat Intelligence Index  reports that manufacturing accounts for 26% of global cases. Healthcare: This sector faces the most brutal pressure. When lives are at stake, health organizations pay ransom 2.3 times more often than others. On the dark web, Protected Health Information (PHI) is now worth 10 to 50 times more than credit card information. Financial Services:  While they have some of the best defences, they rank second in total payments. Attackers now leverage frameworks like the EU’s DORA, threatening to leak data specifically to trigger massive regulatory fines. The "Death of the Decryptor" and the Rise of Triple Extortion Here’s a phrase I never thought I’d say: encryption is becoming optional for hackers. Groups like BianLian  and Karakurt  have pioneered "encryption-less" attacks. They silently exfiltrate your data, leave your systems running so you don't notice, and then hit you with an extortion demand. We’re also seeing the standard move to "Triple Extortion." It’s no longer enough to steal data; attackers are now launching DDoS attacks or worse directly contacting your customers and partners to tell them their data is about to be leaked. As Heimdal Security’s 2026 trends  point out, 93% of victims who pay extortion fees have their data stolen anyway in these "double extortion" schemes. Identity: The New (and Broken) Perimeter If you’re still focusing your budget solely on endpoint protection, you’re fighting the last war. The new mantra among groups like Scattered LAPSUS$ Hunters  is "log in, don’t hack in."   According to Immersive Labs’ profile on the Scattered LAPSUS$ Hunters supergroup , these actors use sophisticated "vishing" (voice phishing) and AI-driven voice agents to trick help desks into resetting MFA tokens. They aren't looking for a back door; they’re walking through the front door with your employees’ credentials. We also can't ignore the "Supply Chain Wave." The 2025 Salesloft and Drift breach  proved that a single vendor compromise can expose hundreds of organizations through OAuth token theft. In that incident, over 700 organizations were impacted not because their  systems were weak, but because they trusted a third-party integration that held excessive permissions. Building Anticipatory Resilience So, where do we go from here? At IronQlad, we believe the only path forward is "Anticipatory Resilience." This means assuming that a breach will  happen and building your environment to make that data useless to an attacker. Zero Trust Architecture (ZTA):  It’s time to move beyond the perimeter. Every connection, whether internal or external, must be verified. Aggressive Data Governance:  You can't be extorted for data you don't have. If you aren't periodically deleting stale or "dark" data, you’re just leaving ammunition for the next Reputationware attack. Automated Moving Target Defence (AMTD):  To counter AI, you need AI. AMTD constantly shifts your digital environment, making it nearly impossible for automated AI bots to map your network. Identity First Security:  Since 68% of breaches involve a human element, as Cobalt.io  reports, your IAM (Identity and Access Management) strategy is now your most important firewall. Conclusion What’s interesting is that organizations using AI to detect anomalies are finding breaches 80 days faster than those relying on human teams alone. According to IBM’s findings , this speed can save an average of $1.9 million per breach.   The era of Reputationware is terrifying. However, it's also an opportunity for businesses to eliminate the "digital junk" that has been stored for years. By concentrating on identity, data hygiene, and defence, we can leverage away from these modern racketeers.   How is your team adapting from managing data protection to managing reputation? Discover how IronQlad can help you on your pathway towards anticipatory resilience and digital transformation .   KEY TAKEAWAYS   Reputation over Encryption:  Modern attackers prioritize stealing sensitive "shame-inducing" data for extortion over simply locking systems.   AI-Powered Precision:  Agentic AI allows threat actors to automate the discovery of high-value secrets, increasing the cost of breaches by 17%.   Identity is the Target:  Groups like Scattered LAPSUS$ Hunters prefer "logging in" via social engineering and OAuth token theft over traditional malware.   Supply Chain Vulnerability:  Over 36% of breaches now originate from third-party vendors, requiring a shift toward Zero Trust for integrations.

SWARNALI GHOSH | DATE: JANUARY 26, 2026 Introduction The rapid integration of Generative AI (GenAI) into enterprise workflows has fundamentally shifted the security perimeter. We aren't just worried about external servers anymore; the new "breach site" is the internal neural weights of the models themselves. As organizations race to adopt these tools for a productivity edge, many are inadvertently creating a "silent archive" of proprietary source code, internal financial data, and customer PII.   The Rise of Shadow AI and User-Induced Exposure   Here’s the thing: the biggest threat to your data isn't always a malicious hacker in a hoodie. Often, it’s a well-meaning employee trying to finish a report by Friday. This phenomenon, known as Shadow AI , involves the unsanctioned use of third-party GenAI applications without IT oversight.   According to Komprise’s 2025 IT Survey: AI, Data & Enterprise Risk , 90% of IT leaders are worried about shadow AI, and nearly 80% report that their organizations have already experienced negative outcomes—including the leaking of sensitive data. When an engineer pastes proprietary code into a public prompt to debug it, that data is effectively exfiltrated. Because public models often use these prompts for training, your "secret sauce" might eventually be served up as an answer to a competitor's query. "One in every 27 Gen AI prompts submitted from enterprise networks poses a high risk of sensitive data leakage," notes a January 2026 report from Check Point Software .   The "Memorization" Phenomenon: Why AI Doesn't Just Forget   Why does this happen? It comes down to how Large Language Models (LLMs) are built. They are designed to minimize cross-entropy loss, a process that encourages the network to assign high probability to specific sequences. In plain English: the model starts encoding sequences as extractable facts. This "memorization" scales log-linearly with model size. The bigger the model, the higher its capacity to store rare or unique sequences like a specific API key or a private financial figure. Fine-tuning only complicates this. Because fine-tuning datasets are smaller than the massive piles of data used for pre-training, individual private records exert a disproportionate influence on the model’s weights. This makes PII leakage during inference much more likely.   Real-World Fallout: When Theories Become Headlines   The world has witnessed the consequences of these vulnerabilities in actions already. In 2023, Samsung engineers leaked semiconductor source code and meeting notes due to ChatGPT summarization. In a similar incident within the same year, Google had an internal scare. By 2025, these demands will have only intensified. An IBM and Ponemon Institute report for 2025 revealed that the average cost of a data breach in the United States jumped to 10.22 million dollars. The report mentioned an “AI Oversight Gap.” Breaches involving shadow AI cost organizations, on average, $670,000 more than breaches involving approved AI tools.   Regulatory agencies are also keeping a close watch. In 2025, TikTok was penalised with a fine of $600 million on the grounds of improper data transfers while, the Italian regulator slapped the makers of the Replika chatbot for their opaque privacy notices. According to one of Gartner’s forecasts, it was high likely that by 2027, more than 40% of AI data breaches would be caused by cross-border misuse of GenAI.   Peeling Back the Layers: How Data is Extracted   Threat actors have become notably more skilful, using peeling techniques against the safety layers of aligned models.   Divergence Attacks: Hackers create prompts that compel a model to repeat certain terms without an end (e.g. “poem poem poem…”). This could make the model leave its instruction-following mode and produce random pieces of its training data.   Confusion-Inducing Attacks (CIA):  This framework maximizes the model’s uncertainty, triggering a "rote recall" of training sequences.   Model Stealing: Attackers can now recover the embedding projection layers of production models for as little as $20, exposing internal model dimensions.   Moving Toward a Secure AI Strategy   So, how do we fix this? At IronQlad, we believe the answer isn't to ban AI—that's a losing battle. Instead, enterprises are shifting toward multi-layered, proactive security strategies. Two-Sided Guardrails: Systems like SafeGPT are becoming the standard. They use input-side detection to redact PII or sensitive code before it ever reaches the AI. On the flip side, output-side moderation prevents the model from generating memorized or policy-violating content.   Transitioning to Enterprise-Grade Tools:   Moving your team away from public, "free" versions of chatbots is the first step. Licensed versions like Microsoft Copilot or Google Gemini for Workspace ensure that your data is not used for training and stays within your organization’s service boundary.   Machine Unlearning: We are seeing the rise of techniques like LIBU (LoRA-enhanced Influence-Based Unlearning). This allows us to selectively remove the influence of specific data from a model's weights without needing to retrain the entire thing. It's a critical tool for complying with the GDPR’s "Right to Erasure," which is notoriously difficult when data is baked into neural weights.   Robust Governance Frameworks:   Gartner identifies AI Trust, Risk, and Security Management (TRiSM)  as a key priority. By 2026, organizations applying TRiSM controls are expected to consume 40% less inaccurate or illegitimate information. This involves classifying data sensitivity levels, maintaining "approved tool" lists, and ensuring human verification of all AI outputs.   The Path Forward   The "move fast and break things" era of AI adoption is ending. In its place, a more mature, risk-aware approach is taking hold. As we look toward 2027, the organizations that succeed won't just be the ones with the most advanced AI—they’ll be the ones that built their innovation on a foundation of trust and data sovereignty.   Is your organization’s data sitting in a "silent archive" elsewhere? Now is the time to audit your AI usage and implement the guardrails that protect your intellectual property.   Explore how our partners like IronQlad can support your journey toward secure, enterprise-grade AI transformation.   KEY TAKEAWAYS   Shadow AI is the primary leak vector:  According to a report in IRISHTIMES, unauthorized use of AI raises breach costs and regulatory risk, with 90% of IT  leaders worried.   Memorization is a feature, not just a bug:  Larger models have a higher capacity to "remember" and potentially leak rare data strings like API keys or PII.   The cost of oversight is real:  U.S. data breach costs have hit an all-time high of $10.22 million, driven by an "AI Oversight Gap."   Governance is the 2026 competitive advantage: Implementing TRiSM (Trust, Risk, and Security Management) controls can reduce decision-making errors by 50%.

