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SHIKSHA ROY | DATE: NOVEMBER 12, 2025 In an era where our lives are intricately woven into the digital fabric, we meticulously plan for the distribution of physical assets like houses and heirlooms. Yet, many of us overlook a vast and growing part of our estate: our digital footprint. From cherished family photos in the cloud to sensitive financial records and social media accounts, our digital lives demand a new kind of estate planning. This comprehensive guide explores the critical importance of digital inheritance and the emerging field of posthumous data security, providing a roadmap to secure your cyber legacy.   Understanding Your Digital Estate: More Than Just Passwords   Before you can manage your digital afterlife, you must first take stock of what you own. Your digital estate encompasses all the digital assets you control.   Financial Assets   Online banking, brokerage accounts (e.g., Fidelity, E*TRADE), cryptocurrency wallets (e.g., Bitcoin, Ethereum), and payment platforms like PayPal and Venmo. These often hold immediate monetary value and are critical for settling your estate. Cryptocurrency, if stored in a private wallet, can be permanently lost without the specific keys.   Media & Entertainment  Libraries of photos on iCloud or Google Photos, music on Spotify or Apple Music, videos on YouTube, and purchased movies/books on Amazon. These are assets of immense sentimental value, representing a lifetime of memories. Note that with subscription services, access may be lost unless content is personally downloaded and saved. Social Media & Communication Accounts on Facebook, Instagram, Twitter, LinkedIn, and email providers like Gmail or Outlook. These accounts hold your personal history and are often the first place loved ones look for closure. Email is a particular linchpin, as it can be used for password resets on other critical accounts.   Business & Storage Documents on cloud services like Google Drive, Dropbox, or Microsoft OneDrive, and websites or domains you own. This can include everything from tax records and contracts to unpublished manuscripts or creative projects. A domain name left to expire can be snapped up by cybersquatters or competitors.   Loyalty Programs Frequent flyer miles, hotel points, and credit card rewards can often hold significant value. Many programs' terms of service allow points to be transferred to a beneficiary upon death. These intangible assets can amount to substantial financial value for your heirs.   Failing to account for these assets can lead to them being lost, inaccessible, or vulnerable to cyber threats long after you're gone.   The Dual Challenge: Accessibility for Heirs vs. Security from Threats The core dilemma of digital inheritance is balancing two opposing needs: providing your loved ones with access while protecting your data from malicious actors.   The Risk of Digital Abandonment When a digital account is left unattended, it becomes a "ghost account." These accounts are low-hanging fruit for cybercriminals. They can be hijacked for phishing scams, identity theft, or to gain access to connected accounts and financial information. A deceased person's identity is a valuable commodity on the dark web, as the theft often goes unnoticed for a long time.   The Burden on Grieving Families Without clear instructions, grieving family members are left to navigate a maze of different platform policies, legal hurdles, and forgotten passwords. The process is often time-consuming, emotionally draining, and can lead to permanent loss of precious digital memories.   Building Your Digital Legacy Plan: A Step-by-Step Guide   Proactive planning is the only solution to ensure your digital wishes are respected and your data remains secure. Follow these steps to create a robust digital legacy plan.   Take a   Digital Inventory Begin by creating a comprehensive list of all your digital assets. For each entry, note the following: 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 Major tech companies have recognised this need and offer their own solutions: Google Inactive Account Manager :  Allows you to set a timeout period. If your account is inactive for that time, it will either notify your trusted contacts or automatically send them your data.   Apple Legacy Contact :  Lets you designate one or more people who can access your Apple Account data (including photos, messages, and notes) after your death, without needing a password or going to court.   Facebook Legacy Contact :  You can choose a friend or family member to manage your memorialised account.   Define Your Wishes for Each Asset What do you want to happen to each account? Your instructions could include:   Transfer:  Granting access to a family member (e.g., for photo libraries). Archive:  Instructing a loved one to download and save important data before closing the account. Delete:  Requesting the permanent deletion of sensitive or private accounts. Memorialise:  For social media platforms like Facebook and Instagram, which offer memorialisation settings that preserve the account as a place for remembrance.   Appoint a Digital Executor This is a crucial role. Your digital executor is the person you trust to carry out your digital wishes. This can be the same person as your traditional estate executor, or someone with more technical aptitude. Discuss your plans with them and formally appoint them in your will.   Secure Your Access Information (Safely) Passwords are the keys to your digital kingdom, but sharing them directly poses a security risk.   Use a Password Manager :  Services like  1Password, LastPass, or Bitwarden  offer secure "Emergency Kit" or designated "Emergency Contact" features. These allow you to grant a trusted person access to your vault in a predefined emergency.   Avoid Plain Text in Wills :  Never put passwords directly in your will, as it becomes a public document upon probate.   Formalise Your Plan in a Legal Document While the tools above are helpful, they should be backed by a legal directive. Work with an estate planning attorney to include a  digital assets clause  in your will or a standalone  digital assets trust . The Revised Uniform Fiduciary Access to Digital Assets Act (RUFADAA) has been adopted by most U.S. states and gives your executor the legal authority to manage your digital property.   Conclusion: An Act of Digital Responsibility Managing your digital inheritance is no longer a niche concern but a fundamental aspect of modern life and responsible estate planning. It is a final act of love and consideration for those you leave behind, sparing them from bureaucratic confusion and protecting them from digital harm. By taking the time to inventory your assets, define your wishes, and use the available tools, you can ensure your digital legacy is handled with the same care and intention as your physical one. Secure your past to protect their future.   Citations What happens to your Facebook account if you pass away | Facebook Help Center. (n.d.). https://www.facebook.com/help/103897939701143 How to add a Legacy Contact for your Apple Account - Apple Support. (2024, November 13). Apple Support. https://support.apple.com/en-us/102631 Uniform Law Commission. (2019). Fiduciary Access to Digital Assets Act, Revised. https://www.uniformlaws.org/committees/communityhomeCommunityKey2c84c19c-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 25,2025 What “Shadow AI” Actually Is Shadow AI is the use of AI tools ; especially generative AI and large language models (LLMs) without approval, monitoring, or governance from IT or security.   Think of it as Shadow IT 2.0: Instead of unsanctioned SaaS, it’s unsanctioned AI copilots, browser extensions, and LLM chatbots. Instead of “rogue” CRMs, you now have “rogue” model endpoints quietly ingesting sensitive data. Recent research shows how deep this runs inside security teams themselves: 87% of cybersecurity practitioners say they’re already using AI in daily workflows. Nearly 1 in 4 admit to using personal ChatGPT accounts or browser extensions outside formal approval, logging, or compliance. A Splunk-based survey found 91% of security executives and professionals are using generative AI, with nearly half calling it “game-changing” for security teams. These aren’t unaware end users ; these are the people writing and enforcing security policy. Why Shadow AI Hits the SOC Harder Than Anywhere Else SOCs are perfect breeding grounds for shadow AI:   Pressure and burnout Analysts are swamped with alerts, false positives, and noisy telemetry. Anything that shortens investigations or writes cleaner incident reports is irresistible.   Text-heavy workflows Logs, tickets, emails, runbooks, threat intel reports, forensics notes SOC workflows are built on text. That’s exactly what LLMs consume and generate best.   Easy to hide A browser extension that “summarizes logs” or a prompt pasted into ChatGPT looks harmless. Traditional monitoring tools barely notice prompt-level activity.   Security pros trust their own judgment Analysts think:  “I know what I’m doing; I won’t paste anything too sensitive.” But under time pressure, that line moves. The result:  unapproved LLM tools become embedded in day-to-day incident response, with no visibility and no controls. The Shadow AI Toolset Inside Your SOC Shadow AI in security teams usually appears in four flavors: Consumer LLM accounts Public ChatGPT, Claude, Gemini, etc., used with personal or work emails. Analysts paste logs, phishing emails, error messages, or even snippets of proprietary detection content into these tools. Browser extensions and plugins “Explain this log,” “summarize this page,” “rewrite this alert.” Many of these extensions proxy your data through third-party servers you’ve never vetted. Unapproved security copilots AI assistants bundled with security tools (SIEM, EDR, ticketing) where AI features are enabled by default but never formally risk-assessed.   Side-loaded or local models Analysts running “private” LLMs on workstations or lab servers, pulling in internal datasets without any formal governance.   Each category brings different risks, but they all share one theme: no official owner, no audit trail, no documented risk acceptance.   The Risk Categories Nobody Wants to Own           Data Leakage & “Prompt Drip” Analysts often paste sensitive information into LLMs ; IP addresses, usernames, PII-filled emails, internal playbooks, or detection logic. Public AI tools may store or share this data, even in “anonymized” form. The Samsung case proved how easily confidential code can leak. In a SOC, this can expose attack timelines, detection techniques, and regulated data (PHI, financial info), creating instant GDPR, HIPAA, or CCPA violations when used in non-compliant tools. Zero Control Over Model Training When data leaves your environment, you lose control over: How long it’s stored Whether it trains the model Who benefits from your proprietary detection logic Your own threat intel could end up improving models attackers use. Compliance & Legal Exposure Shadow AI directly clashes with rules governing: Data location Data processors Data usage GDPR, HIPAA, and industry regulations may be broken by using unapproved LLMs for customer or employee data. If you can’t prove where data went or how it was protected, regulatory defense becomes extremely difficult. Hallucinations as Operational Risk LLMs can hallucinate fake CVEs, wrong detection queries, incorrect file paths, or made-up MITRE techniques. In a SOC, this causes: Time wasted on false leads Broken detections and blind spots Misleading guidance during high-stress incidents Acting on hallucinated outputs can introduce AI-driven negligence into your RCA. Expanded Attack Surface via Prompt Injection AI assistants integrated into ticketing systems, EDR, or SOAR can be manipulated through hidden prompts in emails, logs, or websites. Examples: A malicious email instructs the AI to close an alert A compromised site feeds hidden prompts to mislead the investigation   Shadow-built AI integrations rarely have proper guardrails or threat models.   Invisible Decisions & No Audit Trail Shadow AI undermines SOC accountability: LLM suggestions aren’t logged or reviewed Final actions appear in reports, but the AI influence does not This leads to incomplete RCAs, weaker regulatory reporting, and complex legal discovery. It destroys the transparency SOCs are built on. Why Shadow AI Is Different from Classic Shadow IT It’s tempting to treat shadow AI as just another flavor of shadow IT. It isn’t. Data Gravity Is Stronger Shadow IT often involves tools that store copies  of datasets. Shadow AI tools actively pull new data through prompts, day after day.   