"Shadow AI” in Security Teams: The Hidden Risk of Unapproved LLM Tools in the SOC
- Shilpi Mondal
- 3 days ago
- 9 min read
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:
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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/
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