SWARNALI GHOSH | DATE: JANUARY 26, 2026 Introduction The essence of the Metaverse has always been about presence: “being there” in a digital space, rather than merely seeing. However, as we delve into 2026, many IT leaders are discovering that presence comes with a cost. Picture a private virtual company boardroom for a high-stakes executive meeting. Perhaps an actual meeting, or maybe not so private. A hidden presence lurks in the corner, capturing every movement and murmur. The Man-in-the-Room (MitR) attack isn't a fictional plot from a sci-fi thriller; it’s the reality. As the immersive frontier weaponizes, this attack is just one of the ways. As Extended Reality (XR) is permeating healthcare, education, and enterprise, the attack surface, the medium through which threat actors attack corporate and government assets, has shifted from flat screen to human senses.   The Rise of the "Darkverse" and Virtual Malfeasance   For many decades, we’ve been securing the web; however, the Metaverse raises a difficulty that firewalls weren’t necessarily built for. Studies have begun to identify the “Darkverse,” a subterranean place in Metaverse similar to the Dark Web. According to INTERPOL's 2024 White Paper on Metacrime , these unregulated spaces facilitate illicit marketplaces for drugs, weapons, and illegal services, often without any recording or logging mechanisms to assist law enforcement.   But for the enterprise, the threats are often more subtle and damaging. Virtual theft, once limited to gaming items, now involves the exploitation of virtual assets and corporate intellectual property.   We are seeing a rise in:   Virtual Harassment & Stalking:  Tactile 3D avatars mimicking real-world trauma.   Asset Fraud: Using virtual currencies and smart contract applications in decentralised contexts.   Social Engineering: Leveraging the high levels of trust in VR for employee manipulation.   Beyond Phishing: The New Breed of Immersive Attacks   Here’s where it gets technical. In the world of Virtual Reality security , we are moving past simple password theft into "Immersive Hijacking."   The "Inception" Attack:   Named after the famous film, the Inception attack is one of the most sophisticated threats identified by researchers at the University of Chicago . In this scenario, a hacker traps a user inside a malicious VR application that masquerades as the headset’s entire operating system.   When the user thinks they’ve exited an app to return to their home screen, they are actually entering a "simulated home" controlled by the attacker. In their study, this attack successfully deceived 26 out of 27 participants , even when users were highly experienced with VR hardware. From this vantage point, the attacker can eavesdrop on voice commands, modify financial transactions in real-time, and record every keystroke on virtual keyboards.   Face-Mic and Biometric Exfiltration: Most worrying, however, are the hardware's own vulnerabilities. For instance, some scholars have demonstrated how to utilise the Face-Mic method, which utilises the hardware’s own motion sensors, such as accelerometers and gyroscopes, to detect unique facial characteristics during speech. Essentially, an attacker can eavesdrop on an individual's sensitive information without seeking permission to use the mic.   The Jurisdictional Nightmare: Who Policies the Void?   Considering that a criminal offence took place in a virtual room facilitated by an Irish server and involved an individual from India and an attacker from the United States, who would have jurisdictional authority to prosecute?   This is what is known as the "Jurisdictional Conundrum." As Taylor & Francis’ 2025: Criminology of Metacrime, Metadvice, and Cyberjustice, published in 2025, so eloquently puts it, "The absence of particular jurisdictions, as well as the difficulty of imputing acts committed largely via the efforts of an avatar to real people, generates massive legal voids. Current legal systems, such as India's Information Technology Act of 2000 or the U.S. Computer Fraud Abuse Act, were designed for a 2D world. They are hard put to cope with the transient nature of evidence in the VR world, where digital evidence can be deleted or modified instantly, with no physical trail."   Building a "Secure-by-Design" Metaverse   So how do we protect the enterprise in this lawless frontier? At IronQlad, we believe the answer is a multidisciplinary approach, blending technological innovation with rigorous governance. Leverage AI and Blockchain for Accountability:   We are seeing a move toward using blockchain to establish transparent virtual property rights and AI-driven systems to detect harassment patterns in real-time. According to Accenture’s State of Cybersecurity Resilience 2025 , only 10% of organizations are "Reinvention-Ready", meaning they have integrated cyber strategy directly into their digital transformations. Zero Trust in 3D: The principles of Zero Trust must extend to the hardware level. This means:   Secure Authentication:  Requiring the use of MFA for the network ports used by VR headsets. Restricted Sideloading:  Disabling the installation of apps from unknown sources. Regular Resets:  Scheduled resets for headsets to stop any background "spy" scripts, which are used in Inception assaults.   Collaborative Policing: Organizations such as INTERPOL are already leading the way in utilizing this technology for immersive training and recreations of virtual crime scenes. The implications for enterprises will be establishing relationships with organizations that have expertise in physical laws and how they interact with virtual codes.   Looking Ahead: The Human Element   At the end of the day, the most significant vulnerability in any Metaverse cybersecurity  strategy isn't the code, it's the user. The immersive nature of VR makes us more susceptible to deception because our brains are wired to trust what we see and feel. According to PwC’s 2026 Global Digital Trust Insights , 60% of business leaders are making cyber risk a top strategic priority this year. However, tech alone won't solve the problem. We need "metaverse-aware" legislation and a global standard for biometric protection.   The clock is ticking. As we continue our work on these virtual worlds, we must ensure that they remain constructed upon a foundation of trust, rather than pixels.   Is your organization ready to face the challenges of the immersive frontier? Read on to discover the potential that the IronQlad team can bring to the digital revolution.   KEY TAKEAWAYS   Emergence of the "Darkverse":  Unregulated virtual spaces are facilitating illicit marketplaces that lack traditional digital logging.   Sophisticated Attack Vectors:  "Inception" attacks can trap users in a fake VR layer, while "Face-Mic"  attacks steal data via motion sensors.   The Jurisdiction Gap: Borderless virtual worlds make it nearly impossible to determine which legal systems hold authority over "Metacrimes."   Secure-by-Design: Protecting the enterprise requires a Zero Trust approach to VR hardware and the integration of AI for real-time threat detection.

SHILPI MONDAL| DATE: AUGUST 25,2025 In today’s tech-driven world, supply networks are made up of many linked companies, playing a key role in how most businesses run. Because everything's connected, cyber threats spread easier and faster. Hackers now go after weak spots in outside suppliers or apps - a move that’s getting more complex and common - putting vital systems at risk across the globe. To fight back, some use Zero Trust setups, which assume nothing is safe by default, helping block such breaches. Still, even though it sounds strong, putting Zero Trust into practice isn’t smooth; real-life hurdles pop up, demanding careful planning and smart choices. Understanding Supply Chain Attacks and Their Impact Supply chain hacks happen if hackers find soft spots in software, hardware, or services that link companies together. Cases like SolarWinds show the damage - so do incidents involving Kaseya’s ransomware, plus flaws found in systems from Microsoft, Apple, or Atlassian - not one of them escaped major fallout across borders. These attacks usually go after reliable outside software updates or tools to sneak in harmful code - this ends up affecting countless users and devices down the line. Recent reports show around 35.5% of data leaks come from weak spots in the supply chain, which highlights why fresh approaches to digital safety are badly needed. The Role of Zero Trust in Supply Chain Security Zero Trust works by never assuming safety - everything must prove it’s secure every time. Instead of old systems that automatically accept what's within the network walls, this approach questions everything right from the start. It doesn't matter if someone or something is inside or beyond the firewall - it gets checked just the same. Every login, gadget, or app stays under scrutiny unless proven reliable again and again. In supply chains, Zero Trust lowers threats by using tight access rules, ongoing verification, or split networks - slowing hackers who get inside. It requires close checks on every outside partner at each stage they’re involved. Real-World Implementation Challenges of Zero Trust Despite its potential, implementing Zero Trust in supply chains presents multiple complex challenges across technical, organizational, and cultural dimensions. These include: Legacy Systems and Integration Complexities Plenty of companies still run on old tech setups along with separate apps that don't handle real-time verification or built-in encryption. Hooking up these aging systems usually means heavy changes, rebuilding parts from scratch, or swapping them out entirely - just to get the fine-tuned access checks and tracking a Zero Trust setup demands. This tech challenge gets worse when expert help isn't around - since it's hard to find these days. If teams lack know-how or tools, progress might slow down - or just stop altogether - meaning weaker protection than planned. Organizational and Cultural Resistance Switching to Zero Trust isn't only about new tools - it changes how people think and act at work. Staff who’ve always had smooth, instant access might resist tighter checks they see as annoying or nosy. That comfort-driven resistance can show up as irritation, slower output, or simply skipping safety rules. Getting it done means talking early, pulling in key people from areas like IT, security, HR, or ops - while also teaching them what’s needed so they get on board. If you skip these steps, pushback might weaken rollout plus hurt follow-through with Zero Trust rules. Scalability and Performance Challenges Zero Trust means always checking access, confirming who users are, also slicing networks into small zones - these steps tend to slow systems down a bit. When rolled out widely, they might clog up traffic or hurt speed unless planned real carefully. On top of that, heavier loads on log systems, auth servers, so more pressure on enforcing security rules call for flexible cloud-based setups. Without step-by-step growth plans built in early, things might break during operations or leave holes in protection. Lack of Unified Visibility and Tool Fragmentation Zero Trust works best when you can see everything - devices, users, actions - not just parts. But lots of companies struggle because their tools don’t talk to each other, data is scattered everywhere, while device lists stay old and incomplete, making real oversight nearly impossible. This absence of unified control slows down spotting threats, handling breaches, or applying rules - so keeping Zero Trust across large systems becomes tough. Vendor and Third-Party Risk Management Supply chains usually include many outside suppliers, yet their safety habits differ widely. One shaky partner might leave the whole company open to attack. Setting up Zero Trust means doing tough checks on outside partners, keeping an eye on whether suppliers follow rules, while also limiting system access across the supply chain. Handling this involves tricky oversight and constant reviews - something most standard supplier tracking methods just can't manage. Continuous Education and Skill Development The Zero Trust approach needs fresh know-how in tech and security crews - think system layout, handling risks, or keeping up with rules. Over 50% of companies can't get the full upside from it because there aren’t enough trained people, made worse when staff push back on extra steps they see as annoying. Staying up to date with regular training, earning certs, or joining learning sessions helps teams feel ready - while actually being ready - to set up Zero Trust the right way. Skills grow through practice; trust grows when people know what they’re doing. Each step forward reduces guesswork during rollout. Learning isn't a one-time thing - it keeps pace with new threats. Best Practices for Overcoming Implementation Challenges Groups wanting to do well with Zero Trust when