Natural Language Makes It Frictionless With shadow AI, you don’t need API keys or CSV exports. You just paste text and ask. That makes risky use effortless at scale. Model Behavior Is Probabilistic A shadow SaaS tool may leak data, but its behavior is deterministic. LLMs generate outputs that are non-deterministic and hard to reproduce, complicating investigations. Traditional Security Controls Don’t See It Legacy DLP, SIEM, and endpoint tools weren’t designed to inspect prompt-level interactions, nor to detect AI-specific threats like jailbreaks and prompt injection. Defenders and Attackers Use the Same Tools The same generative AI platforms that help analysts summarize incidents also help attackers craft phishing, malware, and social engineering scripts faster. Shadow AI isn’t just shadow IT with a new logo; it’s a behavioral and architectural shift in how work gets done. Real-World SOC Scenarios Where Shadow AI Shows Up Scenario 1: Phishing Triage at Speed An analyst gets 50 similar phishing emails and uses a personal AI account to: Summarize the campaign Extract URLs and payloads Draft user notifications Risks: Email metadata, internal routing patterns, and user addresses leave your environment. The AI provider could use that corpus to train future models, mixing your data with everyone else’s. You've just sent PHI to an unapproved processor if the email contains regulated data, such as medical records. Scenario 2: AI-Assisted Threat Hunting An engineer asks an LLM: “Write a Splunk query to find signs of this specific attack, based on these log fields…” They paste sample logs containing: Internal hostnames Specific detection gaps Vendor details The LLM returns a query but fabricates field names and logic. The engineer, in a rush, deploys it as-is. Result: The new detection silently breaks or only matches a tiny fraction of events. Leadership believes coverage improved. In reality, coverage has regressed, and no one knows the LLM was involved. Scenario 3: Incident Communications During a breach, comms must be precise and defensible. A leader uses an unapproved AI assistant to: Draft regulator notifications Prepare board updates Write customer emails The tool introduces: Over/understatements of scope Incorrect regulatory references Ambiguous timelines These drafts are sent after being slightly revised. The organization now has to defend AI-influenced language that it can't even fully reconstruct after regulators and plaintiffs' attorneys scrutinize every word. How Big Is the Problem? The Data Says: Huge Multiple surveys paint the same picture:   A 1Password report found 52% of employees  have downloaded unauthorized apps, and around a third ignore AI usage policies altogether. Within security teams specifically, 87% use AI in workflows , and nearly a quarter do so through personal or unsanctioned channels. Combine those numbers, and a blunt conclusion emerges: If you haven’t formally deployed AI into your SOC, you almost certainly have shadow AI already. From Blind Spot to Blueprint: Governing AI in the SOC Banning AI doesn’t work it only pushes usage underground. SOCs need a governed, realistic AI adoption framework . Start with Visibility First identify where shadow AI already exists: Run anonymous surveys on analyst AI usage Review DNS, proxy, and firewall logs for AI domains Check which tools (SIEM, EDR, ticketing) already have embedded AI features Share findings with leadership to replace risky use with sanctioned options.   Classify Data for AI Use Not all SOC data belongs in prompts. Create three clear categories:   Red: Never leaves the environment (secrets, credentials, sensitive personal data, crown-jewel IP) Amber: Only for enterprise-controlled models Green: Allowed with approved third-party AI providers under contract/DPA Classify logs, alerts, and cases accordingly so analysts know what’s safe to use.   Provide a Safe, Approved AI Option Shadow AI thrives when official tools are slow or unavailable. Offer: A secure enterprise AI assistant (VPC-hosted or strong isolation) Workflows for explaining alerts, summarizing long tickets, drafting communications, and suggesting detection logic Include safeguards such as: No training on prompts without opt-in Full prompt/response logging RBAC and strict segmentation   If the official tool is painful to use, analysts will revert to shadow AI.   Write Clear, SOC-Specific AI Policies Avoid vague rules. Instead specify: Allowed: Summarizing tickets without regulated data Drafting initial incident reports/playbooks Explaining unfamiliar technologies   Not allowed: Pasting secrets or credentials Pasting customer-identifiable details without approval Drafting legal/regulatory/HR communications Tie violations to existing policy, balancing enforcement with training. Integrate AI Into Threat Models Modern SOC threat models must ask: How can prompt injection abuse AI-driven workflows? What happens if AI can open/close tickets or update playbooks? How to detect anomalies like unexpected endpoints or strange output patterns? Use emerging AI-security frameworks to extend traditional models. Upgrade Monitoring & DLP Traditional DLP is insufficient. You need: LLM-aware egress controls that detect traffic to AI APIs Monitoring of browser extensions (especially those reading content/clipboard) Prompt-level logging for sanctioned LLM tools feeding into your SIEM AI telemetry must become a standard SOC data source.   Train Security Teams as AI Power Users Organizations increasingly need AI/cybersecurity training. SOC training must include: How LLMs work and why they hallucinate Prompt injection, jailbreaks, and poisoning examples Hands-on training with approved AI tools Legal/regulatory impacts of data misuse Goal: confident, responsible AI users not “AI outlaws.” Measure AI Hygiene Track progress with metrics like: Number of unapproved AI endpoints accessed Ratio of sanctioned vs. unsanctioned prompts Percentage of staff completing AI-security training Incidents where AI was used and its impact Treat AI hygiene as seriously as endpoint hygiene or phishing training. A Practical SOC Checklist for Shadow AI Here’s a condensed checklist security leaders can use: Inventory & Discover Run internal surveys on AI use by security staff. Mine network/proxy logs for AI domains and extension traffic.   Govern & Classify Define data categories (red/amber/green) for AI prompts. Document which SOC data sources are allowed in which tools.   Offer Safe Alternatives Deploy at least one sanctioned, secure AI assistant for SOC workflows. Ensure it has strong data isolation, logging, and RBAC.   Policy & Process Publish SOC-specific AI usage guidelines with concrete examples. Integrate AI usage rules into onboarding and periodic training.   Engineering & Monitoring Add LLM-specific threats to SOC threat models. Stand up AI-aware egress filtering and telemetry collection.   Review & Improve Include AI usage review in post-incident analysis. Track metrics on shadow AI reduction and safe AI adoption.   If you systematically work through this list, shadow AI goes from a blind spot to a managed risk.   The Bottom Line: AI Will Enter Your SOC With or Without You   In cybersecurity, generative AI is here to stay. Since it speeds up spotting threats and reacting, big names such as Microsoft, Palo Alto Networks, or IBM are building it right in.   However, the governance gap widens as adoption picks up speed: CISOs worry about uncontrolled data flows and unclear usage. Employees including security analysts quietly use whatever AI tools help them work faster.   In this gap, shadow AI thrives. The reality for security leaders is simple:   If your SOC doesn’t have a generative AI strategy,you don’t have “no AI” you have shadow AI.   The real choice isn’t between using AI or avoiding it.It ’s between: Governed, transparent AI you can justify to leadership and regulatorsor Ungoverned shadow AI that reveals itself only during an incident.   Now is the time to expose shadow AI, set guardrails, and turn a hidden risk into a controlled, strategic advantage.   Citations: Security, V. (n.d.). AI Security: Shadow AI is the New Shadow IT (and It’s Already in Your Enterprise) | Valence Security . https://www.valencesecurity.com/resources/blogs/ai-security-shadow-ai-is-the-new-shadow-it-and-its-already-in-your-enterprise Quilr. (n.d.). QUILR | Human Risk Management for Modern Security . https://www.quilr.ai/blog-details/shadow-ai-a-cybersecurity-nightmare ? Zylo, & Zylo. (2025, September 5). Shadow AI explained: Causes, consequences, and best practices for control. Zylo. https://zylo.com/blog/shadow-ai/ ? What is shadow AI? how it happens and what to do about it. (n.d.). Palo Alto Networks. https://www.paloaltonetworks.com/cyberpedia/what-is-shadow-ai ? Mindgard. (2025, June 15). Research: Shadow AI is a Blind Spot in Enterprise Security, Including Among Security Teams. Mindgard. https://mindgard.ai/resources/shadow-ai-is-a-blind-spot State of Security 2024: The Race to Harness AI | Splunk . (n.d.). Splunk. https://www.splunk.com/en_us/form/state-of-security-2024.html Fitzgerald, A. (2025, June 18). How can generative AI be used in cybersecurity? 15 Real-World examples. Secureframe . https://secureframe.com/blog/generative-ai-cybersecurity 1Password. (n.d.). 1Password Annual Report 2025 Reveals Widening Access-Trust Gap in the AI era | 1Password . https://1password.com/press/2025/oct/annual-report-2025-the-access-trust-gap Fitzgerald, A. (2025, June 18). How can generative AI be used in cybersecurity? 15 Real-World examples. Secureframe. https://secureframe.com/blog/generative-ai-cybersecurity Lasso Security. (2025, October 15). The CISO’s Guide to GENAI Risks: Unpacking the real security pain points. https://www.lasso.security/blog/the-cisos-guide-to-genai-risks-unpacking-the-real-security-pain-points ? Sadoian, L. (2025, June 4). Shadow AI: Managing the security risks of unsanctioned AI tools. UpGuard. https://www.upguard.com/blog/unsanctioned-ai-tools ? Security, L. (2025, November 13). How can generative AI be used in cybersecurity? Legit Security. https://www.legitsecurity.com/aspm-knowledge-base/how-can-generative-ai-be-used-in-cybersecurity ? Low-Code Security Automation & SOAR Platform, Swimlane. (2025, September 4). CISO Guide: AI’s Security Impact | SANS 2025 Report. AI Security Automation. https://swimlane.com/blog/ciso-guide-ai-security-impact-sans-report/ ? Collins, B. (2025, November 1). Shadow IT is threatening businesses from within - and today’s security tools simply can’t keep up . TechRadar. https://www.techradar.com/pro/shadow-it-is-threatening-businesses-from-within-and-todays-security-tools-simply-cant-keep-up   Image Citations: Guaglione, S. (2025, March 21). WTF is ‘shadow AI,’ and why should publishers care? Digiday. https://digiday.com/media/wtf-is-shadow-ai-and-why-should-publishers-care/ Shadow AI and Data Leakage: The Hidden Threat in Everyday Productivity Tools. (n.d.). https://trendsresearch.org/insight/shadow-ai-and-data-leakage-the-hidden-threat-in-everyday-productivity-tools/?srsltid=AfmBOooBXuXqKUC5NNhnKgs3B2rNq5hNjDZR2JFabr-rN_h-UdDfUHeJ

SHILPI MONDAL| DATE: DECEMBER 01,2025 Why Human Risk Deserves an Economic Model For years, cybersecurity has quietly acknowledged a brutal truth: people are involved in most breaches. Verizon’s Data Breach Investigations Report (DBIR) has repeatedly found that the human element errors, social engineering, misuse is implicated in the majority of incidents. In recent editions, phishing and related social engineering (like business email compromise, or BEC) remain among the top initial attack vectors across industries. Other analyses echo the point: in many sectors, phishing or pretexting via email accounts for more than two-thirds of breaches, and median time to click on a malicious email is often under a minute. Academic and industry research now treat human behavior as a quantifiable cyber risk driver, not just a vague “weakest link.” Studies into phishing show how busy people are, their situation at the moment, also what’s going through their mind - these all affect if they’ll click or flag a scam email. Put simply: Phishing is predictable at scale. The losses it causes are material and recurrent. The variables are measurable: who gets targeted, who clicks, who authenticates payments, who has sign-off authority. That makes phishing perfect for an economic treatment: you can model it, assign probabilities, estimate financial impact, and optimize investment. Why Executives Are the Highest-Value Human Risks Leaders have money power, sway, or entry points so hackers aim right at them using custom scams like whale attacks or fake email traps.A hacked CFO might spark huge money troubles, legal trouble,or damage trust so could a breached CEO. A corrupt general counsel? That brings fines, scrutiny, even public backlash. Each role failing spreads ripple effects across the company’s stability. Turning Human Behavior Into Priced Cyber Risk Phishing risk can be modeled like any other financial risk: Expected Loss = Probability × Impact For executives, key scenarios include: BEC (fraudulent payments, vendor scam) Credential Theft (account takeover, lateral movement, ransomware) Sensitive Data Leakage (M&A documents, legal files) Reputational Damage (fake announcements, market manipulation) Because impacts are large and measurable, phishing lends itself to quantitative loss modeling  not guesswork. Measuring Human Susceptibility Executive susceptibility is quantifiable using: Behavioral Metrics Click and data-submission rates in simulations Reporting behavior Time-to-click / time-to-report History of near-misses or previous compromises Control Usage Use of phishing-resistant MFA (FIDO2) Device hygiene Secure communication practices Contextual Risk Travel Public visibility High-pressure business cycles Executives can be categorized into low-, medium-, and high-risk tiers using this data, allowing for focused interventions. Quantifying the Financial Impact of a Phished Executive Impact assessment must include: Direct losses:  fraudulent transfers, recovery efforts, legal expenses Operational impact:  downtime, delayed filings, disrupted projects Regulatory/legal costs:  fines, investigations Strategic + reputational impacts:  lost deals, market reaction, leaked negotiations Typical ranges: Generic mailbox compromise → $10K–$100K Executive compromise → hundreds of thousands to tens of millions These can be modeled using EAL, VaR, and CVaR, translating cyber behavior into financial exposure the board understands. Turning It into a Price: Phishing Exposure per Executive Now you have: P(executive j is successfully phished) per year. Loss distribution if that happens. You can calculate per-executive phishing exposure in monetary terms. A simple formula For each executive