dealing with supply chain risks need to: Check all equipment and suppliers carefully - this helps spot parts and know who’s responsible. Use different tools to map what you’ve got plus see where control shifts between teams. Begin with small tests - zero in on key areas to roll out step by step, building as you go. Get different teams involved upfront - this helps everyone stay on the same page while building trust along the way. Use automation or AI tracking systems to boost ongoing checks and spot risks faster. Set up strong outside oversight systems that demand following rules plus limited access rights. Put effort into teaching your team + boosting their knowledge on safety to keep skills growing steadily. Conclusion The rise in sneaky supply chain hacks turns Zero Trust from a good idea into a must-have for solid security. Still, getting it right isn't simple - it demands flexible tech skills, shifts in workplace mindset, also clear direction. Firms tackling obstacles directly while using smart tactics and always improving stay ahead of new risks tied to suppliers. Citations: Securing the supply chain: Embracing zero trust for digital trust. (n.d.). https://www.techuk.org/resource/securing-the-supply-chain-embracing-zero-trust-for-digital-trust.html Supply Chain Attacks: 7 examples and 4 defensive Strategies. (2023, April 14). BlueVoyant. https://www.bluevoyant.com/knowledge-center/supply-chain-attacks-7-examples-and-4-defensive-strategies Wikipedia contributors. (2025, December 9). SolarWinds . Wikipedia. https://en.wikipedia.org/wiki/SolarWinds Wikipedia contributors. (2025, April 27). Kaseya VSA ransomware attack. Wikipedia. https://en.wikipedia.org/wiki/Kaseya_VSA_ransomware_attack 11 Examples of supply chain attacks | DerScanner | blog. (n.d.). https://derscanner.com/blog/11-examples-of-supply-chain-attacks What is zero trust security and why does it matter in 2025? - SecurityScorecard. (2025, August 22). SecurityScorecard. https://securityscorecard.com/blog/what-is-zero-trust-security-and-why-does-it-matter-in-2025 (25) Zero‐Trust Architecture: Implementation Challenges & Solutions | LinkedIn. (2025, August 6). https://www.linkedin.com/pulse/zerotrust-architecture-implementation-challenges-mul5c/ Kumar, V. (2024, July 11). Software Supply Chain with Zero Trust. Practical DevSecOps. https://www.practical-devsecops.com/software-supply-chain-security-with-zero-trust/ Proofpoint. (2025, January 2). What is a supply chain attack? - Definition, examples & more | ProofPoint US. https://www.proofpoint.com/us/threat-reference/supply-chain-attack FireMon. (2025, July 7). Why zero trust fails in the real world | FireMon. www.firemon.com . https://www.firemon.com/blog/why-zero-trust-fails-in-the-real-world-and-what-you-can-do-about-it/#i-the-promise-and-the-paradox-of-zero-trust Owda, A. (2025, October 22). The Hidden Cost of Supply Chain Breaches: 2025 Statistics on Downtime, disruption, and Financial Loss. SOCRadar® Cyber Intelligence Inc.   https://socradar.io/blog/hidden-cost-of-supply-chain-breaches-2025-statistics Security, H. N. (2024, May 31). Lack of skills and budget slow zero-trust implementation - Help Net Security. Help Net Security. https://www.helpnetsecurity.com/2024/05/31/zero-trust-implementation-driver-for-organizations Image Citations: Contributor, G. (2025, January 21). Zero trust in the supply chain: A new era of cybersecurity practices. Technext . https://technext24.com/2025/01/21/zero-trust-supply-chain-cybersecurity/ Patel, J. (2024, February 7). Zero Trust, IT Supply Chain Security are Cybersecurity’s Next Big Buzzwords | GovCIO Media & Research . GovCIO Media & Research. https://govciomedia.com/zero-trust-it-supply-chain-security-are-cybersecuritys-next-big-buzzwords/

SHILPI MONDAL| DATE: DECEMBER 09,2025 Phishing, deceptively crafted messages or communications that trick individuals into revealing sensitive data, remains one of the most persistent and effective forms of cyberattack. Phishing exploits not software vulnerabilities, but human psychology.   In recent years, researchers have begun investigating an approach that goes beyond “phishing detection” alone: profiling individuals’ psychological traits to identify who might be more susceptible to phishing; what we might call “phish-ready users.” This raises both promising possibilities and serious ethical concerns. This article explores the psychological foundations of phishing susceptibility, what recent research reveals about profiling, the potential practical uses and the ethical boundaries that should guide any such effort.   Why phishing works: Psychological levers at play   Social engineering as a psychological attack: Phishing is not (just) a technical exploit. It is, fundamentally, a psychological exploit. Attackers rely on human factors, not code vulnerabilities. Generally speaking, phishing falls under social engineering tactics in cyber threats - techniques tricking people into weakening security. In this setup, scam emails are designed to exploit mental, emotional, and social weak spots. Because they play on how folks choose actions - especially when rushed - they steer users into making illogical moves. Common psychological tactics used by phishers: Empirical and theoretical research r epeatedly shows a set of psychological tactics or “influence levers” that phishers exploit. Among the usual ones are: Authority & Trust:  Impersonating a trusted figure or entity (e.g., a CEO, bank, government agency) triggers deference. People tend to comply when a communication appears to come from a perceived authority. Urgency mixed with fear:   Phrases like "your account gets locked" or "reply fast or else" play on our instinct to rush when stressed, skipping careful thought.  Social Proof & Familiarity:  Folks tend to trust things more when they see coworkers doing the same - safety in numbers kind of thing. When people similar to them go along, doubts drop off. Emotional/ Cognitive Overload:  Too much info, confusing words, or strong emotions might push people into quick reactions without thinking - stuff just hits too hard, so they respond on instinct instead of pausing to reflect. Deception & Illusion of Truth:  Tricking people with fake websites, copied logos, or similar layouts gives a false sense of trust - so users often don’t stop to verify if it’s real. Instead, they just believe what looks familiar. The power ofthese  methods comes from tapping into gut reactions instead of logical thinking. As noted in a foundational review, social engineering attacks “exploit weaknesses in human cognitive functions.” Given how rooted these vulnerabilities are in basic human psychology not in technological flaws they are inherently difficult to “patch” with software alone.   Psychological Profiling: What does research say about “phish-ready users”? In the last few years, experts looking at online behaviour shifted focus - now checking out targets more than just scams. So rather than focusing solely on how phishing works, they’re digging into what sets apart folks who get tricked versus those who don’t - also wondering if these differences can actually be measured. Key findings: Personality traits, behaviours, and demographics: A recent study from 2025 showed some character types, particularly those acting on impulse or feeling anxious, are more likely to fall for online scams. A different report from 2025 looked at many personality and background aspects results linked openness, friendliness, or anxiety to a higher risk of falling for scams; however, being careful lowered it. Research using behavioural metrics (e.g., reaction times, click rates in phishing simulations) confirms that human error, rather than technological failure, remains a primary vector for phishing success. A recent modelling approach, applying frameworks such as the Heuristic‑Systematic Model (HSM) and Cyber‑Routine Activity Theory (Cyber-RAT) to phishing susceptibility among younger users (e.g. Gen Z), suggests that both habitual online behaviour and reliance on heuristic decision-making, rather than systematic evaluation, increase risk. Moreover, recent work has attempted to formalise the “psychological profile” of phish-ready individuals, transitioning from verbal characterisation to data-driven modelling. These studies demonstrate that phishing susceptibility is not random. Social engineering interacts with individual differences personality, cognitive style, emotional state, and even routine behaviours in predictable ways. Taking steps toward focused prevention - here’s hoping smart profiles boost real defences: The results really matter. When people with riskier mindsets get spotted - or when situations and features that boost vulnerability become clear - companies can act. They could step in early. Adjust their approach based on behaviour clues. Use insights to shape better responses. Prevent issues before they grow. Design targeted training or interventions:  Set up specific training sessions tailored to individuals by focusing more on those who tend to act quickly without thinking or who worry excessively, rather than making everyone attend the same generic phishing talk.  Use adaptive defences: Stay flexible with protection by applying extra security layers selectively when someone handles risky information. Use multiple safeguards only when the threat level is high, keeping processes smooth otherwise. Deploy “cyber-psychological hygiene” programs:  Run "digital mental habits" training that combines online safety tips with mindset techniques, helping people build confidence, recognise their own vulnerabilities, and remain calm under stress to reduce impulsive mistakes. Thus, psychological profiling could, in principle, help turn the human weakest link into a manageable part of the defence strategy. Ethical Boundaries and Risks: Why profiling “phish-ready users” is a slippery slope Even though it might help, using mind analysis in online safety brings big worries about ethics, personal space, and unfair treatment. Privacy, consent and autonomy: Informed consent:  Getting permission first matters when using personal info to guess someone’s behaviour. If people don’t clearly agree, it can harm their right to choose what happens to their data. Pulling details about character - say, how anxious or impulsive they are - is tricky ground morally. People should know what's being gathered and why. Potential misuse:  Using such data without caution can lead to unfair treatment, like biased hiring or excessive employee monitoring.  Clear info plus control: People need to know where their data goes, who sees it, while understanding choices like removing details, fixing mistakes, or saying no later on. Risk of unfair treatment or prejudice: Some personality features often link up with things like age, where someone’s from, or their income level. When companies build profiles, it can accidentally push unfair patterns forward - groups might get flagged or left out, not due to real risk, yet shaped by societal conditions instead.   Ethical limits on predictive profiling: Even if statistical correlations exist, they do not determine individual behaviour. Labelling someone “phish-ready” does not guarantee that person will fall for phishing; it only indicates elevated risk. Picking up labels like they're fixed rules can bring about unfair outcomes. Besides, things might get worse - targeting people could shift toward deeper mind checks, which slowly break down honesty, private space, or how we value ourselves. Security vs. human dignity: balancing interests: Organisations have legitimate security concerns. But enforcing profiling-based controls or mandatory “psych tests” may erode trust, harm workplace culture, or violate rights. There needs to be a balance between security and respect for individuals as autonomous, private persons. Practical Use Cases: How and Where Profiling Could Be Applied (Responsibly)   Given both the promise and the perils, where might psychological profiling of phishing vulnerability be applied responsibly and ethically? Voluntary training & awareness programs: Organisations (companies, educational institutions) can offer optional psychology-based training modules. For example: Personality-aware security education for participants who opt in, trainers can highlight common vulnerabilities tied to impulsivity, stress, or cognitive overload. Gamified simulations that help participants learn their own behaviours under stress or urgency to build self-awareness and “phishing self-efficacy.” (Such gamified approaches have been proposed and studied.  