j  and each scenario s  (e.g., BEC, credential theft): EALᵢⱼ = Σ [ P(attack_s against j) × P(success_s | attack_s, behavior_j, controls) × Expected Loss_s,j ] Then sum across scenarios: Total Phishing EAL for Executive j = Σ EALᵢⱼ You might find, for example, that: CEO: expected loss from phishing = $900,000 per year CFO: expected loss from phishing = $1.4M per year CHRO: expected loss from phishing = $350,000 per year CIO: expected loss from phishing = $600,000 per year These numbers are illustrative, but they give the board a price tag for each role’s phishing exposure. Building a “Human Risk Premium” You can also express this as a risk premium: Imagine what cyber insurance would charge just for covering phishing-related incidents involving executives. That implicit premium is your phishing risk price for the executive team. This framing is powerful because it: Converts “training fatigue” into capital and insurance costs. Allows you to say, “If we reduce the CFO’s phishing exposure by 40%, we effectively ‘earn back’ $X in expected loss and lower our implied risk premium.” Controls with ROI: Prioritizing What Reduces Loss the Most Once risks are priced, investments can be made based on ROI. High-ROI Controls Technical Advanced email security FIDO2 authentication Strong payment/approval safeguards Data loss prevention (DLP) Human + Process Executive-specific simulations “Never approve over email” rules Executive assistant training Clear crisis playbooks Controls should be prioritized by how much expected loss they eliminate per dollar spent. How to Present This to the Executive Team and Board Numbers only matter if they drive decisions. Framing is critical. Speak the language of finance and risk Instead of “training completion rate,” talk about: Expected Annual Loss from executive phishing Worst-case scenario loss (VaR) over one year Risk reduction achieved by specific initiatives Cost per dollar of risk reduced This aligns with guidance for board-oriented cyber reporting, which stresses a small set of quantified risk metrics such as expected and worst-case loss. Use simple, credible visuals Examples: Heat map of phishing exposure by role X-axis: role criticality; Y-axis: susceptibility. Color: expected loss band. “Before vs after” bar chart Show EAL per role before and after a specific control (e.g., hardware keys rollout). Loss funnel Total phishing attempts → attempts reaching inboxes → clicks → compromises → monetary loss. Mark where controls and behaviors reduce volume or impact. Narrative framing that works “We are not trying to blame individuals; we are pricing a risk that happens to flow through human behavior.” “Think of executive phishing exposure as a line item we can shrink through a portfolio of technical, process, and behavioral investments.” “This lets you compare cyber investments to other risk-reducing initiatives—hedging FX, diversifying suppliers, or holding more inventory.” Implementation Roadmap for a Human Risk Pricing Program Baseline & Inventory Figure out who’s on the list - especially top decision-makers or key players. Collect old info  like phishing drills, close calls, breaches, insurer forms, plus audit results. Track key boss-led workflows like payments, adding vendors, giving approval, or sharing info - swap "and" with commas or dashes where needed. Keep it short. Break up common phrases. Use everyday words. Match original line length closely. Data Collection & Model Building Run realistic, executive-focused phishing simulations. Grab behavior info across half a year or more - maybe stretch into twelve months, depending how things go. Check real-world data - like DBIR or FBI IC3 - to compare how often attacks happen and how bad they are. Set up the model with basic expected-loss sheets - or go for something organized, such as FAIR. Integrate Into Enterprise Risk & Planning Add phishing exposure metrics to the enterprise risk register. Align the data with business continuity, capital planning, and cyber insurance initiatives. Calculate the amount of money or insurance needed to cover losses caused by phishing. Governance, Culture & Continuous Improvement Explain who’s in charge of the model - also mention how often tweaks happen. Promote a non-punitive culture that encourages quick reporting; even after clicking. Continuously refine probabilities and impact estimates as attacker methods, business conditions, and controls evolve. Pitfalls and Ethical Considerations Pricing human risk is powerful—and sensitive. Missteps can damage trust and create perverse incentives. Avoid weaponizing the numbers Don’t turn per-executive EAL into a public scorecard or a shaming tool. Focus on role-level exposure and anonymized data where possible. Use named data only where it directly informs coaching or tailored protections. Guard privacy and fairness Limit who can see detailed behavioral metrics. Clearly communicate: What is being measured. How it will be used. How long data is retained. Watch for bias: Roles or teams that get more simulations might look “worse” if you don’t normalize properly. Executives in high-pressure roles may appear riskier simply due to volume and urgency—something you should address structurally, not just by blaming individuals. Align with recognized best practices Frameworks like NIST SP 800-50 and its updated guidance emphasize that awareness and training programs should be role-based, measurable, and aligned with organizational risk—not just generic e-learning. Your economic model should support, not replace: Strong baseline controls. Continuous training and culture building. Clear accountability at the leadership level. The Strategic Payoff: From “User Error” to Managed Risk When you shift from talking about “people clicking links” to pricing human risk, several things change: Cybersecurity joins the language of finance. You discuss expected loss, VaR, risk premiums, and ROI—not just alerts and patches. Executives see themselves as risk owners, not victims. Their own behavior, approvals, and disciplines become levers to reduce a price tag the board cares about. Investment decisions become clearer. Should you roll out hardware keys to the top 200 staff? Is a dedicated executive protection and email security suite justified? Does the cost of bespoke executive training pay off in risk reduction? Culture improves. You’re not blaming “the human factor”; you’re managing an economically material risk that happens to be expressed through people. Phishing will never disappear. But by treating executive phishing exposure as a priced risk, not a moral failure, you give your organization a concrete way to shrink one of its most persistent and expensive vulnerabilities. Citations: 2024 Data Breach Investigations Report: Vulnerability exploitation boom threatens cybersecurity. (2025, April 9). News Release | Verizon. https://www.verizon.com/about/news/2024-data-breach-investigations-report-vulnerability-exploitation-boom ? Șandor, A., Tonț, G., & Simion, E. (2021). A Mathematical model for risk assessment of social engineering attacks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4180646 2017 Volume 1 Phishing Detection and Loss Computation Hybrid Model A Machine learning Approach. (n.d.). ISACA. https://www.isaca.org/resources/isaca-journal/issues/2017/volume-1/phishing-detection-and-loss-computation-hybrid-model-a-machine-learning-approach ? Bouveret, A. (2019). Estimation of losses due to cyber risk for financial institutions. The Journal of Operational Risk. https://doi.org/10.21314/jop.2019.224 Wei, X., & Dong, Y. (2025). A hybrid approach combining Bayesian networks and logistic regression for enhancing risk assessment. Scientific Reports, 15(1), 26802. https://doi.org/10.1038/s41598-025-10291-9 Mayou, C. (2025, November 14). Board reporting for cybersecurity: What executives need to see (and why). Meriplex. https://meriplex.com/board-reporting-for-cybersecurity-what-executives-need-to-see-and-why/ ? Dezeure, F., Webster, G., Trost, J., Leverett, E., Gonçalves, J. P., Mana, P., McCord, G., & Magri, J. (2022). Reporting Cyber Risk to Boards. https://www.eurocontrol.int/sites/default/files/2022-03/reporting-cyber-risk-to-boards-ce-20220322.pdf Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology, U.S. Department of Commerce. (n.d.). NIST Publishes SP 800-50 Revision 1 | CSRC. https://csrc.nist.gov/News/2024/nist-publishes-sp-800-50-revision-1 ? FBI’s 2024 Internet Crime Complaint Center report released. (2025, April 24). Federal Bureau of Investigation. https://www.fbi.gov/contact-us/field-offices/elpaso/news/fbis-2024-internet-crime-complaint-center-report-released ? Gallo, L., Gentile, D., Ruggiero, S., Botta, A., & Ventre, G. (2023). The human factor in phishing: Collecting and analyzing user behavior when reading emails. Computers & Security , 139 , 103671. https://doi.org/10.1016/j.cose.2023.103671 Proofpoint. (2025, September 15). The Human Factor 2025: Vol. 1 Social Engineering | ProofPoint US . https://www.proofpoint.com/us/resources/threat-reports/human-factor-social-engineering ? (74) WhatsApp Business. (n.d.). https://web.whatsapp.com/ Nwafor, C. N., Nwafor, O., Brahma, S., & Acharyya, M. (2025). A hybrid FAIR and XGBoost framework for cyber-risk intelligence and expected loss prediction. 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SWARNALI GHOSH | DATE: AUGUST 19, 2025 Introduction: When Seeing Isn’t Believing We are entering an era where the adage "seeing is believing" no longer holds weight. The explosion of synthetic media deepfake video, AI generated audio, and convincingly crafted text has blurred the lines between the real and the fabricated. While these technologies offer creative and communicative potential, they also harbour profound cybersecurity threats that can disrupt trust, institutions, and personal lives. The rapid evolution of artificial intelligence (AI) has given rise to synthetic media content created or manipulated using AI technologies including text, images, videos, and audio. This revolutionary capability has wide applications in entertainment, marketing, education, and communication, but it also introduces serious cybersecurity risks and ethical challenges. As synthetic media becomes more prevalent, understanding these risks is essential to navigating the brave new world of digital content. Understanding Synthetic Media and AI-Generated Content Synthetic media leverages AI to produce or alter content in a way that can mimic real people, events, or voices with striking realism. Deepfakes, a subset of synthetic media, employ techniques such as face-swapping in video and voice cloning to create hyper-realistic but fabricated content. These tools have democratized content creation, allowing even individuals with limited technical expertise to produce compelling audio-visual material that can deceive audiences. Deepfakes and Synthetic Media: The Multi-Dimensional Threat Landscape Executive Impersonation & Financial Fraud:   Deepfake-driven scams are on the rise. In one notorious case, fraudsters used AI-generated audio to mimic a CEO’s voice, convincing a finance director to transfer €220,000 to a fraudulent account. In 2024, a “deepfake” attack defrauded a British firm in Hong Kong of £25.4 million by replicating the CFO's image, voice, and signature. Business Email Compromise (BEC) fraud involving deepfakes-also known as “vishing”-poses a growing and highly believable threat. Political Manipulation & Disinformation:   Synthetic media now powers disinformation campaigns aimed at destabilizing political systems. For instance, deepfake videos misrepresenting Ukrainian President Zelenskyy’s surrender surfaced online to erode morale. Election interference and propaganda—enhanced by AI-generated audiovisual content-pose a direct threat to democratic integrity. Social Trust Erosion & The "Liar’s Dividend": As deepfakes grow increasingly realistic, public trust in legitimate media erodes. Experts caution against the so-called ‘liar’s dividend,’ where genuine videos risk being brushed off as fabrications, fuelling scepticism and uncertainty. Studies confirm most people are unable to reliably distinguish deepfakes from genuine content; exposing a profound vulnerability in human perception. AI-Powered Phishing, Prompt Injection, and "AI vs AI" Attacks: Phishing has become dramatically more effective with AI-generated, highly convincing messages. According to Kaspersky, cybercriminals are increasingly using AI-driven phishing schemes, where deepfake technology is employed to trick individuals into revealing sensitive information or authorizing fraudulent transactions. Moreover, attackers exploit large language models (LLMs) through indirect prompt injection—embedding malicious prompts that get executed by AI assistants without user awareness.   Harassment, Exploitation, and Access to Sensitive Communities: AI tools that realistically remove clothing from images (“nudifying”) have sparked serious ethical and legal concerns particularly when used against minors. In the UK, such tools have been used for extortion and harassment, with tragic outcomes including suicides. Deepfake pornography also continues to spread, with celebrities and individuals alike falling prey, amplifying emotional, social, and legal harm. National Security & Corporate Espionage:   Deepfakes are no longer parlor tricks they’re strategic tools in cyber warfare. Fabricated videos portraying U.S. government officials have been circulated to mislead foreign diplomats and create turmoil within global communication networks. Corporations face threats not only from impersonation but also from synthetic applicants, stolen credentials, and insider deception by foreign agents. Emerging AI Tools and Risks: Cheapfakes and Generative Propaganda A wave of “cheapfakes” ; low-effort AI-generated clips using static images and sensational scripts proliferates on platforms like YouTube. They incite outrage while evading detection, monetized via engagement despite being deceptive. Meanwhile, tools like Google’s Veo 3 can fabricate riot or election-side content with jaw-dropping realism, undermining fact-checking protocols. Defensive Strategies: Fighting AI with AI and Systems of Resilience Advanced Detection Technologies:   Fields of research and development are exploding with tools capable of sniffing out synthetic media. AI models detect speech pattern anomalies, metadata inconsistencies, or artifacts in manipulated content. Real-time forensic platforms like Vastav AI offer metadata-based detection, heatmaps, and confidence scoring to law enforcement and enterprises. Scholarly reviews call for adversarial-robust detection systems that resist manipulation attempts.   Organizational Preparedness & Cyber Hygiene:   Companies are advised to embed deepfake risk into their cybersecurity frameworks by integrating awareness, detection, response, and recovery strategies. Recommendations include: Multi-factor and multi-channel verification for high-risk requests (e.g., verbal confirmation via separate channels). Organizations should educate staff on spotting indicators of deepfakes and identifying common warning signs of phishing attempts. Watermarking official media and practicing digital footprint management to limit high-quality public content for attackers.   Legal, Ethical, and Regulatory Approaches:   Policy interventions include the European Union's AI Act, mandating transparent labelling and audit trails for synthetic content. Platforms are urged to enforce stricter moderation, employ detection algorithms, and incorporate visible (but tamper-resistant) watermarks.   Media Literacy and Public Awareness:   Boosting the public’s ability to critically assess media is as important as technical defences. Awareness campaigns, media literacy programs, and visibility into AI’s risks are essential lines of defence against deception. Research into labelling designs demonstrates that simple visual flags can significantly increase user detection of AI-generated content, though their impact on sharing behaviour varies. The Future Landscape The synthetic media market is growing rapidly, projected to expand from USD 4.5 billion in 2023 to USD 16.6 billion by 2033. As AI-driven content creation becomes ubiquitous, balancing the benefits of creative innovation with the imperative to protect privacy, integrity, and security will shape digital communication's future. Organizations must stay vigilant and proactive in combating the evolving threats that synthetic media introduces. Cybersecurity defences must evolve alongside AI advancements, as these technologies become intertwined battlegrounds for trust and truth in the digital age. Conclusion: Vigilance in the Age of Synthetic Reality Synthetic media’s cybersecurity risks span the deeply personal to the geopolitical. These technologies threaten financial systems, democratic institutions, trust, identity, and mental health. Yet our collective resilience—anchored in AI-assisted detection, systemic preparedness, regulatory frameworks, and educated vigilance—can curtail their power. As we navigate this “post-trust” era, the fight against synthetic deception is not just technological—it’s societal.   Citations/References: KPMG International, Home. (2025, July 17). Deepfake threats to companies. KPMG. https://kpmg.com/xx/en/our-insights/risk-and-regulation/deepfake-threats.html Makhija, A. (2024, January 19). Deepfakes and Synthetic Media: Tackling the Cybersecurity Threats. Enterprise Blog. https://www.techjockey.com/enterprise-blog/tackling-the-cyber-security-threats-from-deepfakes-and-synthetic-media Deepfake Attacks: Detection, Prevention & Risks | Paramount. (2025, June 26). Paramount. https://paramountassure.com/blog/deepfake-attacks-cybersecurity/ AI Deepfake Security Concerns | CSA. (2024, June 25). https://cloudsecurityalliance.org/blog/2024/06/25/ai-deepfake-security-concerns BitsofBytes. (2025, April 27). Deepfakes & Cybersecurity: Protecting Your Business from Synthetic Threats. https://business.bitsofbytes.tech/deepfake-cybersecurity-risks/ Intelligence, Z. (2025, February 28). 3 Notable synthetic media attacks. ZeroFox. https://www.zerofox.com/blog/synthetic-media-attacks/ Wikipedia contributors. (2025, August 8). Prompt injection. Wikipedia. https://en.wikipedia.org/wiki/Prompt_injection Wikipedia contributors. (2025, June 30). Synthetic media. Wikipedia. https://en.wikipedia.org/wiki/Synthetic_media Baker, S. J. (2025, August 14). My son, 16, killed himself over a terrifyingly realistic deepfake. . . as sick ‘nudifying’ apps sweep YOUR child. . . The Irish Sun. https://www.thesun.ie/news/15687749/ai-deepfake-schools-app-children/ Loten, A. (2025, August 18). AI drives rise in CEO impersonator scams. WSJ. https://www.wsj.com/articles/ai-drives-rise-in-ceo-impersonator-scams-2bd675c4   Image Citations: Understanding AI cybersecurity risks and how to mitigate them. (n.d.). https://www.harbortg.com/blog/understanding-ai-cybersecurity-risks-and-how-to-mitigate-them What is deepfake: AI endangering your cybersecurity? | Fortinet. (n.d.). Fortinet. https://www.fortinet.com/resources/cyberglossary/deepfake Uddin, M., Irshad, M. S., Kandhro, I. A., Alanazi, F., Ahmed, F., Maaz, M., Hussain, S., & Ullah, S. S. (2025). Generative AI revolution in cybersecurity: a comprehensive review of threat intelligence and operations. Artificial Intelligence Review, 58(8). https://doi.org/10.1007/s10462-025-11219-5 Arad, R. (2024, August 6). 4 Examples of How AI is Being Used to Improve Cybersecurity [Video]. Memcyco. https://www.memcyco.com/how-ai-is-being-used-to-improve-cybersecurity/ Unlocking the Potential of Generative AI in Cybersecurity: A Roadmap to Opportunities and challenges. 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JUKTA MAJUMDAR | DATE March 04, 2025 Introduction Small and Medium Enterprises (SMEs) are increasingly targeted by cybercriminals, yet they often lack the resources and expertise to implement robust cybersecurity measures. AI-powered cybersecurity solutions are emerging as a game-changer, bridging this resource gap and providing SMEs with advanced protection at an affordable cost.   The Cybersecurity Challenge for SMEs SMEs face unique cybersecurity challenges:   Limited Budgets SMEs typically have smaller budgets than larger enterprises, making it difficult to invest in expensive cybersecurity infrastructure and personnel.   Lack of Expertise Many SMEs lack in-house cybersecurity expertise, making it challenging to implement and manage complex security solutions.   Growing Threat Landscape SMEs are increasingly targeted by sophisticated cyberattacks, including phishing, ransomware, and data breaches.   How AI is Bridging the Gap AI is making advanced cybersecurity tools accessible and affordable for SMEs in several ways:   Automation of Security Tasks AI automates routine security tasks, such as threat detection, vulnerability scanning, and incident response, reducing the need for manual intervention and freeing up limited IT resources. This reduces the need for expensive security teams.   Predictive Threat Analysis AI algorithms analyse vast amounts of security data to identify patterns and predict potential threats before they materialise. This proactive approach enables SMEs to prevent attacks before they cause damage.   Simplified Security Management AI-powered cybersecurity platforms provide user-friendly interfaces and intuitive dashboards, simplifying security management for SMEs with limited technical expertise. This reduces the complexity associated with implementing and managing security solutions.   Cost-Effective Solutions AI-powered cybersecurity solutions are often offered as cloud-based services, eliminating the need for expensive hardware and software investments. Subscription-based models also make advanced security more financially accessible.   Adaptive Security AI systems continuously learn and adapt to evolving threats, ensuring that SMEs are protected against the latest cyberattacks without requiring constant manual updates or expensive overhauls.   Making Advanced Tools Accessible and Affordable AI is democratizing cybersecurity by:   Cloud-Based Delivery Cloud-based AI cybersecurity solutions provide SMEs with access to enterprise-grade security tools without the need for significant upfront investments.   Managed Security Services   AI-powered managed security services (MSSPs) offer SMEs access to a team of cybersecurity experts who can monitor and manage their security posture remotely, providing cost-effective protection.   AI-Powered Security Platforms Integrated AI security platforms offer comprehensive protection against a wide range of cyber threats, simplifying security management and reducing costs.   Benefits of AI-Powered Cybersecurity for SMEs   Enhanced Protection AI provides SMEs with advanced protection against sophisticated cyberattacks, reducing the risk of data breaches and financial losses.   Reduced Costs AI-powered solutions automate security tasks and simplify management, reducing the need for expensive personnel and infrastructure.   Improved Efficiency AI automates routine security tasks, freeing up IT resources to focus on other critical business initiatives.   Increased Scalability Cloud-based AI solutions can easily scale to meet the evolving needs of growing SMEs.   Conclusion AI-powered cybersecurity is revolutionising the way SMEs protect themselves against cyber threats. By automating tasks, predicting threats, and simplifying security management, AI is bridging the resource gap and making advanced cybersecurity tools accessible and affordable for SMEs. This allows smaller businesses to focus on growth, knowing they are protected against the ever-evolving cyber threat landscape.   Sources Hinton, M. (2023, August 6). The role of artificial intelligence in SME cybersecurity. NF Team. Retrieved from https://nf-team.org/ai-in-cybersecurity-for-smes/   Raman. (2024, September 6). AI-powered cyberattacks: How SMEs can prepare and defend. The Office Pass. Retrieved from https://www.theofficepass.com/toppings/how-smes-can-prepare-and-defend-ai-powered-cyberattacks.html   World Economic Forum. (2023, July 20). Generative AI for small- to medium-sized businesses: Cybersecurity chaos or empowerment? Retrieved from https://www.weforum.org/stories/2023/07/generative-ai-small-medium-sized-business/   Image Citations wgi. world & wgi. world. (2024, February 6). Can Cybersecurity Keep Up with the AI Arms Race in the Philippines?  World Geostrategic Insights. https://www.wgi.world/can-cybersecurity-keep-up-with-the-ai-arms-race-in-the-philippines/   info@epublisher-world.com . (2021, March 5). The 21st Century’s AI Arms Race Unfolds – Trends (audiotech) . https://trends-magazine.com/the-21st-centurys-ai-arms-race-unfolds/   Weigand, S. (2025, January 9). Cybersecurity in 2025: Agentic AI to change enterprise security and business operations in the year ahead . SC Media. https://www.scworld.com/feature/ai-to-change-enterprise-security-and-business-operations-in-2025

SHILPI MONDAL| DATE: AUGUST 21,2025 Introduction The  electric vehicle (EV) revolution  is transforming global transportation, with over 5 million EVs already on American roads and billions of federal dollars accelerating charging infrastructure deployment. However, this rapid expansion brings unprecedented cybersecurity challenges. EV charging stations represent a unique convergence of energy infrastructure, transportation systems, and networked technologies—creating a complex  cyber-physical ecosystem  vulnerable to malicious attacks. As charging networks expand, they become increasingly attractive targets for cybercriminals and state-sponsored actors seeking to disrupt critical infrastructure. The cybersecurity of these networks has evolved from a technical consideration to a  national security imperative , requiring urgent attention from manufacturers, policymakers, and security professionals.   The Expanding Attack Surface of EV Charging Infrastructure   Network Architecture Vulnerabilities EV charging infrastructure constitutes a sophisticated network of physical charging stations (Electric Vehicle Supply Equipment or EVSE), cloud services, grid connections, and communication protocols. Each component introduces potential vulnerabilities. Charging stations themselves contain  multiple access points  including Ethernet, USB, Wi-Fi maintenance ports, and physical interfaces that can be exploited by attackers. Researchers have demonstrated that a single compromised charger could potentially affect an entire network of connected devices. Communication Protocol Risks Secure communication between electric vehicles and charging infrastructure is facilitated by standardized protocols like the Open Charge Point Protocol (OCPP) and IEEE 2030.5, alongside various proprietary manufacturer systems. These communications support critical functions like authentication, billing, and charging management. Without proper encryption and authentication, these protocols are vulnerable to  eavesdropping, message manipulation, and session hijacking . The integration of vehicle-to-grid (V2G) technology further expands the attack surface by enabling bidirectional energy flow—creating potential pathways for grid disruption through compromised charging infrastructure.   