Because participation is voluntary and data is not used for punitive measures, this respects autonomy while boosting resilience. Risk-based adaptive security controls (with consent): In high-risk roles (finance, admin, IT, HR), organisations might, with informed consent, apply stronger security controls for individuals with higher phishing susceptibility scores. For example: Mandatory multi-factor authentication (MFA) Additional verification/training before executing critical transactions (e.g., money transfers) Periodic refresher training aligned with psychology-based risk profiles This approach treats psychological profiling as part of a defence-in-depth  strategy, not a judgment, but a risk management tool. People-wide cyber safety studies, along with public info drives: Scientists plus officials might look at grouped, unnamed info to see what habits or mindsets link to bigger scam risks - so they build better awareness drives, school lessons, or local programs while skipping individual tracking. Using psychology tips in computer safety software: Some recent work incorporates “psychological traits” or “persuasion features” into machine learning models that detect phishing content. For example, a 2022 study showed that scoring emails based on psychological persuasion tactics (fear, urgency, desire) improved phishing detection performance. In the future, security tools could combine technical detection with “psychological-risk heuristics,” especially in environments where human judgment remains central (e.g., approving financial transactions). Why Profiling Is Challenging And What We Don’t (Yet) Know Even as early research shows promise, there remain important limitations and uncertainties.   Limited scope and generalisation: Some research uses fake phishing tests, yet actions during actual attacks might change when stress is high and targets are everywhere. People high in neuroticism or impulsive by nature might be more at risk, yet it’s never a sure thing who’ll end up affected. Though these tendencies show some link, they don’t seal anyone’s fate. Just because someone scores one way doesn’t mean harm will follow. It's about chances, not guarantees. Cultural, educational, and social context matters. What holds in one population (e.g., a university cohort, a corporate office) may not replicate elsewhere. Indeed, a recent review of profiling victims of cyberattacks points out that “digital context amplifies certain vulnerabilities and introduces new forms of risk.”   Ethical, legal and privacy hurdles: As discussed earlier, consent, fairness, data protection, and transparency. Many organisations may lack robust governance frameworks to ensure appropriate use.   Risk of over-relying on profiling: neglect of broader defence: Focusing too hard on profiling might make teams slack off when it comes to general security. Phishing protection needs several layers - tools, education, rules - but shouldn’t rely only on behaviour tracking. Guiding Principles Ethical Use of Psychological Profiling for Phishing If organisations, researchers, or policymakers choose to adopt psychological profiling,  they should commit to a set of ethical guardrails: Informed consent: Users must be fully informed about what is being measured, how data will be used, stored, and who can access it. Participation ideally should be voluntary. Be open: let people know what the profile is for, how it’ll be used, where it might fall short, yet also explain possible downsides they could face. Non-punitive use: Profiling scores should not be used for punishment, discrimination, or negative judgments; only for protective or supportive security measures.   Data minimisation & anonymisation:   Only collect what is necessary; aggregate/desensitise data where possible; avoid storing sensitive psychological data longer than needed. Equity & fairness: Ensure profiling does not unfairly target specific demographic groups; monitor for bias. Complementarity, not replacement:   Use profiling as one component alongside technical defences, education, and organisational policy, never as a sole security strategy. Ongoing evaluation: Regularly audit the effectiveness, fairness, and unintended consequences of profiling initiatives. The Future: Toward a Cyber-Psychology-Informed Security Paradigm   The mix of behaviour studies, mind science, and online safety is creating something fresh - labelled by one study as Cybersecurity Cognitive Psychology. While experts from different areas pitch in, ideas start merging; this blend forms a unique field shaped by real-world habits and digital risks. Though it’s just starting out, early results show promise because it focuses on how people actually act, not just theory.   With science moving forward, it could happen sooner than expected:   Standardised psychological-risk assessment tools:    Validated instruments to assess phishing susceptibility.   Adaptive organisational frameworks:   Where security controls dynamically adapt to user risk profiles (with consent).   Public-awareness campaigns informed by behavioural data:    Tailored interventions not just teaching “don’t click suspicious links,” but educating people about when and why they are psychologically vulnerable.   Integration of “human factor” models into detection systems:    Technical tools that flag not only suspicious content, but also risky contexts (e.g. urgent request + high-stress time + user traits). This future holds promise but only if approached with care, respect for individual rights, and a commitment to fairness. Conclusion Phishing is no longer just a matter of technology and firewalls. It’s increasingly a matter of psychology of social engineering, cognitive biases, emotional pressure, and human vulnerability. Research now suggests that some individuals may be more susceptible to phishing than others based on personality traits, behavioural tendencies, and habitual online behaviour. Psychological profiling of “phish-ready users” offers a powerful but delicate tool. If leveraged responsibly with informed consent, transparency, and fairness it can enhance security awareness, reinforce defences, and tailor interventions for those most at risk. But used carelessly, it could become a tool of surveillance, discrimination, or unjust control.   In the end, organisations and society must tread carefully. Profiling should support security; it should never replace the human dignity, privacy, and autonomy of individuals.   With thoughtful frameworks and ethical guardrails, psychological profiling may well become a valuable but always responsibly applied pillar of next-generation cybersecurity defence.   Citations: Wikipedia contributors. (2025, November 20). Phishing. Wikipedia. https://en.wikipedia.org/wiki/Phishing Washo, A. H. (2021). An interdisciplinary view of social engineering: A call to action for research. Computers in Human Behaviour Reports, 4, 100126. https://doi.org/10.1016/j.chbr.2021.100126 Masas, R. (2023, December 20). What is Social Engineering | Attack Techniques & Prevention Methods | Imperva. Learning Centre. https://www.imperva.com/learn/application-security/social-engineering-attack/ Islam, M. R., & Patwary, M. A. (2023). Psychological tactics of phishing emails. Issues in Information Systems, 24 (2), 71–83. https://iacis.org/iis/2023/2_iis_2023_71-83.pdf Marshall, M. (2025, October 18). The Psychology of Phishing: Why humans fall for social engineering and how identity management can protect your enterprise. Avatier. https://www.avatier.com/blog/the-psychology-phishing-identity Rodriguez, R. M., Golob, E., & Xu, S. (2020, July 9). Human Cognition through the Lens of Social Engineering Cyberattacks. arXiv.org . https://arxiv.org/abs/2007.04932 Islam, A., Rashid, M. M., Othman, F., Kaosar, M. G., & Islam, L. (2025). Identifying personality traits associated with phishing susceptibility. Security Journal, 38(1). https://doi.org/10.1057/s41284-025-00466-4 Identifying personality traits associated with phishing susceptibility. (n.d.). Psychologie Légale. https://psychologie-legale.fr/identifying-personality-traits-associated-with-phishing-susceptibility Tjondro, E., Ester, C., Sardjono, Y. G., & Kusumawardhani, A. (2025). Investment scam vulnerability among university students: the role of personality traits and risk tolerance. Cogent Education, 12(1). https://doi.org/10.1080/2331186x.2025.2464309 López-Aguilar, P., Urruela, C., Batista, E., Machin, J., & Solanas, A. (2025). Phishing vulnerability and personality traits: Insights from a systematic review. Computers in Human Behaviour Reports, 20, 100784. https://doi.org/10.1016/j.chbr.2025.100784 Pasupuleti, M. K. (2025). Human-Centric Cybersecurity: Evaluating phishing susceptibility using behavioural metrics. International Journal of Academic and Industrial Research Innovations(IJAIRI), 05(06), 412–424. https://doi.org/10.62311/nesx/rphcrcscrcp4 Gan, C. L., Lee, Y. Y., & Liew, T. W. (2024). Fishing for phishy messages: predicting phishing susceptibility through the lens of cyber-routine activities theory and heuristic-systematic model. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-04083-1 Baral , G., & Arachchilage, N. a. G. (2018, November 22). Building Confidence not to be Phished through a Gamified Approach: Conceptualising Users’ Self-Efficacy in Phishing Threat Avoidance Behaviour. arXiv.org . https://arxiv.org/abs/1811.09024 Shahriar, S., Mukherjee, A., Gnawali, O., & University of Houston. (2021). IMPROVING PHISHING DETECTION VIA PSYCHOLOGICAL TRAIT SCORING. In the University of Houston [Journal-article]. https://arxiv.org/pdf/2208.06792   Image Citations: Blog — Centre for Internet and Society. (n.d.). https://cis-india.org/internet-governance/blog?b_start:int=160&subject=rti Watson, K. (2025, February 11). AI Phishing: How AI is Making Attacks More Sophisticated? Second Cyber. https://seconcyber.com/ai-phishing-how-ai-is-making-attacks-more-sophisticated/

SHILPI MONDAL| DATE: NOVEMBER 12 ,2025 Picture controlling a surgery knife from miles away, sensing every bit of pushback through a smart glove. Or think about guiding a massive machine across the country like you’re right there, hands-on, feeling the grind of steel on steel. This is what the Tactile Internet could bring - sending touch, pressure, and movement back and forth almost instantly, fast enough for humans to react naturally, often said to be within just one millisecond. When actions happen this quickly over open networks, risks shift from theory to reality - not just data leaks but actual injury, broken equipment, lost money. Here’s a close-up view of how these lightning-fast touch systems might get hacked, how attackers could twist split-second delays or fake feedback signals, and what fixes - both tech upgrades and workflow changes - are needed now before things go wrong. Why the Tactile Internet is different (and riskier) than video or audio Web apps usually handle delays of dozens or even hundreds of milliseconds - no problem. But touch-based feedback can’t cope. Studies and guidelines show: tasks like remote surgery, delicate machine handling, or precision robots need response times under 10 ms, sometimes near 1 ms, just to feel natural and stay stable. This tight window totally shifts how we think about safety. Old-school crypto setups take too long 'cause they keep resending data and checking stuff over again; this lag messes up real-time controls plus annoys users when things feel sluggish. Hitting 1 millisecond response time became a big deal in touch-based web experiments. Safety’s about real-world harm: breaches might not just grab info - tampering with touch signals could lead to bodily harm or wreck machines for good. Edge computing (MEC) along with time-sensitive networking (TSN) often get pushed as fixes for speed delays - yet shifting operations closer to devices expands where trust is needed while bringing fresh issues around code and systems. The attack surface: ways hackers might take advantage of touch-based tech Here’s a breakdown of real threat types sorted by their main focus - timing issues, info accuracy, privacy risks, command pathways, or how people see things. Delay plus timing shifts - attacks that mess with when data arrives If control relies on precise timing, someone messing with delays - by slowing things down, adding shaky gaps, or randomly cutting data - can throw off touch-based systems. That might make vibrations wobble unpredictably, push users to react too much or too little, or trigger dizziness when sound, sight, and feel don’t line up. Worst scenarios? Risky machine movements. What sets time-focused hacks apart is they turn timing itself into a tool, not just the data. One person sneaks between two others while altering commands sent back and forth Haptic signals usually come as quick, repeated bursts carrying data on speed, location, or pressure. Instead of altering commands directly, an attacker might tweak responses, nudging the device off course - say, by shifting resistance slightly - which risks faulty or risky actions. Because each packet is tiny and sent nonstop, squeezing in solid encryption gets tough without slowing things down. Replay plus sync-related threats Playing back old haptic data might trick the senses or make motors move again unexpectedly. Since these systems need precise timing, using delayed packet times could mess things up badly. Denial-of-service attacks happen when systems get overwhelmed or misused URLLC or Tactile Internet apps need fixed network capacity. Overloading a system on purpose - say, by swamping a local server or jamming high-priority queues - can block vital touch-based communications, possibly leading to real-world harm. Using slices of the network along with traffic controls offers some protection; still, these setups might draw hackers trying to breach separation layers. Side-channel or inference attacks on touch-based information Haptic signals - like force patterns or movement trails - might accidentally expose private details about activities, items, or someone’s physical state. Take a doctor's touch during treatment; that sensation profile could hint at secret industrial methods or personal medical info. If hackers get hold of sensor records or motion data streams, they could piece together confidential scenarios. Firmware breach plus supply chain weak spots at the device or edge layer Haptic gadgets like gloves or robotic arms, along with local servers, often use custom code and built-in software. If hackers break into that level, they skip firewalls completely - a tainted software upgrade might twist how pressure gets sent or felt. Devices running on nearby computing hubs (like MEC) mean more spots where attacks can happen. Adversarial AI and perception manipulation Some touch-based setups use AI to boost user commands - like guessing moves or measuring push strength. Instead of just working together, sneaky inputs might trick the learning parts into giving risky feedback. This can alter how hard something feels, or mess up timing fixes so the person gets confused. Leaked data along with secret filming Constant touch data collection - saved for records, rules checks, or coaching - opens doors to spying. Leaked files might expose private health details or business info; worse yet, they could sync up with sound or visuals to build deeper profiles. Actual outcomes from real situations Tele-surgery:   Hackers add tiny delays into touch-response signals, making doctors react too strongly mid-procedure - putting patients in real danger. That’s not just speculation - research on ultra-fast networks keeps pointing out how risky this application really is. Remote upkeep in factories:   When force data’s played back, it makes a robot mimic moves - sometimes wrecking equipment or hurting someone close by. Stolen ideas:   Copied touch-based data from a hidden production method shows rivals distinct clues about how it’s made. Big chaos:   If hackers hit several key network hubs at once - those running a city's virtual reality and touch-feedback systems - it might shut down essential urban functions overnight. Why regular safety fixes don’t work as well Cryptography versus speed:   TLS setups, checking certificates, or tagging each packet with a MAC keep data safe - yet they slow things down. Lightweight encryption still demands smart design so it doesn’t mess up real-time responses. Some new approaches test tweaked versions of QUIC or TLS, shaped around touch-sensitive applications. Retransmitting data’s risky: ARQ or retry methods meant to ensure delivery can introduce random lag, messing up real-time touch feedback. Instead, systems should rely on error-correcting codes or multiple signal routes; however, those eat up more network space and make sync harder.   Edge expands where we place trust: Using local servers or device-based computing cuts delays, yet multiplies the systems needing protection. Rules are in place to assist, although real-world use still falls short. Sensible fixes - built in levels, down-to-earth, easy to track It’s not about one quick fix. When it comes to touch-based tech, protection needs to grow alongside speed, consistency, or risk control - depending on the situation. Here's a mix of hands-on fixes and team setups, each with real compromises you’d have to weigh.   Keep key loops close to the user - that’s how layout shields function   Edge computing (MEC):  shift haptic feedback systems closer to endpoints - this slashes lag. Use verified software updates alongside trusted startup routines for tighter device protection. Weigh easier access against stricter machine shielding, fixes, and tracking. Standards from ETSI MEC back these setups.   Deterministic networking but also separation Wired parts use Time-Sensitive Networking (IEEE-TSN), while wireless relies on 5G URLLC along with segmented networks - both keep delays under control and separate traffic flows. When set up right, TSN plus slicing cuts down jitter so odd timing patterns stand out faster. Yet these systems aren’t simple; they need full-path setup to actually work.   Easy-to-use encryption that’s quick plus checks identity without slowing things down Go for encryption that checks identity, fits tiny data chunks, works fast on chips built for it - like AEAD modes with AES-GCM or ChaCha20-Poly1305 when you’ve got special circuitry to handle the math quick.   Shift away from bulky handshakes - use session resuming instead, try out 0-RTT tools if you’ve got solid replay safeguards, or set up shared keys ahead of time for vital connections to cut down on back-and-forth. Some newer studies are testing how QUIC can work better for touch-based interactions.   Extra backups or separate routes keep things running - even if one part fails Shoot copies - or error-tough versions - of touch feedback through separate fast lanes when possible. When a route gets hit or jammed, the rest keep steering alive. Using extra data hurts efficiency, yet it’s usually better than resending later.   Safety covers or backup options nearby Build backup controls right into devices at the spot: when connection gets too shaky, gadgets switch automatically - hold still, turn on vibration alerts, or start self-driving actions. That way, they don’t need constant signal strength to stay safe.   Live verification plus spotting oddities right away Set up tools to watch how long actions take, how much pressure is used, and whether commands look off - like odd spikes in force or weird repeats. Use quick checks that lean on caution so real issues are caught without flagging normal use by mistake.   Secure device lifecycle and supply chain controls Safe startup, verified software, keys built into hardware, or solid check-in during updates must always guard touch devices and local hubs. Checking where parts come from can stop hidden traps planted early.   Keeping data private while tracking info but also recording logs Use tricks such as noise addition, tight permission rules, also local data merging - so touch records meant for analysis won’t get easily traced back or stolen.   Checking proofs along with proof packages When it comes to high-stakes uses - like remote surgery - apply strict verification techniques on timing-critical systems while building clear proof files that show how system stability holds up if networks fail or slow down.   Governance plus rules - yet also response guides People running systems need clear actions for emergencies - like shutting things down fast or alerting teams far away. Working alongside groups such as 3GPP, IEEE, ETSI, and ITU makes it easier to match up on basic safety rules.   Research paths or unresolved questions   Fast-secure coding tools: fresh encryption setup built for tiny, repeated data chunks - works smoothly with dedicated chips. Safe, lightweight login for short-lived access: Methods delivering solid confidence minus lengthy back-and-forth steps. Spotting sneaky changes by checking if touch, sight, or sound don’t match up - using one to test another when something feels off. Figuring out ways to protect touch-based machine learning from sneaky fake signals that mess up predictions - using tricks to spot weak spots plus training systems to resist tampering. Rules for safe touch-data records: Setting which info gets saved, how much time you store it, also ways to guard that data. Latest school studies along with tech talks show one thing’s certain - when setting up safe touch-based setups, each safety rule needs checking for how it affects speed; because of this demand, digging into solutions isn’t just pressing - it pulls from loads of different fields. A worker's to-do list - simple moves to start right away Pinpoint key touch-based signals then check how much delay or variation they can handle from start to finish. Harden endpoints by using secure boot - include signed firmware, while keeping the attack surface small. Run control loops on edge devices, while securing MEC machines using local intrusion detection plus regular updates. Use AEAD for tiny touch-based messages while leaning on quick reconnects or zero-delay starts when it’s secure enough. Use TSN - or something like it - for fixed-line parts, while setting up 5G ultra-reliable links where wireless is needed. Set up safety zones plus actions that kick in when network quality drops without warning. Keep logs minimal using tight access limits along with data masking when checking stats. Try tough tests - like timing tricks or repeating actions - with machine learning edge cases during quality checks. Team up with local staff, legal advisors, or safety reps to build shared response plans for digital and physical threats. One last idea - put people’s safety at the front The Tactile Internet gives wild new powers - like remote surgery, distant factories running live, deep interactive training - but now people and stuff are stuck inside automated digital commands. So safety isn’t about firewalls or codes; it’s whether the system keeps folks from getting hurt, even when bits of it fail. If something goes wrong, can someone halt movement right away, no hesitation? When a local device gets hijacked, is there a way to yank it offline without putting anyone in danger?   Solving these issues means network pros, cyber defenders, control techs, hardware builders, also field experts - like surgeons or factory engineers - have to work together. Just copying old internet safety tricks won’t cut it for touch-based systems; instead, we need fresh thinking around protection, shaped by the tight demands of near-instant response times, while making sure reliability and human safety drive how these devices are built from the start. Citations: Fettweis, G., Boche, H., Wiegand, T., Zielinski, E., Schotten, H., Alcatel-Lucent Stiftung für Kommunikationsforschung, Merz, P., Hirche, S., Festag, A., Häffner, W., Meyer, M., Steinbach, E., Kraemer, R., Steinmetz, R., Hofmann, F., Eisert, P., Scholl, R., Ellinger, F., Weiß, E., & Riedel, I. (2014). The tactile internet. In ITU-T Technology Watch Report. https://www.itu.int/dms_pub/itut/oth/23/01/t23010000230001pdfe.pdf Kalsha, S., & Kalsha, S. (2025, October 1). Tactile Internet: Ultra-Low Latency Networks for Haptic Feedback | QodeQuAY. Qodequay Technologies. https://www.qodequay.com/tactile-internet-haptic-feedback Li, Y., Raison, N., Ourselin, S., Mahmoodi, T., Dasgupta, P., & Granados, A. (2024). AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education. Journal of Robotic Surgery, 18(1), 403. https://doi.org/10.1007/s11701-024-02153-9 The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective. (n.d.). https://arxiv.org/html/2401.01589v1 Sheikh, A. M., Islam, M. R., Habaebi, M. H., Zabidi, S. A., Najeeb, A. R. B., & Kabbani, A. (2025). A survey on Edge Computing (EC) Security Challenges: Classification, threats, and mitigation strategies. Future Internet, 17(4), 175. https://doi.org/10.3390/fi17040175 A comprehensive survey of the tactile internet: state-of-the-art and research directions. (2021). In IEEE Communications Surveys & Tutorials [Journal-article]. https://arxiv.org/pdf/2009.12164 Satka, Z., Ashjaei, M., Fotouhi, H., Daneshtalab, M., Sjödin, M., & Mubeen, S. (2023). A comprehensive systematic review of integration of time sensitive networking and 5G communication. Journal of Systems Architecture, 138, 102852. https://doi.org/10.1016/j.sysarc.2023.102852 Rodriguez-Martin, P., Adamuz-Hinojosa, O., Muñoz, P., Caleya-Sanchez, J., Navarro-Ortiz, J., & Ameigeiras, P. (2025). Empirical analysis of the impact of 5G jitter on time-aware shaper scheduling in a 5G-TSN network. arXiv. https://doi.org/10.48550/arXiv.2503.19555 Benetel, & Benetel. (2025, September 10). Time-Sensitive Networking /TSN) and 5G integration. Benetel - OpenRAN Radio Units.  Opening Possibilities.   https://benetel.com/benetel-white-paper-time-sensitive-networking-tsn-and-5g-integration-enabling-deterministic-and-ultra-reliable-communications/ Maier, M., Chowdhury, M., Rimal, B. P., & Van, D. P. (2016). The tactile internet: vision, recent progress, and open challenges. IEEE Communications Magazine, 54(5), 138–145. https://doi.org/10.1109/mcom.2016.7470948 Mehmet. (2024, January 24). Integration of 5G with Time-Sensitive Networking for Industrial Communications. 5G-ACIA. https://5g-acia.org/whitepapers/integration-of-5g-with-time-sensitive-networking-for-industrial-communications Dahmen-Lhuissier, S. (n.d.). Multi-Access Edge Computing (MEC). ETSI. https://www.etsi.org/technologies/multi-access-edge-computing ? Sengupta, J., Dey, D., Ferlin, S., Ghosh, N., & Bajpai, V. (2024). Accelerating Tactile Internet with QUIC: A Security and Privacy Perspective [Journal-article]. arXiv. https://vaibhavbajpai.com/documents/papers/preprints/2024-arxiv-quic.pdf ?   Mahbub, M., & Shubair, R. M. (2023). Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions. Journal of Network and Computer Applications, 219, 103726. https://doi.org/10.1016/j.jnca.2023.103726 Time-Sensitive Networking (TSN) Task Group |. (n.d.). https://1.ieee802.org/tsn/ ?   Image Citations: Kalsha, S., & Kalsha, S. (2025, October 1). Tactile Internet: Ultra-Low Latency Networks for Haptic Feedback | QodeQuAY. Qodequay Technologies . https://www.qodequay.com/tactile-internet-haptic-feedback