Types of Cyber Threats Targeting EV Charging Networks   Attack Classification EV charging networks face diverse cyber threats categorized by their objectives and methods:   Spoofing: Masquerading as legitimate users, processes, or system elements  Tampering: Modifying or editing legitimate information  Repudiation: Denying actions executed by the system  Information disclosure:  Unauthorized access to protected data  Denial of service:  A denial-of-service (DoS) attack disrupts access for authorized users by overwhelming a system with traffic or requests. Elevation of privilege:  Elevation of privilege occurs when an attacker gains unauthorized higher-level access to a system.   Specific Attack Vectors Research has identified numerous specific attack vectors targeting charging infrastructure:   False Data Injection Attacks (FDIA):  Manipulating charging data to disrupt grid operations Distributed Denial of Service (DDoS):  Overwhelming charging networks to cause widespread service interruptions  Charger Manipulation:  Gaining physical access to install malware or extract user data  Vehicle-to-Grid Exploitation:  Using bidirectional charging capabilities to destabilize power grids  Payment System Compromises:  intercepting or manipulating billing information and transactions    Common Cyber Attacks on EV Charging Infrastructure False Data Injection Potential Impact:  Grid disruption, financial fraud Difficulty Level:  Medium DDoS Attacks Potential Impact:  Service disruption, revenue loss Difficulty Level:  Low   Firmware Manipulation Potential Impact:  Full charger compromise Difficulty Level:  High   Payment System Attacks Potential Impact:  Financial theft, data breach Difficulty Level:  Medium   V2G Exploitation Potential Impact:  Grid destabilization Difficulty Level:  High   Consequences of Cybersecurity Failures   Grid Stability Implications The most significant risk of inadequate charging cybersecurity is  potential disruption to the electrical grid . As noted by researchers at Sandia National Laboratories, "Can the grid be affected by electric vehicle charging equipment? Absolutely" . With high-power charging stations drawing 350-400+ kW (and even exceeding 1 MW for heavy-duty applications), coordinated attacks on charging networks could create  substantial load imbalances  potentially causing blackouts or requiring controlled outages.   Privacy and Financial Impacts EV charging systems collect and process  sensitive user data  including payment information, location history, personal identities, and usage patterns. Compromised charging stations could lead to significant privacy violations and financial fraud. The integration with smart grid systems further expands the potential for  energy theft  through manipulated charging sessions.   Erosion of Public Trust Beyond immediate technical impacts, cybersecurity incidents could  undermine public confidence  in EV technology, potentially slowing adoption rates. As charging infrastructure becomes essential transportation infrastructure, ensuring its reliability and security becomes crucial for the continued transition to electric mobility.   Standards and Frameworks for Cybersecurity   International Standards Several international standards provide frameworks for securing EV charging infrastructure:   ISO 15118: This critical standard defines secure communication protocols between EVs and charging stations, supporting features like Plug & Charge (PnC) authentication using Public Key Infrastructure (PKI) and Transport Layer Security (TLS) encryption. IEC 61851: Focuses on electrical safety and basic control of EV charging, working complementarily with ISO 15118's digital security provisions. ISO 27001: Provides a comprehensive framework covering legal, physical, and technical security aspects relevant to charging infrastructure.   Industry Initiatives The  Open Charge Point Protocol (OCPP)  has emerged as a widely adopted standard for communication between charging stations and central management systems. When implemented with security features including TLS encryption, authentication, and secure API access, OCPP can provide a robust foundation for secure charging operations. Industry groups like the  Open Charge Alliance  continue to develop and promote security standards while certification programs like DEKRA's three-level cybersecurity certification provide verification mechanisms for charging equipment.   Emerging Technologies and Defense Strategies   Artificial Intelligence and Machine Learning Advanced AI techniques show significant promise for detecting and preventing cyber attacks on charging networks. Research published in Scientific Reports demonstrates how  Generative Adversarial Networks (GANs)  integrated with deep learning models can predict cyber attacks with high accuracy. The study found that a  GAN-GRU model  exhibited the highest accuracy with the lowest mean absolute error (0.0281), enabling proactive defense against emerging threats.   Blockchain Applications Blockchain technology offers potential solutions for  secure transactions  and  decentralized energy trading  in EV charging networks. By providing tamper-resistant records of charging transactions and energy transfers, blockchain can enhance transparency and security while supporting peer-to-peer energy trading applications.   Defense-in-Depth Approach A comprehensive cybersecurity strategy employs multiple overlapping protective measures:   Physical security:  Protecting charging hardware from unauthorized access  Network security:  Implementing firewalls, intrusion detection systems, and network segmentation  Authentication and encryption:  Using PKI, TLS, and secure authentication protocols  Monitoring and detection:  Deploying AI-powered anomaly detection systems  Firmware security:  Implementing secure boot processes and code signing    Cybersecurity Technologies and Their Applications in EV Charging   Public Key Infrastructure (PKI) – Used for Plug & Charge authentication, enabling secure and automatic identification of vehicles and chargers.   Transport Layer Security (TLS) –  Provides encryption for data in transit, ensuring that communications between EVs, chargers, and backend systems remain protected.   Artificial Intelligence (AI) and Anomaly Detection –  Helps predict and prevent attacks by analyzing patterns and identifying unusual behavior, offering proactive defense mechanisms.   Blockchain –  Secures transactions and energy trading by maintaining tamper-resistant records, enhancing trust and transparency.   Intrusion Detection Systems (IDS) –  Monitors network activity to detect suspicious behavior, allowing for real-time threat identification.   Institutional and Policy Responses   Government Initiatives Recognizing the critical importance of charging infrastructure security, governments worldwide are implementing regulatory frameworks. The United States has established  minimum cybersecurity standards  for federally funded EV charging infrastructure projects through the National Electric Vehicle Infrastructure (NEVI) program. The  Joint Office of Energy and Transportation  offers resources, data, and tools to inform cybersecurity decisions and has developed sample procurement language to help states meet federal requirements.   Public-Private Partnerships Addressing EV charging cybersecurity requires collaboration across multiple sectors. The Joint Office collaborates with other government agencies, research partners, and industry stakeholders to develop and implement comprehensive security strategies. Research institutions like  Sandia National Laboratories  have conducted comprehensive studies of EV charging cybersecurity challenges and recommended solutions.   Certification and Compliance Programs Third-party certification programs help ensure charging equipment meets security standards. DEKRA offers three levels of cybersecurity certification:   Level 1: Basic cybersecurity requirements Level 2: Advanced security requirements including software assessment Level 3: Penetration testing for comprehensive validation    Future Challenges and Research Directions   Evolving Threat Landscape The cybersecurity landscape continues to evolve with emerging threats including:   AI-powered attacks:  Malicious use of artificial intelligence to develop more sophisticated attacks  Supply chain compromises:  Attacks targeting hardware and software supply chains  Vehicle-to-grid exploitation:  Novel attacks leveraging bidirectional charging capabilities   Research Priorities Future research should address several critical areas:   Standardization:  Developing unified security standards across manufacturers and jurisdictions  Resilience architectures:  Designing systems that can maintain operations during attacks  Quantum resistance:  Preparing for post-quantum cryptography threats  Human factors: Addressing social engineering and human vulnerabilities    Conclusion: Toward a Secure Electric Mobility Future   The transition to electric transportation represents one of the most significant technological shifts of our time. Ensuring the cybersecurity of charging infrastructure is not merely a technical challenge but a  societal imperative  that requires coordinated action across multiple domains. By implementing robust standards like ISO 15118, adopting advanced technologies including AI-powered defense systems, and fostering collaboration between public and private sectors, we can build charging networks that are both convenient and secure.  The continued evolution of EV charging cybersecurity will require  ongoing vigilance ,  adaptability , and  investment  as threats evolve and technology advances. With proper attention to these challenges, we can realize the full potential of electric transportation while ensuring the reliability and security of our critical infrastructure .   Citations: Securing EV charging infrastructure Part 1: Why cybersecurity matters. (n.d.). Energy.gov . https://www.energy.gov/ceser/articles/securing-ev-charging-infrastructure-part-1-why-cybersecurity-matters Hu , X., Jiang, X., Zhang, J., Wang, S., Zhou, M., Zhang, B., Gan, Z., & Yu, B. (2025). Electric vehicle charging network security: A survey. Journal of Systems Architecture, 159, 103337. https://doi.org/10.1016/j.sysarc.2025.103337 Charging Summit, E. (2023, June 8). 5 Cybersecurity challenges facing the EV industry - EV charging. . . EV Charging Summit Blog. https://evchargingsummit.com/blog/cybersecurity-challenges-facing-ev-industry/ Johnson, J., Berg, T., Anderson, B., & Wright, B. (2022). Review of electric vehicle charger cybersecurity vulnerabilities, potential impacts, and defenses. Energies, 15(11), 3931. https://doi.org/10.3390/en15113931 Tanyıldız, H., Şahin, C. B., Dinler, Ö. B., Migdady, H., Saleem, K., Smerat, A., Gandomi, A. H., & Abualigah, L. (2025). Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92895-9 PlaxidityX. (2025, June 4). ISO 15118 and EV Cybersecurity: Securing the charging ecosystem. https://plaxidityx.com/blog/blog-post/iso-15118-ev-cybersecurity-guide/ Ampcontrol. (n.d.). Cybersecurity for EV charging infrastructure - AMPControl. https://www.ampcontrol.io/cybersecurity Hamdare, S., Kaiwartya, O., Aljaidi, M., Jugran, M., Cao, Y., Kumar, S., Mahmud, M., Brown, D., & Lloret, J. (2023). Cybersecurity risk analysis of electric vehicles charging stations. Sensors, 23(15), 6716. https://doi.org/10.3390/s23156716 Sayarshad, H. R. (2025). Securing power grids and charging infrastructure: Cyberattack resilience and vehicle-to-grid integration. Journal of Transport Geography, 126, 104231. https://doi.org/10.1016/j.jtrangeo.2025.104231   Image Citations: Securing EV charging infrastructure Part 1: Why cybersecurity matters. (n.d.). Energy.gov . https://www.energy.gov/ceser/articles/securing-ev-charging-infrastructure-part-1-why-cybersecurity-matters PlaxidityX. (2025, June 4). ISO 15118 and EV Cybersecurity: Securing the charging ecosystem. https://plaxidityx.com/blog/blog-post/iso-15118-ev-cybersecurity-guide/ Charging Summit, E. (2023, June 8). 5 Cybersecurity challenges facing the EV industry - EV charging. . . EV Charging Summit Blog. https://evchargingsummit.com/blog/cybersecurity-challenges-facing-ev-industry/ Tanyıldız, H., Şahin, C. B., Dinler, Ö. B., Migdady, H., Saleem, K., Smerat, A., Gandomi, A. H., & Abualigah, L. (2025). Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92895-9 Farnsworth, E. (2024, June 17). How improving EV charging infrastructure can bolster US cybersecurity measures. Cyber Defense Magazine. https://www.cyberdefensemagazine.com/how-improving-ev-charging-infrastructure-can-bolster-us-cybersecurity-measures/