MINAKSHI DEBNATH | DATE: DECEMBER 11, 2025 People don’t want to be “bad actors.” People just wanna finish their tasks quickly, without hassle. If safety rules slow them down, folks tend to skip around them faster but risky shortcuts pop up. Build UX that works with security, not against it, cuts out those detours, slashes danger, helps teams move better and feel better too. Ahead: what’s really going wrong, real-world proof from studies and companies, stories from the field, plus hands-on fixes you can start using now. Security that’s hard to use gets bypassed; bypasses create real risk. Why that matters Industry incident analysis shows the human element (errors, misconfigurations, social engineering, etc.) played a role in a large majority of breaches in 2024 the DBIR finds the human element was a component of 68%  of breaches. Large breach-cost studies show that IT failures and human error account for a substantial share of incidents  roughly about 22–25% of breaches in major industry analyses. Reducing those human-factor failures has measurable financial impact. These numbers make the core point plain: users are a primary factor in real-world security outcomes. That means improving the user-facing side of security is not optional it’s a high-leverage control. How workarounds form Workarounds are often empathetic acts, not malice. A nurse who logs in with a colleague’s credentials to administer time-sensitive meds, a salesperson who shares a password to avoid missing a client call, or an engineer who bypasses a slow VPN during a production incident these are symptoms of design friction colliding with real human needs. Research into workplace and healthcare settings shows this clearly: when workflows are interrupted by poorly designed systems, users create informal fixes that restore flow but undermine safety and these workarounds can lead directly to errors and breaches. Systematic reviews of clinical workarounds find they most often occur because EHRs and related systems don’t fit clinicians’ workflows, and they pose significant safety risks . Usability × Security: the scholarly consensus A quarter-century of usable security research repeatedly finds the same human patterns: when security is painful, people choose convenience over strict compliance often in ways that reduce actual security. Designing security with usability in mind (sometimes called “usable security”) improves adherence to controls, reduces shadow practices, and increases overall system resilience. One practical example from authentication research: risk-based authentication (RBA) which adapts friction to the risk context is often perceived as more usable  and comparably secure versus many step-up 2FA approaches in user studies, illustrating that smarter UX choices can deliver both security and user acceptance. Case studies Case study 1 Healthcare: EHR workarounds and patient safety Several reports suggest nurses often find shortcuts when electronic records take too much time  say, while giving meds. Such fixes might lead to mistakes in treatment or missing notes. The research synthesis shows workarounds are commonly triggered by poor usability or mismatches between system workflows and clinical practice and that redesigning interfaces and workflows reduces the frequency of dangerous shortcuts. Case study 2 Enterprise: authentication friction and shadow IT Across industries, teams frustrated by slow or intrusive authentication sometimes adopt shadow IT (personal cloud services, shared accounts) so work can proceed. Industry breach and compliance reports link these human responses to measurable incidents and to longer detection/containment times meaning the convenience gained by workarounds often costs more later in risk and remediation. (See the DBIR and breach-cost analyses referenced above.) Practical design principles to prevent workarounds Here are concrete, UX-centric strategies that teams can adopt right away. Build for how folks really do their job - watch them closely while they work, like during shadow sessions or real-time interviews, so you can shape tools that match their flow instead of forcing new habits. (Healthcare research above shows the cost of mismatch.) Risk-based checks Add hurdles only if danger shows up, like extra login steps when actions seem off. Default to the secure path; Make the secure option the easiest, fastest, and most convenient by default (single click, SSO, integrated device auth).  Ease mental effort don’t overwhelm people with tricky rules. Swap jargon for plain words, reveal info step by step, add hints right where they’re needed - so folks do the right thing without stress or guesswork. Fix things fast instead of pointing fingers create clear, do-it-yourself ways to regain access, like simple reset options, so people don’t pass around passwords when shut out. Communicate the ‘why’ Humanize policies: explain the business reason for a control in plain terms so people understand the tradeoffs and feel part of the solution rather than policing. A short checklist for teams Swap a tedious task like typing passwords or using a standalone VPN app with something easier, like automatic login or built-in device approval. Try risk-based auth with some users, then check if support calls go down. Log and review shadow IT indicators weekly; treat the top 3 apps as signals for design change. Share one short human story internally each month about how a UX fix prevented a workaround make successes visible. Conclusion: security is a human product Security technology will always be necessary, but the point of technology is to help humans do things well. When we design security as an obstacle, we force users to choose between safety and getting the job done and they will choose the latter. When we design security as a collaborator adaptive, understandable, and convenient we reduce the root causes of workarounds and shift the human element from a liability to an asset. Citations/References Cost of a data breach report 2024. (2024). https://table.media/wp-content/uploads/2024/07/30132828/Cost-of-a-Data-Breach-Report-2024.pdf Verizon Business. (2024). 2024 Data Breach Investigations Report (DBIR) . Verizon. https://www.verizon.com/business/resources/reports/2024-dbir-data-breach-investigations-report.pdf Hospital clinicians’ EHR “workarounds” pose risk to patient safety and quality, study finds. (2019, March 11). Fierce Healthcare. https://www.fiercehealthcare.com/tech/hospital-clinicians-ehr-workarounds-pose-risk-to-patient-safety-quality-study-finds Patil, A. (2025, June 19). Shadow IT in the Cloud: Risks and mitigation strategies . SecPod Blog. https://www.secpod.com/blog/shadow-it-cloud-risks-mitigation-guide/ Garnham, C. (2023, March 21). What is a UX Strategy? Overview, Best Practices & Examples . https://dovetail.com/ux/ux-strategy/ Wiefling, S., Dürmuth, M., & Lo Iacono, L. (2020). More than just good passwords? A study on usability and Security perceptions of risk-based authentication. Annual Computer Security Applications Conference , 203–218. https://doi.org/10.1145/3427228.3427243 Reuter, C., Lo Iacono, L., & Benlian, A. (2022). A quarter century of usable security and privacy research: transparency, tailorability, and the road ahead. Behaviour and Information Technology , 41 (10), 2035–2048. https://doi.org/10.1080/0144929x.2022.2080908 Gulati, B., & Gulati, B. (2025, April 7). UX Design Principles: The 10 rules behind products Users love. Thoughts about Product Adoption, User Onboarding and Good UX | Userpilot Blog . https://userpilot.com/blog/ux-design-principles/ Image Citations: Dhruv, V. (2025, January 20). The Role of Cybersecurity in UX/UI Design - UI UX Design vs Cybersecurity . Better Experience Design. https://yellowslice.in/bed/the-role-of-cybersecurity-in-ux-ui-design/ Ropstam Solutions Inc. (2025, July 3). Best Tips and Tricks To Enhance Security with UI/UX Design . https://www.ropstam.com/tips-and-tricks-to-enhance-security-with-ui-ux-design/

SHIKSHA ROY | DATE: NOVEMBER 12, 2025 In today’s world, where daily life ties closely to online spaces, people carefully arrange who gets their home or old keepsakes. But most forget about something just as real - their digital presence. Whether it's private bank details, photos saved online, or profiles on social apps, this stuff doesn't vanish when we’re gone. Instead of ignoring it, more folks are starting to take steps ahead of time. This guide breaks down why handing off your digital life matters - and how protecting data after death is becoming a real concern. Simple moves now can save confusion later. Understanding Your Digital Estate: More Than Just Passwords Start by listing everything you’ve got online. What’s yours includes every bit of data tied to you. Financial Assets   Online banking plus investment accounts - say, Fidelity or ETRADE - count just as much as digital cash spots such as Bitcoin wallets. Think PayPal or Venmo - they’re part of your money picture too. If you don’t leave access details, those funds might vanish for good. Especially crypto tucked in personal wallets; no key means it’s gone, forever. Media & Entertainment  Photos tucked away in iCloud or Google Photos, tunes stored on Spotify or Apple Music, clips hanging out on YouTube - also films and books bought through Amazon. Each holds deep personal meaning, packed with moments from years gone by. Keep in mind: if it’s tied to a subscription, you could lose entry whenever the service stops sharing - unless you’ve made your own backup. Social Media & Communication Facebook, Instagram, Twitter - these profiles store your life bits. When someone’s gone, folks check them first. Same goes for Gmail or Outlook access. That inbox? Super key. It unlocks other accounts if passwords get lost. Think of it as a digital back door. Business & Storage Folders stored online - say, Google Drive, Dropbox, or OneDrive - as well as sites or web addresses tied to your name. Think things like invoices, legal papers, even unfinished stories or side work sitting around. Let a website address lapse? Someone else might grab it fast - maybe a scammer, maybe a rival. Loyalty Programs Frequent flyer miles or hotel points might actually be worth quite a bit. Some programs let you pass those credits to someone after you die - check their rules first. Even though they’re digital, these perks could turn into real money for family later. Failing to track these items might mean they get misplaced, hard to reach, or exposed to online risks well beyond your lifetime. The Tough Balance: Making Things Reachable For Family Later - Yet Keeping Them Safe From Risks Now The main challenge with digital inheritance? Making sure family can get in - without letting hackers sneak around. It’s about giving access safely, so people you trust aren’t locked out but strangers stay out. One side wants openness, yet the other demands tight security. Sharing info carefully means setting limits that work both ways. You want ease for kin, not loopholes for crooks. The Risk of Digital Abandonment A ghost account pops up when someone ignores their online profile. Such profiles? Easy targets for hackers. Hackers grab them - use them for fake emails, steal identities, or break into linked services and bank details. Dead people’s names sell well in hidden internet corners because no one spots the theft fast. The Burden on Grieving Families Folks dealing with loss face confusion when stuck sorting through unclear rules on various sites, tangled laws, or lost login details - handling it takes ages, wears them out inside, while risking never getting back irreplaceable moments saved online. Building Your Digital Legacy Plan: A Step-by-Step Guide Foresight now means peace later - so your online choices stay honored while keeping info safe. Take this path to build a solid plan for your digital life. Take a   Digital Inventory Start off by listing every digital thing you own. Take each one and write down these details: Platform/Service Name (e.g., Gmail, Chase Bank, iCloud) Username/Account ID The Asset's Nature (e.g., "primary email," "family photo storage") Its Value (sentimental, financial, or both) Leverage Built-in Legacy Tools Folks in big tech get it - so they’re rolling out tools to match: Google Inactive Account Manager:  Google’s Inactive Account tool lets you pick how long before action kicks in. When the timer runs out without activity, it can alert people you’ve chosen - or just share info straight away. No extra fluff - just clear control over what happens if you go silent. Apple Legacy Contact:  Apple lets you pick someone to get into your account later - say, when you’re gone. They’ll see stuff like pics or texts no problem. No passcode needed. This skips any legal hassle. You decide who gets this role ahead of time. Facebook Legacy Contact:  You pick someone close to handle your Facebook if it gets turned into a tribute. They’ll take care of things after you're gone, keeping posts private or sharing memories - whatever feels right. Define Your Wishes for Each Asset What should we do with every account? You might say things like: Sharing: Letting someone close use your stuff - say, pictures - with you. Save key info by asking someone close to grab it before shutting things down. Delete:  Asking to wipe sensitive or personal accounts for good. Save online profiles after someone passes - Facebook or Instagram can keep pages up so friends reflect on memories together instead. Appoint a Digital Executor This job matters a lot. The one you pick as your digital helper must be someone you truly rely on to follow through on your online choices. It could be the same individual handling your regular estate duties - or maybe a tech-savvy contact instead. Talk things over clearly, then name them properly in your will. Secure Your Access Information (Safely) Passwords unlock your online life, yet passing them around openly can lead to trouble. While it might seem harmless, doing so leaves you open to sneaky attacks from others looking to exploit access. Try a password manager - apps like 1Password, LastPass, or Bitwarden come with handy tools such as an Emergency Kit or special contact setup. That way, someone you trust can get into your account if something serious happens. These options let you share access safely when it really matters. Avoid writing passwords in plain text inside your will - once probate happens, anyone can see it. Instead, use a secure method to store access details separately from legal papers. Formalise Your Plan in a Legal Document Even though those tools help, you’ll still need a legal step to back them up. Team up with an estate lawyer so your will covers digital stuff - or set up a separate trust just for that. A law called RUFADAA is now used in nearly every U.S. state. It lets your executor handle online accounts after you’re gone. Conclusion: An Act of Digital Responsibility Handling your online stuff after you're gone isn't just for techies - it's part of everyday life now, so plan ahead. It’s a kind move, really; it helps loved ones skip stress and stay safe online when you’re not around anymore. Make a list of what matters digitally, say what should happen, then pick tools that fit - don’t wait. That way, your internet footprint gets treated right, like anything else you’d pass on. Lock down old data today, simply because they’ll benefit tomorrow. Citations Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf Federal Bureau of Investigation. (2024). Internet Crime Report 2023. FBI Internet Crime Complaint Center (IC3). https://www.ic3.gov/Media/PDF/AnnualReport/2023_IC3Report.pdf American Airlines. (2024). AAdvantage® program terms and conditions. https://www.aa.com/i18n/loyalty/aadvantage-program/aadvantage-terms-and-conditions.jsp#estate Google. (n.d.). Make a plan for your Google account if you pass away. Google Account Help. https://support.google.com/accounts/answer/3036546 Apple Inc. (2024, November 13). How to add a Legacy Contact for your Apple account. Apple Support. https://support.apple.com/en-us/102631 Meta. (n.d.). What happens to your Facebook account if you pass away? Facebook Help Center. https://www.facebook.com/help/103897939701143 Uniform Law Commission. (2019). Fiduciary Access to Digital Assets Act, Revised (RUFADAA). https://www.uniformlaws.org/committees/community-home?CommunityKey=2c84c19c-9bd4-4ba1-9e13-59b0c21ee954 Image Citations From LinkedIn: https://www.linkedin.com/pulse/ai-after-death-digital-identity-neural-echoes-rights-del-valle-djnoe/ Beeble, May 2024. https://beeble.com/en/blog/digital-inheritance-can-you-bequeath-your-account

SHILPI MONDAL| DATE: NOVEMBER 21,2025 From coffee and groceries to blood samples and critical medicines, drones are fast becoming the “last mile” workhorses of modern logistics. Companies from Amazon to Zipline, Wing, UPS Flight Forward and countless startups are betting that small autonomous aircraft will weave through cities and suburbs, dropping packages with near-zero human touch. But there’s a harsh truth buried under the sleek marketing videos: drone delivery networks are also a brand-new, highly exposed cyber-physical attack surface. If a drone is just a flying robot connected to the internet, then hijacking that robot or the network that coordinates thousands of them becomes a tempting prize for cybercriminals, extortion gangs, hostile states and thrill-seeking hackers. This article dives deep into how cyber threats could hijack autonomous logistics systems, what that means in practice, and what regulators, operators and technology providers must do now before “just one compromised drone” turns into “a compromised fleet.” How Drone Delivery Networks Actually Work To understand how they can be hacked, we need to understand what’s under the hood. A modern drone delivery ecosystem typically includes: The drone itself Flight controller & onboard computer:  Runs autopilot, navigation, collision avoidance, and sometimes AI-based vision systems. Sensors: Like GPS or GNSS units, plus IMUs, hook up with cameras - also LiDAR, radar, barometers, or even sound detectors now and then. Radio systems: LTE or 5G, along with Wi-Fi, custom wireless signals, even satellites - these handle control commands plus data feedback . Payload systems:  Winch mechanisms, cargo bays, temperature control for medical goods, etc.Each of these components has firmware and software that can harbor vulnerabilities. Ground systems & cloud Fleet-management platforms:  Cloud services that plan routes, push updates, and monitor every drone in real time. Operations centers:  Where human supervisors oversee hundreds of flights via dashboards. Edge & base stations:  Rooftop “nests” or docks with chargers, antennas, and sometimes local compute units for routing and health checks. These systems are classic IT targets: web APIs, databases, authentication systems, VPNs, and internal dashboards. Traffic management and regulators When flights happen out of sight, the sky gets busier. Tools such as UTM help drones steer clear of each other by adjusting height, order, or restricted spots. In the U.S., green lights from the FAA for companies like Zipline and Wing - alongside matching rules in Europe and beyond - are moving regular BVLOS use forward. If a hacker slips fake info into the UTM system or sneaks through its openings - it’s not only about taking over one drone; it's more like warping the flow of every airborne delivery link tied to that grid. Why Hijacking Drone Logistics Is So Attractive The motives for attacking drone delivery networks are varied and powerful: Financial extortion Ransomware crews could warn they’ll wreck drones, mess up daily runs or spill secret shipping info if the target won’t pay up. Research on cyber risk loss distribution suggests that large-scale attacks on drone delivery networks could lead to substantial aggregate losses across many nodes, from damaged drones to cascading service outages. Cargo theft but also smuggling Hijack a drone while it's flying, send it somewhere else using remote access - grab expensive stuff like gadgets or medicine, maybe even digital cash once it lands. Criminals might sneak drones in to drop off illegal stuff or gear for breaking into systems - this’s happened before when hackers plopped custom Wi-Fi gadgets on office rooftops to grab login details. Kinetic harm and sabotage A drone is a flying kinetic object . Redirect one into crowds, airports, power substations, pipelines or chemical facilities and you have a weaponized logistics platform.  Defence oriented analyses warn that drone hijacking can be used to weaponize systems, including feed switching and flight path control for hostile purposes. Espionage & data theft Drones collect video, sensor data, and sometimes customer or operational data in transit. Hijacking communication links or storage services could expose sensitive delivery patterns, client lists, or location data of high-value targets. Geopolitical leverage & critical infrastructure disruption On a countrywide level, taking over or knocking out big vehicle groups might mess up hospital supplies, crisis aid delivery, or fast-paced distribution networks. Experts are worried - relying on drones built overseas could risk safety, while more thefts keep happening. The Core Cyber Threats to Drone Delivery Networks GPS spoofing: steering drones with fake skies Commercial drones rely mostly on GNSS - GPS, for example - to get around. So they’re at high risk from GPS spoofing; hackers send out phony signals that fool the drone’s system. Academic reviews describe how civil GPS can be spoofed with relatively affordable hardware, allowing attackers to misdirect or fully hijack UAVs. More recent work and field reports show spoofing is not theoretical: it’s been used in conflict zones to mislead or crash drones and even manned aircraft navigation.   In a delivery network, GPS spoofing can: Slowly drift drones off course to a capture point. Nudge them into no-fly zones, triggering regulatory scrutiny or automatic failsafes that ground fleets. Cause controlled landings in attacker-controlled areas by faking the “home” location. Command-and-control (C2) hijacking Drones send info to base units using radio signals - sometimes Wi-Fi if they’re small, sometimes custom frequencies or cell networks like 4G or 5G.These links can be: Eavesdropped:  to harvest telemetry, routes, or encryption keys. Manipulated:  injecting malicious commands. Taken over:   by replaying or forging control packets if authentication is weak. Security checks plus alerts from makers say open C2 links might let hackers take over drones completely - camera, cargo, route included. Tests in everyday situations proved: Some consumer drones got taken over because their Wi-Fi was unsecured or had weak passwords. Small drones losing signal or being taken over, whether piloted by hand or on auto-pilot. In a delivery network where a single operator might supervise hundreds of semi-autonomous drones, one compromised ground system or VPN account can become a pivot point to entire fleets . Jamming and denial-of-service Jamming is cruder than spoofing, but brutally effective: Radio interference might break drone control signals - so they’ll hover, head back, or touch down on their own. Instead of staying online, they react automatically when contact’s lost. No signal means no remote commands, so built-in safety steps kick in right away. GPS signals blocked might mess up navigation, causing odd actions, wrong turns, or forced landings. Research into drone safety shows weak jam resistance leaves groups open to small, low-cost blockers messing up whole routes. Instead of solid backup plans, these systems often fail when hit by simple interference tools. Some experts highlight how easily operations collapse if signals get scrambled nearby. Attacks on AI and autonomy: fooling “smart” drones As drone delivery networks become more autonomous, they lean on AI for: Obstacle detection and avoidance Computer vision–based landing, docking and “follow-me” functions Dynamic route planning in dense urban airspace   This creates new attack surfaces:   Adversarial examples & sensor spoofing A 2025 USENIX paper on UAV “follow-me” systems showed how carefully crafted motion trajectories can trick vision models into following the attacker instead of the intended target. Similar techniques could mislead drones into mis-classifying obstacles or landing zones, causing unsafe landings or route changes.   Model and data poisoning If an attacker accesses the training pipeline or over-the-air model update channels, they can inject biased data or malicious models.   AI-driven defenses vs AI-driven attackers New work highlights both the potential and vulnerabilities of AI-powered UAV systems unauthorized access, model theft, and manipulation of AI decision-making layers are all in scope. The paradox: we rely on AI to make drone operations safer and more scalable, but AI itself becomes a prime target.   