MINAKSHI DEBNATH | DATE: December 18,2024 Blockchain technology, initially designed to support cryptocurrencies like Bitcoin, has proven to be a transformative innovation with applications far beyond digital currencies. Its decentralized, transparent, and immutable ledger system has gained significant traction in fields such as supply chain management and security. These industries are leveraging blockchain to address long tanding challenges, improve efficiency, and enhance trust. This article explores the potential of blockchain in these domains and provides insights into its practical applications. Blockchain in Supply Chain Management Supply chains are complex networks involving multiple stakeholders, including manufacturers, suppliers, logistics providers, and retailers. Traditional supply chain systems often struggle with inefficiencies, lack of transparency, and susceptibility to fraud. Blockchain technology offers a robust solution by enabling real-time, tamper-proof tracking of goods and transactions. Enhanced Transparency Blockchain’s immutable ledger allows all participants in a supply chain to access a single source of truth. For example, Walmart has employed blockchain to track the origin and journey of food products. This system ensures transparency and helps identify contamination sources during food safety recalls, reducing response times from days to seconds (IBM Blockchain, 2023). Improved Traceability Traceability is crucial in industries like pharmaceuticals, where counterfeit drugs pose severe risks. Blockchain can authenticate the origin and movement of medicines through the supply chain. By utilizing unique digital identifiers and blockchain ledgers, companies like Pfizer are combating counterfeiting and ensuring compliance with regulatory standards. Cost and Efficiency Gains Blockchain eliminates intermediaries by enabling direct transactions between stakeholders. Smart contracts—self-executing contracts with predefined rules—streamline operations and reduce administrative overhead. Maersk and IBM’s TradeLens platform uses blockchain to digitize and simplify global shipping processes, saving billions annually (Maersk, 2023). Blockchain in Security Security is a critical concern across industries, and blockchain’s inherent properties make it a powerful tool for enhancing data protection and system integrity. By decentralizing data storage and ensuring that records cannot be altered retroactively, blockchain mitigates many traditional cybersecurity risks. Data Integrity and Authentication Blockchain’s cryptographic hashing ensures data integrity by creating a unique digital fingerprint for each transaction. This technology is invaluable for securing sensitive information in healthcare, finance, and government sectors. Estonia’s e-Residency program uses blockchain to safeguard citizen data, providing a secure platform for digital identity verification and access to government services (e-Estonia, 2023). Decentralized Identity Management Traditional identity management systems are centralized, making them vulnerable to breaches. Blockchain-based decentralized identity systems empower individuals to control their personal data. Microsoft’s Azure Decentralized Identity initiative is a prominent example, enabling users to share verified credentials without exposing sensitive information. Enhanced IoT Security The Internet of Things (IoT) introduces vulnerabilities by connecting numerous devices to networks. Blockchain can secure IoT ecosystems by decentralizing control and providing a tamper-resistant ledger for device communications. Companies like IBM and Bosch are exploring blockchain-based IoT solutions to prevent cyberattacks and ensure device authenticity (IBM IoT Blockchain, 2023). Challenges and Future Outlook Despite its potential, blockchain adoption faces challenges, including scalability, energy consumption, and regulatory hurdles. However, advancements such as Layer 2 solutions, proof-of-stake mechanisms, and evolving legal frameworks are addressing these concerns. The future of blockchain in supply chain and security looks promising. The technology’s ability to foster transparency, trust, and efficiency aligns with the increasing demand for sustainable and secure solutions. As industries continue to innovate, blockchain’s role is expected to expand, transforming traditional systems and creating new possibilities. Conclusion Blockchain technology is redefining the boundaries of its application, demonstrating value far beyond cryptocurrency. In supply chain management, it enhances transparency, traceability, and efficiency. In security, it fortifies data integrity, decentralizes identity management, and strengthens IoT systems. While challenges remain, ongoing advancements and industry adoption suggest a bright future for blockchain as a cornerstone of modern innovation. Citation/References: IBM Blockchain. (2023). Supply Chain Solutions. Retrieved from https://www.ibm.com/blockchain/solutions/supply-chain A.P. Moller - Maersk and IBM to discontinue TradeLens, a blockchain-enabled global trade platform https://www.maersk.com/news/articles/2022/11/29/maersk-and-ibm-to-discontinue-tradelens Image Citations: The Impact of Blockchain Technology on Actuarial Science https://www.linkedin.com/pulse/impact-blockchain-technology-actuarial-science-mahdi-oliaei-6ahcf/ How to Implement Blockchain in Supply Chain Management? https://www.appventurez.com/blog/blockchain-in-supply-chain Blockchain Technology: Beyond Cryptocurrency https://renierbotha.com/2024/07/18/blockchain-technology-beyond-cryptocurrency/

SHILPI MONDAL| DATE: DECEMBER 19,2024 Green cloud computing integrates advanced cloud technologies with eco-friendly practices to enhance energy efficiency and reduce the environmental impact of data centers. As the demand for cloud services escalates, so does the energy consumption of data centers, making sustainable solutions imperative. Environmental Impact of Data Centers Data centers are essential for modern digital infrastructure, supporting services like cloud computing, artificial intelligence (AI), and data storage. However, their substantial energy consumption and associated carbon emissions have raised environmental concerns. Energy Consumption and Carbon Emissions Global Energy Use :  Data centers account for approximately 3% of global electricity consumption and about 2% of total greenhouse gas emissions, comparable to the entire airline industry. Projected Growth:   The International Data Corporation (IDC) forecasts that data center energy consumption will grow at a 16% compound annual growth rate (CAGR), increasing from 382 terawatt-hours (TWh) in 2022 to 803 TWh in 2027. Scope of Emissions:   Scope 2 emissions, primarily from purchased electricity, represent 31% to 61% of a data center's total carbon footprint. Regional Impacts Ireland:   The rapid expansion of data centers in Ireland, driven by the AI boom, has led to these facilities consuming 21% of the nation's electricity. This surge has raised concerns about potential blackouts and conflicts with Ireland's emissions reduction goals.   Mitigation Strategies To address the environmental impact of data centers, several strategies are being implemented:   Renewable Energy Integration:   Transitioning to renewable energy sources, such as solar and wind power, can significantly reduce carbon emissions associated with data centers.   Energy Efficiency Improvements : Enhancing energy efficiency through advanced equipment and cooling systems helps minimize energy consumption.   Heat Recovery : Utilizing excess heat from data centers for community heating purposes can offset energy consumption and reduce environmental impact. Carbon Offsetting : Investing in carbon offset projects can help mitigate emissions that are challenging to eliminate directly. Strategies for Reducing Carbon Footprint Server Virtualization:   By running multiple virtual servers on a single physical server, organizations can reduce the number of physical machines required, leading to lower energy consumption.   Renewable Energy Adoption:   Transitioning to renewable energy sources, like AWS's investment in wind farms or Google's solar-powered data centers, can significantly reduce carbon emissions. Companies like Amazon Web Services (AWS) are investing in renewable energy projects to achieve net-zero carbon by 2040.   Energy-Efficient Hardware:   Utilizing energy-efficient servers and storage devices reduces power consumption. Innovations in liquid cooling and simplified electrical designs contribute to this efficiency.   Artificial Intelligence (AI) Optimization:  Implementing AI to manage workloads and optimize resource allocation can enhance energy efficiency. AI algorithms can predict peak usage times and adjust resources accordingly, minimizing unnecessary energy use.   Efficient Cooling Systems:   Advanced cooling technologies, such as liquid cooling, can reduce the energy required to maintain optimal temperatures in data centers. AWS's latest data center designs incorporate such innovations to improve efficiency.   Benefits of Green Cloud Computing   Cost Savings:   Reducing energy consumption lowers operational costs for data centers. Utility expenditures can be significantly reduced by adopting energy-efficient habits.   Environmental Sustainability:   Adopting green cloud computing practices contributes to global efforts to combat climate change by reducing greenhouse gas emissions. This aligns goals for carbon neutrality. Regulatory Compliance:   As governments implement stricter environmental regulations, green cloud computing helps organizations comply with new standards, avoiding potential fines and enhancing corporate reputation. Case Studies Microsoft: Despite ambitious sustainability goals, Microsoft's emissions increased by 29% due to substantial investments in data infrastructure. This highlights the challenges tech companies face in balancing growth with environmental commitments.   Space-Based Data Centers: A European initiative has found that space-based data centers could be economically viable and reduce the carbon footprint of data processing infrastructure. These centers would be powered by solar energy, contributing to EU carbon neutrality goals by 2050. Conclusion Green cloud computing presents a viable pathway for reducing the carbon footprint of data centers. By implementing strategies such as server virtualization, adopting renewable energy, utilizing energy-efficient hardware, leveraging AI for optimization, and investing in efficient cooling systems, organizations can achieve both economic and environmental benefits. As the digital landscape continues to expand, embracing sustainable cloud computing practices will be essential for mitigating the environmental impact of data centers. Citation: O’brien, M. (2024, December 19). Ireland embraced data centers that the AI boom needs. Now they’re consuming too much of its energy | AP News. AP News. https://apnews.com/article/ai-data-centers-ireland-6c0d63cbda3df740cd9bf2829ad62058 Aruna. (2024, September 12). Innovations in green cloud computing and sustainable solutions. Telecom Review Asia Pacific. https://telecomreviewasia.com/news/featured-articles/4532-innovations-in-green-cloud-computing-and-sustainable-solutions/ ?   Harris, L. (2024). Green Cloud Computing: Reducing Energy Consumption with AI. ResearchGate. https://www.researchgate.net/publication/384695747_Green_Cloud_Computing_Reducing_Energy_Consumption_with_AI Green Cloud Computing: reducing carbon footprints in 2025. (n.d.). https://www.simpliaxis.com/resources/green-cloud-computing-carbon-footprint ? Energy demand from AI – Energy and AI – Analysis - IEA. (n.d.). IEA. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai ? Carbon emissions of data usage? - Anthesis-Climate Neutral Group. (2021, February 10). Anthesis-Climate Neutral Group. https://www.climateneutralgroup.com/en/news/carbon-emissions-of-data-centers/ ? Datacenter Energy and carbon emission projections and the drive to net zero. (n.d.). IDC: The Premier Global Market Intelligence Company. https://www.idc.com/getdoc.jsp?containerId=US50646723& Image Citation: Bigdata. (2023, October 18). Green Cloud Computing – the sustainable way to use the cloud. Big Data Analytics News. https://bigdataanalyticsnews.com/green-cloud-computing-sustainable-use/ Lavi, H. (n.d.). Measuring greenhouse gas emissions in data centers: the environmental impact of cloud computing | Insights & Sustainability | Climatic. https://www.climatiq.io/blog/measure-greenhouse-gas-emissions-carbon-data-centres-cloud-computing Dsouza, R. (2024, December 10). Green Cloud Computing: the future of sustainable IT. IT Blog | Mobile App Development India | Offshore Web Development - Bacancytechnology.com . https://www.bacancytechnology.com/blog/green-cloud-computing

JUKTA MAJUMDAR | DATE: DECEMBER 18, 2024 Introduction Deepfakes are a form of synthetic media that uses artificial intelligence to create convincing but fabricated images and videos, have emerged as a potent force with the potential to disrupt and deceive. As the technology behind deepfakes continues to evolve, so too does its potential for misuse, raising concerns about the spread of misinformation, the erosion of trust, and the potential for manipulation on a massive scale.   Understanding Deepfakes At their core, deepfakes leverage advanced machine learning algorithms to manipulate existing media content. By analyzing vast datasets of images or videos of a particular person, these algorithms can learn to mimic their likeness with startling accuracy. This allows for the creation of hyperrealistic content where individuals are depicted saying or doing things they never actually did.   The Dangers of Deepfakes The potential for harm posed by deepfakes is significant and multifaceted. In the realm of politics, deepfakes can be used to create fabricated speeches or smear campaigns, potentially swaying public opinion and undermining democratic processes. In the business world, deepfakes could be employed for financial fraud, such as impersonating CEOs to authorize fraudulent transactions. Furthermore, deepfakes can be used to spread misinformation and propaganda, exacerbate social divisions, and even incite violence.   Combating the Deepfake Threat Addressing the challenges posed by deepfakes requires a multi-pronged approach that combines technological innovation, policy measures, and public awareness.   Technological Solutions: Researchers are actively developing sophisticated detection algorithms that can identify inconsistencies and anomalies in deepfake content. These tools can analyze subtle cues like blinking patterns, facial expressions, and inconsistencies in lighting and shadows to flag potentially fabricated media. Policy and Regulation: Governments and regulatory bodies are exploring legal frameworks to address the creation and dissemination of harmful deepfakes. This includes measures such as increased transparency requirements for AI-generated content, stricter regulations on the use of facial recognition technology, and potential legal penalties for the malicious use of deepfakes. Public Awareness and Education: Fostering media literacy and critical thinking skills among the general public is crucial in combating the impact of deepfakes. Educating individuals about the potential for manipulated content and providing them with tools to critically evaluate information can help mitigate the spread of misinformation and reduce the vulnerability to deepfake-related scams. Conclusion Deepfakes present a complex challenge with far-reaching implications. While the technology behind deepfakes continues to evolve, so too must our efforts to mitigate their risks. By combining technological innovation, robust policy frameworks, and widespread public awareness, we can navigate the challenges of the deepfake era and safeguard the integrity of information in the digital age.   Sources Sharma, V. K., Garg, R., & Caudron, Q. (2024). A systematic literature review on deepfake detection techniques. Multimedia Tools and Applications . https://doi.org/10.1007/s11042-024-19906-1 Gambín, Á. F., Yazidi, A., Vasilakos, A., Haugerud, H., & Djenouri, Y. (2024). Deepfakes: current and future trends. Artificial Intelligence Review , 57 (3). https://doi.org/10.1007/s10462-023-10679-x   Image Citations Team , B. R. a. I. (2024, August 29). Deepfakes and Digital Deception: Exploring their use and abuse in a generative AI world. BlackBerry . https://blogs.blackberry.com/en/2024/08/deepfakes-and-digital-deception Admin. (2024, May 22). Examining deepfakes and the growing threat of synthetic media . Springbrook. https://springbrooksoftware.com/examining-deepfakes-and-the-growing-threat-of-synthetic-media/ What is deepfake: AI endangering your cybersecurity? | Fortinet . (n.d.). Fortinet. https://www.fortinet.com/resources/cyberglossary/deepfake