Supply chain, firmware and cloud exploitation Drone delivery is a complex mesh of hardware and software from different vendors:  Flight controllers, GPS modules, batteries, cameras, radios, and base stations often come from third parties. Cloud APIs and analytics platforms integrate weather, mapping, customer data and payment systems. Security guidance on unmanned aircraft systems emphasizes: Firmware backdoors and outdated libraries in components. Bad or hacked updates sent through unsafe ways. Cloud setup errors - like open S3 storage, loose IAM rules, or public dashboards.   If one hacker hits a maker, connected company, or patch hub, they could quietly reach tons of drones and their bases from behind.   Attacks on UTM and ecosystem infrastructure As regulators move toward large-scale BVLOS operations, new rules bring new systems and new attack surfaces. A 2025 U.S. FAA & TSA proposal would require drone operators, manufacturers, and UTM data service providers to implement formal cybersecurity programs, including risk assessments and incident response. UTM systems will ingest and broadcast large volumes of real-time positional data and authorizations. If an attacker can: Inject false traffic into UTM, they may cause near-collisions , route disruptions, or overconcentration of drones in specific corridors. Deny service to UTM (e.g., API overload), they may force fleets into fallback modes that curtail operations.   In a fully mature drone logistics ecosystem, UTM becomes as critical and as attack-worthy as a financial market’s matching engine. Real-World Research & Incidents: Proof That Hijacking Is Possible Even before fully commercial drone delivery at scale, research and incidents have shown what is possible. AR.Drone 2.0 hacks:    Security researchers have demonstrated how consumer drones with unsecured Wi-Fi and open ports can be taken over in mid-flight, altering their trajectory and video feed. Jamming and hijacking micro aerial drones: Experimental work on micro-drones showed effective jamming and command hijacking of both autonomous and operator-controlled flights, emphasizing the need for shielding strategies. Follow-me hacks using tricky movements:   Scientists found that doing specific motions lets someone fool tracking tech, shifting the drone’s focus from the real user to themselves, basically taking control by mimicking movement patterns. Roof-top Wi-Fi hacking using drones:  Security experts found a major incident where hackers flew drones onto a business rooftop, dropped custom gear that grabbed login details, then got inside private networks. Rising fake GPS signals in war areas:   Studies from Ukraine and elsewhere show these tricks mess up drone navigation, proving how shaky systems relying on satellite timing really are. Measuring hacking risks in drone shipping: Fresh models guess total losses from various cyber threats during delivery flights, giving solid numbers that help insurance folks and crews plan better. Mid-flight hijack detection systems: In 2025, Florida International University researchers announced “SHIELD,” a mid-flight defense system that uses machine learning to detect hijacking attempts and anomalous behavior in real time.   Taken together, these examples show that hijacking is not a hypothetical “someday” risk—many of the building blocks already exist in the wild. What Happens When a Drone Network Gets Hijacked? Think beyond a single lost drone. In a dense, commercial network, hijacking can ripple through multiple layers:   Tactical impact (minutes to hours) Crashed or stolen drones and cargo Emergency flight suspensions across regions Airspace congestion as drones hold or return to base Operational impact (days to weeks) Route closures or regulatory pauses in specific corridors Overtime costs for human drivers stepping in Incident response, forensic analysis, patching and re-certification   Strategic impact (months to years) Regulatory backlash, stricter requirements and slower approval of BVLOS operations Insurance premium hikes, revised underwriting assumptions for cyber-physical exposure Reputational damage: customers reluctant to trust “flying couriers” with sensitive goods The more autonomous and interconnected the system, the more it behaves like a networked financial system : local shocks can cascade globally. Defending Drone Delivery Networks: From Single Drone to Full Ecosystem No single control can “solve” hijacking. A robust strategy combines technical, operational and regulatory layers. Security by design for drones   Strong cryptography for C2 and telemetry Make sure drones and ground stations verify each other through certs or up-to-date key methods. Go for full-path encryption that changes keys automatically instead of fixed shared ones.   Hardened firmware and secure boot Signed firmware images and secure boot chains prevent attackers from installing modified system images. Lock down debug interfaces (JTAG, UART, USB) in production.   Defense against GPS spoofing Multi-sensor fusion uses GPS, IMU, a barometer, or visual odometry to catch odd shifts in location or motion. Try using GPS from different satellite groups while also tapping into verified signal sources when you can get them. Real-time spoofing detection methods using machine learning on GPS/IMU patterns are promising and should be operationalized.   Resilience to jamming Spectrum-smart radios switching from LTE to 5G, or hopping on satellites when needed - also linking through nearby mesh setups if required. Stable backup mode: if trouble hits, it hovers steady or steers toward a clear spot to land - no sudden drops.   Model security for AI-driven features Safe ways to update models - using checks like digital signatures, confirming changes, plus guards against reverting. Testing object detection by challenging it - like simulating attacks with tricky visual patterns. Trying out landing systems under pressure - using fake threats to check performance. Securing fleet management and cloud infrastructure Zero-trust architecture across control planes Robust controls for user access - using two-step verification while limiting permissions to only what’s needed. Fine-tuned splits in vehicle tracking, smart insights, or info handling for users. Secure APIs Hardened authentication and throttling for route planning, status queries, and UTM interactions. Continuous security testing (SAST/DAST) and bug bounty programs for cloud services. Robust logging and anomaly detection Behavioral models that baseline normal flight patterns, communication rates, and operator actions and raise alerts on deviations. Integrate telemetry with SIEM/XDR platforms so that drone events appear alongside broader enterprise threats.   UTM and regulatory defenses   NIST-aligned cybersecurity frameworks for UAS Emerging FAA/TSA proposals point toward requiring operators and UTM providers to adopt formal cybersecurity programs aligned with NIST standards risk assessments, incident response, continuous monitoring.   Secure data sharing across the ecosystem End-to-end integrity protection for position, intent and authorization messages in UTM. Strict authentication of all actors: operators, service providers, drones and regulators.   Mandatory reporting and coordinated response Clear rules for reporting hijacking attempts, spoofing incidents and near-misses. Shared threat intelligence feeds specifically for UAS ecosystems.   Insurance, risk modeling and governance   Quantitative cyber risk modeling Work like Chiaradonna et al.’s aggregate loss models for drone delivery networks helps operators, regulators and insurers estimate expected loss under various attack scenarios. This directly informs premium pricing, reserve planning, and ROI on security investments.   Specialized insurance products Insurers are already experimenting with coverage for cyber-driven drone hijacking and associated damages.   Board-level oversight For major logistics and e-commerce players, drone delivery risk should be explicitly addressed in enterprise risk management (ERM) and board cyber-risk dashboards.   Training & operational discipline Even the most secure drone hardware can be undermined by weak human practices: Operator training on hijacking indicators (unexpected route changes, telemetry anomalies, unexplained failsafes). Playbooks for safe recovery: when to immediately land, when to return to base, when to notify regulators and customers. Red-teaming plus drills that mimic GPS tricks, signal blocking, or command hijacks - checks full response from start to finish.   The Future: Securing an Airspace Full of Autonomous Couriers   The trajectory is clear: Folks studying the market say drone deliveries will expand fast - mostly in North America - even though hackers could slow things down. While demand rises, safety worries tag along behind. Regulators across the globe are shifting slowly toward regular BVLOS flights, even as they start shaping cyber rules for drones, pilots, or air traffic setups. Scientists are scrambling to keep up - crafting live threat alerts along with smarter security powered by machine learning.   So, pushing self-driving trucks means you’ve got to lock them down fast - otherwise someone else might beat you to safety. Drone delivery will only gain public trust if every flight is backed by: Tamper-resistant hardware Cryptographically strong communication Intelligent, AI-aware defenses Clear rules that work well but also bounce back when tested The skies soon might swarm with self-flying delivery bots - yet who really runs them, owners or hidden hackers, hinges on choices we’re making today.   Citations: Daleo, J. (2023, September 21). Zipline secures key operational approval for drone package deliveries. FreightWaves. https://www.freightwaves.com/news/zipline-secures-key-operational-approval-for-drone-package-deliveries ? Khan, S. Z., Mohsin, M., & Iqbal, W. (2021). On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions. PeerJ Computer Science, 7, e507. https://doi.org/10.7717/peerj-cs.507 McNabb, M. (2023, September 19). FAA authorizes zipline for drone delivery beyond visual line of sight. DRONELIFE. https://dronelife.com/2023/09/19/faa-authorizes-zipline-for-drone-delivery-beyond-visual-line-of-sight/ ? Chiaradonna, S., Jevtić, P., & Lanchier, N. (2024). Cyber risk loss distribution for various scale drone delivery systems. Risk Sciences., 1, 100009. https://doi.org/10.1016/j.risk.2024.100009 Tripathi, L. C. N. (2025, January 24). Emerging threats of drone feed hijacking: Strategic implications for Indian defence . raksha-anirveda.com . https://raksha-anirveda.com/drone-hijacking-threats-impact-on-defence/?srsltid=AfmBOorVXHNFY6oAKMgF5XGeNAIJega2axP3GiRzVvRdvGh1gMBJhMF3&utm Exploring jamming and hijacking attacks for micro aerial drones . (n.d.). https://arxiv.org/html/2403.03858v1 Alsadie, D. (2025). Cybersecurity and artificial intelligence in unmanned aerial vehicles: emerging challenges and advanced countermeasures. 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In Proceedings of the 3rd USENIX Symposium on Vehicle Security and Privacy [Conference-proceeding]. https://www.usenix.org/system/files/vehiclesec25-li-jiarui.pdf ? Crowell & Moring LLP. (n.d.). Hacker No fly Zone: FAA and TSA propose cybersecurity rules for drone ecosystem. Crowell & Moring - Hacker No Fly Zone: FAA and TSA Propose Cybersecurity Rules for Drone Ecosystem. https://www.crowell.com/en/insights/client-alerts/hacker-no-fly-zone-faa-and-tsa-propose-cybersecurity-rules-for-drone-ecosystem ? Exploring jamming and hijacking attacks for micro aerial drones. (n.d.). https://arxiv.org/html/2403.03858v1 ? Drone package delivery market in North America: Forecast & analysis. (n.d.). https://www.marketsandmarkets.com/blog/AD/drone-package-delivery-market-in-north-america ? Avantika. (2024, March 15). The game of drones of hovering cybersecurity risks. The Cyber Express. https://thecyberexpress.com/drones-hacking-hovering-cybersecurity-risks/ Federal Aviation Administration. (2024). Notice of proposed rulemaking: Beyond visual line of sight (BVLOS) operations [PDF]. U.S. Department of Transportation. https://www.faa.gov/newsroom/BVLOS_NPRM_website_version.pdf OPSGROUP. (2024). GPS spoofing final report: Working group OG24 [PDF]. OPSGROUP. https://ops.group/dashboard/wp-content/uploads/2024/09/GPS-Spoofing-Final-Report-OPSGROUP-WG-OG24.pdf Image citation: Cybersecurity Challenges in Drone-Based Systems | Anvil Labs. (n.d.). https://anvil.so/post/cybersecurity-challenges-in-drone-based-systems Avantika. (2024, March 15). The game of drones of hovering cybersecurity risks. The Cyber Express. https://thecyberexpress.com/drones-hacking-hovering-cybersecurity-risks/ Figure 1. Scenario of GPS spoofing attack on a UAV. A UAV takes off. . . (n.d.). ResearchGate. https://www.researchgate.net/figure/Scenario-of-GPS-spoofing-attack-on-a-UAV-A-UAV-takes-off-from-the-starting-position-and_fig1_365229005 Alsadie, D. (2025). Cybersecurity and Artificial Intelligence in Unmanned Aerial Vehicles: Emerging Challenges and Advanced Countermeasures. IET Information Security, 2025(1). https://doi.org/10.1049/ise2/2046868