SHIKSHA ROY | DATE: MARCH 19, 2025 The advent of 5G technology has revolutionized the way we connect, communicate, and consume data. With its ultra-fast speeds, low latency, and massive connectivity, 5G is set to transform industries, from healthcare to autonomous vehicles. However, as with any technological advancement, 5G also brings with it a host of cybersecurity challenges. The increased speed and connectivity of 5G networks create new vulnerabilities that cybercriminals can exploit. This article explores the emerging threats to 5G networks and discusses strategies to protect these ultra-fast networks from cyberattacks. Understanding the 5G Landscape What is 5G? 5G, or fifth-generation wireless technology, is the latest iteration of cellular networks, designed to provide faster data speeds, lower latency, and greater capacity than its predecessors. It operates on a broader range of frequencies, including millimeter waves, which allow for higher data transfer rates. 5G is not just an upgrade from 4G; it is a transformative technology that enables the Internet of Things (IoT), smart cities, and other advanced applications. The Importance of 5G Cybersecurity As 5G networks become more widespread, they will underpin critical infrastructure, including power grids, transportation systems, and healthcare services. This makes 5G networks a prime target for cyberattacks. A breach in 5G security could have catastrophic consequences, from disrupting essential services to compromising sensitive data. Therefore, securing 5G networks is not just a technical challenge but a societal imperative. Emerging Threats to 5G Networks Increased Attack Surface One of the most significant challenges of 5G cybersecurity is the expanded attack surface. 5G networks connect a vast number of devices, from smartphones to IoT sensors. Each connected device represents a potential entry point for cybercriminals. The sheer volume of devices and the complexity of 5G networks make it difficult to monitor and secure every potential vulnerability. Network Slicing Vulnerabilities Network slicing is a key feature of 5G that allows multiple virtual networks to operate on a single physical infrastructure. While this enables customized services for different applications, it also introduces new security risks. If not properly secured, a breach in one network slice could potentially compromise the entire 5G infrastructure. Supply Chain Risks The global nature of 5G technology means that components and software are often sourced from multiple vendors across different countries. This complex supply chain introduces risks, as vulnerabilities in any component could be exploited by malicious actors. Additionally, the involvement of multiple stakeholders makes it challenging to establish uniform security standards. AI-Powered Attacks As 5G networks leverage artificial intelligence (AI) for network management and optimization, cybercriminals are also using AI to launch more sophisticated attacks. AI-powered malware can adapt to its environment, evade detection, and exploit vulnerabilities at an unprecedented scale. This arms race between cybersecurity professionals and cybercriminals is a growing concern in the 5G era. Privacy Concerns 5G networks generate and process vast amounts of data, raising significant privacy concerns. The increased connectivity and data collection capabilities of 5G make it easier for malicious actors to track users, steal personal information, and conduct surveillance. Ensuring data privacy in a 5G world is a critical challenge that must be addressed. Strategies for Protecting 5G Networks Implementing Zero Trust Architecture Zero Trust Architecture (ZTA) is a security model that assumes no user or device, whether inside or outside the network, can be trusted by default. In a 5G context, ZTA requires continuous verification of user identities, device integrity, and network activity. By implementing ZTA, organizations can reduce the risk of unauthorized access and limit the potential damage from cyberattacks. Enhancing Encryption and Authentication Encryption is a fundamental tool for protecting data transmitted over 5G networks. Strong encryption protocols ensure that even if data is intercepted, it cannot be read or altered by unauthorized parties. Additionally, robust authentication mechanisms, such as multi-factor authentication (MFA), can prevent unauthorized access to 5G networks and devices. Leveraging AI for Cybersecurity While AI-powered attacks are a growing threat, AI can also be a powerful tool for defending 5G networks. AI-driven cybersecurity solutions can analyze vast amounts of data in real-time, detect anomalies, and respond to threats more quickly than traditional methods. By leveraging AI, organizations can stay ahead of cybercriminals and protect their 5G networks more effectively. Securing Network Slicing To mitigate the risks associated with network slicing, it is essential to implement strict isolation between different slices. This can be achieved through advanced firewalls, intrusion detection systems, and secure access controls. Regular security audits and penetration testing can also help identify and address vulnerabilities in network slices. Strengthening Supply Chain Security Given the complexity of the 5G supply chain, it is crucial to establish rigorous security standards for all components and vendors. This includes conducting thorough security assessments, requiring transparency in software and hardware development, and implementing strict access controls. Collaboration between governments, industry stakeholders, and cybersecurity experts is essential to create a secure 5G ecosystem. Ensuring Data Privacy Protecting user privacy in a 5G world requires a combination of technical and regulatory measures. Data minimization, anonymization, and encryption can help safeguard personal information. Additionally, governments and regulatory bodies must establish clear guidelines for data collection, storage, and processing to ensure that user privacy is respected. The Role of Collaboration in 5G Cybersecurity Public-Private Partnerships Securing 5G networks is a shared responsibility that requires collaboration between governments, private companies, and cybersecurity experts. Public-private partnerships can facilitate the sharing of threat intelligence, best practices, and resources to enhance 5G security. Governments can also play a role in setting regulatory standards and providing funding for cybersecurity initiatives. International Cooperation Given the global nature of 5G technology, international cooperation is essential to address cybersecurity challenges. Countries must work together to establish common security standards, share threat intelligence, and coordinate responses to cyberattacks. Organizations such as the International Telecommunication Union (ITU) and the Global Cybersecurity Alliance (GCA) can play a key role in fostering international collaboration. Conclusion 5G technology holds immense promise for transforming industries and improving our daily lives. However, the increased speed, connectivity, and complexity of 5G networks also introduce new cybersecurity challenges. From expanded attack surfaces to AI-powered threats, the risks are significant and multifaceted. Protecting 5G networks requires a comprehensive approach that includes implementing Zero Trust Architecture, enhancing encryption, securing network slicing, and strengthening supply chain security. Collaboration between public and private sectors, as well as international cooperation, is also crucial to creating a secure 5G ecosystem. As we continue to embrace the benefits of 5G, it is imperative that we remain vigilant and proactive in addressing the cybersecurity threats that come with it. By doing so, we can ensure that 5G networks remain a safe and reliable foundation for the future of connectivity. Citations 5G Security and Resilience | Cybersecurity and Infrastructure Security Agency CISA. (n.d.). https://www.cisa.gov/topics/risk-management/5g-security-and-resilience Bartock, M., Cichonski, J., Souppaya, M., Scarfone, K., Grayeli, P., & Sharma, S. (2025, March 18). 5G Cybersecurity. https://csrc.nist.gov/pubs/sp/1800/33/ipd 5G Cybersecurity: Initial Public Draft of SP 1800-33A Cybersecurity Practice Guide. (2025, March 18). NIST. https://www.nist.gov/news-events/news/2025/03/5g-cybersecurity-initial-public-draft-sp-1800-33a-cybersecurity-practice Image Citations The impact of 5G on cyber security in Africa: opportunities and risks | LinkedIn. (2024, October 22). https://www.linkedin.com/pulse/impact-5g-cyber-security-africa-opportunities-risks-mehdi-mahir-prdke/ Zorko, L. (2023, April 24). What is 5G Network Slicing? Tridens. https://tridenstechnology.com/what-is-5g-network-slicing/

SHIKSHA ROY | DATE: MARCH 05, 2025 Decentralized Finance (DeFi) has emerged as a revolutionary force in the financial sector, offering users the ability to access financial services without intermediaries. By leveraging blockchain technology, DeFi platforms enable peer-to-peer transactions, lending, borrowing, and trading. However, the rapid growth of DeFi has also exposed significant cybersecurity risks. Smart contract exploits, protocol vulnerabilities, and malicious attacks have resulted in substantial financial losses. This article explores the challenges in the DeFi space and discusses emerging practices to mitigate these risks. Understanding the DeFi Ecosystem and Its Vulnerabilities What is DeFi? DeFi refers to a suite of financial applications built on blockchain networks, primarily Ethereum. These applications operate without centralized control, relying instead on smart contracts—self-executing code that automates transactions and agreements. While this decentralization offers transparency and inclusivity, it also introduces unique security challenges. Key Vulnerabilities in DeFi The DeFi ecosystem is particularly susceptible to cybersecurity risks due to its reliance on smart contracts and the absence of centralized oversight. Some of the most common vulnerabilities include: Smart Contract Bugs:  Errors in code can be exploited by attackers. Oracle Manipulation:  DeFi platforms often rely on external data sources (oracles) to execute transactions. If these oracles are compromised, the entire system can be manipulated. Flash Loan Attacks:  Attackers borrow large sums of cryptocurrency without collateral, manipulate market prices, and exploit vulnerabilities in DeFi protocols. Rug Pulls:  Malicious developers create fraudulent projects, attract investments, and then disappear with the funds. Smart Contract Exploits: A Major Threat to DeFi How Smart Contracts Work Smart contracts are the backbone of DeFi platforms. They are programmed to execute specific actions when predefined conditions are met. However, if the code contains flaws, attackers can exploit these weaknesses to drain funds or disrupt operations. Notable Exploits Several high-profile exploits have highlighted the risks associated with smart contracts: The DAO Hack (2016):  A vulnerability in The DAO's smart contract led to the theft of $50 million worth of Ethereum.   Poly Network Attack (2021):  Hackers exploited a vulnerability in Poly Network's cross-chain protocol, stealing over $600 million.   Wormhole Exploit (2022):  A flaw in the Wormhole bridge allowed attackers to steal $320 million in cryptocurrency. Why Smart Contracts Are Vulnerable Complexity:  Writing secure smart contracts requires expertise, and even minor errors can have catastrophic consequences. Immutability:  Once deployed, smart contracts cannot be easily modified, making it difficult to patch vulnerabilities. Lack of Standardization:  The absence of universally accepted coding standards increases the risk of errors. Risk Mitigation Practices for DeFi Platforms Bug Bounty Programs Many DeFi projects now offer bug bounty programs, incentivizing ethical hackers to identify and report vulnerabilities. This proactive approach helps uncover potential exploits before they can be exploited maliciously. Code Audits and Formal Verification One of the most effective ways to mitigate smart contract risks is through rigorous code audits. Independent security firms can review the code for vulnerabilities and recommend fixes. Additionally, formal verification—a mathematical approach to proving the correctness of code—can help ensure that smart contracts behave as intended. Insurance Protocols DeFi insurance platforms, such as Nexus Mutual and Cover Protocol, allow users to protect their investments against hacks and exploits. These protocols provide a safety net, encouraging greater participation in the DeFi ecosystem. Decentralized Oracles To address oracle manipulation, DeFi platforms can use decentralized oracles that aggregate data from multiple sources. This reduces the risk of a single point of failure and makes it harder for attackers to manipulate prices. Multi-Signature Wallets Using multi-signature wallets for fund management adds an extra layer of security. Transactions require approval from multiple parties, reducing the risk of unauthorized access. Education and Awareness Educating users about the risks associated with DeFi and promoting best practices, such as verifying smart contract addresses and avoiding suspicious projects, can help reduce the likelihood of falling victim to scams. Emerging Cybersecurity Challenges in DeFi Rapid Innovation vs. Security The DeFi space is characterized by rapid innovation, with new projects and protocols launching frequently. However, this fast-paced environment often prioritizes speed over security, leaving platforms vulnerable to attacks. Cross-Chain Risks As DeFi expands across multiple blockchains, interoperability becomes a challenge. Cross-chain bridges, which facilitate asset transfers between networks, are particularly vulnerable to exploits. Regulatory Uncertainty The lack of clear regulatory frameworks for DeFi creates an environment where malicious actors can operate with relative impunity. This uncertainty also hinders the development of standardized security practices. The Future of DeFi Security Collaboration and Standardization The DeFi community must collaborate to establish industry-wide security standards. Organizations like the DeFi Security Alliance are working towards this goal, promoting best practices and sharing knowledge. Advanced Technologies Emerging technologies, such as zero-knowledge proofs and AI-driven security tools, have the potential to enhance DeFi security. These innovations can help detect vulnerabilities and prevent exploits in real-time. Regulatory Frameworks As governments and regulatory bodies develop clearer guidelines for DeFi, the industry will benefit from increased accountability and transparency. However, it is crucial to strike a balance between regulation and innovation to avoid stifling growth. Conclusion The DeFi revolution has unlocked unprecedented opportunities for financial inclusion and innovation. However, the cybersecurity risks associated with decentralized finance cannot be ignored. Smart contract exploits, oracle manipulation, and other vulnerabilities pose significant threats to the ecosystem. By adopting robust risk mitigation practices—such as code audits, decentralized oracles, and insurance protocols—the DeFi community can build a more secure and resilient future. As the industry continues to evolve, collaboration, education, and technological advancements will play a critical role in safeguarding DeFi platforms from exploits. Citations OWASP Smart Contract Top 10 | OWASP Foundation. (n.d.). https://owasp.org/www-project-smart-contract-top-10/ Decentralized finance: 4 challenges to consider | MIT Sloan. (2022, July 11). MIT Sloan. https://mitsloan.mit.edu/ideas-made-to-matter/decentralized-finance-4-challenges-to-consider Baran, G. (2025, January 21). OWASP Top 10 2025 - Most Critical Weaknesses Exploited/Discovered. Cyber Security News. https://cybersecuritynews.com/owasp-top-10-2025-smart-contract/ Dinnero, L. (2024, December 19). Top risk management practices in the DEFI space. Smart Liquidity Research. https://smartliquidity.info/2024/12/19/top-risk-management-practices-in-the-defi-space/ Hamory, W. (2025, February 25). Protecting Decentralized Exchanges: A Comprehensive Guide to DEFI Risk Management. Founder Shield. https://foundershield.com/blog/guide-to-defi-risk-management/ Navigating DEFI Risks: Managing and mitigating potential pitfalls. (2024, July 23). https://coin-report.net/navigating-defi-risks-managing-and-mitigating-potential-pitfalls/ Image Citations Credgenics. (2024, March 6). Is Decentralized Finance (DeFi) the future of lending?- Credgenics. Blog. https://blog.credgenics.com/decentralized-finance-defi/ https://www.cnbctv18.com . (2022, April 20). What are flash loan attacks — the phenomenon behind the latest $182 million hack. CNBCTV18. https://www.cnbctv18.com/cryptocurrency/what-are-flash-loan-attacks--the-phenomenon-behind-the-latest-182-million-hack-13212002.htm Williams, L. C. (2023, July 10). DOD expands bug bounty program to public networks, systems. Nextgov.com . https://www.nextgov.com/cybersecurity/2021/05/dod-expands-bug-bounty-program-to-public-networks-systems/258868/ Unido. (2022, January 6). What is Multisig? What is Key Management in Crypto? Medium. https://medium.com/unidocore/what-is-multisig-what-is-key-management-in-crypto-6d08b6ffbeae Yuki. (2024, October 12). Що таке "rug pull" у криптовалюті та 6 способів його виявити • CryptoAcademy. CryptoAcademy. https://cryptoacademy.com.ua/shho-take-rug-pull-u-kryptovalyuti-ta-6-sposobiv-jogo-vyyavyty/

ARPITA (BISWAS) MAJUMDER | DATE: JANUARY 15, 2025 Operational Technology (OT) networks are the backbone of industrial operations, managing everything from manufacturing processes to critical infrastructure. However, many of these networks rely on legacy devices—such as programmable logic controllers (PLCs), remote terminal units (RTUs), and older SCADA systems—that were not designed with modern cybersecurity considerations. This lack of inherent security makes them prime targets for cyberattacks, posing significant risks to operational continuity and safety.   The Legacy OT Security Problem   Legacy OT devices often lack basic security features and are no longer supported by manufacturer security patches. This absence of updates leaves known vulnerabilities unaddressed, making it easier for attackers to exploit these weaknesses to disrupt operations, steal data, or even cause physical damage. The increasing convergence of IT and OT networks exacerbates this risk, providing attackers with more pathways to access these vulnerable systems.   Why Traditional Firewall Segmentation Falls Short   Traditional network segmentation methods, such as creating a demilitarized zone (DMZ) between IT and OT networks, offer some level of protection. However, this approach is often not granular enough to secure legacy OT devices effectively. Once an attacker breaches the OT network, they can move laterally within it, potentially compromising multiple vulnerable devices. Given that IT leakage is responsible for a significant percentage of attacks on OT networks, protecting unpatchable devices is a high priority for OT security administrators.   Micro segmentation: A Granular Approach   Micro segmentation enhances network security by isolating individual devices or groups of devices, limiting communication between them to only what is necessary. This "zero trust" methodology operates on the principle that no device is automatically deemed trustworthy, necessitating explicit permissions for all communication. Implementing micro segmentation in OT networks has traditionally been challenging due to the complexity and potential downtime involved. However, modern solutions are emerging that simplify this process, making it more feasible to deploy without significant operational disruptions.   Benefits of Micro segmentation for Legacy OT Devices   Containment:  If a legacy device is compromised, micro segmentation prevents the attacker from moving laterally to other critical systems, containing the breach and minimizing its impact.   Reduced Attack Surface:  By limiting communication pathways, micro segmentation significantly reduces the attack surface, making it harder for attackers to find and exploit vulnerabilities in legacy devices.   Simplified Compliance:  Micro segmentation can help organizations meet regulatory compliance requirements, such as those outlined in IEC 62443, by demonstrating a strong commitment to security best practices and risk management.   Virtual Patching:  In cases where devices cannot be patched, micro segmentation can act as a virtual patch by creating rules that block known exploits, thereby protecting vulnerable devices from potential attacks.   Implementing Micro segmentation in Line with IEC 62443 IEC 62443 is a collection of cybersecurity standards designed specifically for operational technology (OT) and industrial automation and control systems (IACS). These standards provide detailed requirements and methods to address the unique security challenges found in industrial environments. Implementing micro segmentation in accordance with IEC 62443 involves creating security zones and conduits, defining security levels, and establishing robust access control measures. This structured approach ensures that legacy OT devices are adequately protected within the network architecture.   Challenges and Considerations   While micro segmentation offers significant security benefits, implementing it in OT environments presents challenges:   Complexity:  OT environments are naturally intricate, consisting of numerous interconnected systems and devices. Implementing network segmentation can be difficult as it demands a comprehensive understanding of the network's intricacies and interdependencies.     Legacy Systems:  Many OT environments consist of legacy devices and equipment that may not readily accommodate modern network segmentation approaches. Compatibility issues can hinder segmentation efforts.   Operational Disruption:  Implementing micro segmentation can require changes to network configurations, which may lead to operational downtime if not managed carefully.   Conclusion   Securing legacy OT devices is a pressing challenge for organizations managing critical infrastructure and industrial operations. Micro segmentation provides a powerful solution by creating granular security boundaries that limit the impact of breaches and reduce the overall attack surface. By implementing micro segmentation in line with IEC 62443 standards, organizations can significantly improve their OT security posture and protect their critical assets.   Citations/References Brash, R. (2024, March 14). Using IEC 62443 to secure OT Systems: The Ultimate Guide . Verve Industrial. https://verveindustrial.com/resources/blog/the-ultimate-guide-to-protecting-ot-systems-with-iec-62443/ Sectrio. (2024, May 31). OT Micro-Segmentation: A successful path to ICS security. Sectrio . https://sectrio.com/blog/ot-micro-segmentation-complete-guide/ OT Network Segmentation and Microsegmentation Guide | Fortinet . (n.d.). Fortinet. https://www.fortinet.com/resources/cyberglossary/ot-network-segmentation-and-microsegmentation Toll, W. (2025, January 7). IEC 62443 in 2025: Network Segmentation requirements and Changes. Elisity . https://www.elisity.com/blog/iec-62443-in-2025-network-segmentation-requirements-and-changes Hewitt, N. (2023, August 17). Why Device Microsegmentation is Important for OT and IT . TrueFort. https://truefort.com/device-microsegmentation/   How Microsegmentation Enhances OT Security: Insights for Network Security Architects . (n.d.). https://www.byos.io/blog/how-microsegmentation-enhances-ot-security-insights-for-network-security-architects Greitser, R. (2024, July 17). The benefits of microsegmentation for compliance. Akamai . https://www.akamai.com/blog/security/the-benefits-of-microsegmentation-for-compliance Securing the Unsecurable: Segmentation for Legacy OT Devices with IEC 62443 | OT Cybersecurity . (n.d.). https://www.blastwave.com/blog/securing-the-unsecurable-segmentation-for-legacy-ot-devices-with-iec-62443 Elisity. (n.d.). White Paper | Enhancing OT Network Security with IEC 62443: Microsegmentation & Device Visibility . https://www.elisity.com/resources/wp/iec-62443-segmentation-white-paper Elisity. (n.d.). Solution Guide | 2024 IEC 62443 OT Engineer Segmentation Guide . https://www.elisity.com/resources/wp/iec-62443-ot-engineer-guide   Image Citations Mavis. (2024, May 24). How to construct the cornerstone of OT Cybersecurity using ISA/IEC 62443 | TXONe Networks . TXOne Networks. https://www.txone.com/blog/how-to-construct-the-cornerstone-of-ot-cybersecurity-using-isa-iec-62443/ (28) Addressing Cybersecurity Challenges in Legacy Systems with IEC 62443 | LinkedIn . (2024, May 28). https://www.linkedin.com/pulse/addressing-cybersecurity-challenges-legacy-systems-iec-sourabh-suman-ucsme/ Team , C. (2024, August 1). How to accelerate OT Industrial Network Segmentation . Claroty. https://claroty.com/blog/how-to-accelerate-segmentation-alongside-the-xiot Network Segmentation | OT Microsegmentation . (n.d.). https://www.blastwave.com/network-segmentation   About the Author Arpita (Biswas) Majumder is a key member of the CEO's Office at QBA USA, the parent company of AmeriSOURCE, where she also contributes to the digital marketing team. With a master’s degree in environmental science, she brings valuable insights into a wide range of cutting-edge technological areas and enjoys writing blog posts and whitepapers. Recognized for her tireless commitment, Arpita consistently delivers exceptional support to the CEO and to team members.