Do you know how much AI is really being used in your organization? Probably less than you think… or rather, you see less than what’s actually happening.
Today, AI is no longer limited to known chatbots—it’s embedded in apps you use daily (Salesforce, Microsoft 365, Google Workspace, internal tools). On top of that, many people connect personal AI accounts on corporate devices, creating a massive blind spot for security teams, with serious risks like data leaks, regulatory violations, and IP theft.
At TecnetOne, we’ll explain why this happens, the risks involved, and how to bring order without killing innovation.
The Big Blind Spot: Embedded AI
Security perimeters used to be clear: corporate email, internal networks, and approved apps. Now, work happens across distributed SaaS platforms, and AI is just a click away inside each one. This means AI workflows bypass traditional DLPs and proxies, which can’t see the context.
Real-world examples we see often:
- Healthcare: Clinicians summarize medical notes using embedded AI in EHRs or paste them into external assistants. If that model isn’t covered by HIPAA or similar, there’s PHI exposure—even if everything happens “inside” the approved app.
- Finance: Teams working on IPOs use personal chatbots with confidential documents. CISOs don’t see it; the legal risk is huge.
- Insurance/Retail: Customer segmentation using AI inside a CRM. Without controls, the model learns from demographic data and may suggest pricing or campaigns that violate anti-discrimination laws.
The trap is thinking “approved app” means “approved workflow”—it doesn’t.
Why Security Can’t See It
- AI hides behind product features (buttons like “summarize,” “rewrite,” “predict”).
- Personal accounts on corporate devices (or BYOD) create invisible tunnels.
- Traditional DLP focuses on files, not prompts or AI-invoked data types.
- Static allowlists of apps become outdated in weeks: new plugins, features, and vendors appear constantly.
Read more: AI Agents: Only as Smart as the Database Behind Them
Flow-Based Governance, Not Just App-Based
The key is to shift focus from generic blocking to visibility with context. At TecnetOne, we recommend a three-layer model:
- Edge Detection (Endpoint-First): Instrument laptops and browsers to recognize AI interactions in real time (prompts, function calls, sensitive data being pasted), without exfiltrating traffic. This avoids new attack surfaces and detects patterns, not just app names.
- Flow Risk Intelligence
Classify:
- What kind of data goes into the prompt (PHI, financial, PII, secrets)
- What AI function is invoked (summarization, classification, generation)
- Where it runs (contracted model or not)
- Whether it complies with policies/regulations (HIPAA, GDPR, SOX, anti-bias laws)
- Adaptive Controls
- Allow low-risk or contracted AI use
- Redirect “gray zone” use to secure environments (in-tenant, with logging and retention)
- Block surgically when red lines are crossed (e.g., PHI sent to non-covered model)
All with telemetry and auditability.
This approach doesn’t kill innovation—it guides it to safe channels and cuts only what’s dangerous.
How to Implement It Practically
Fast Deployment on Endpoints
Use your MDM to deploy a lightweight browser/endpoint sensor that can:
- Detect AI prompts and calls without inspecting cloud content
- Map approved SaaS apps and their AI features
- Measure data types and context (who, where, why)
Use-Case Based Policies
Define zones:
- Green: AI in corporate environments with contracts/logging (e.g., Copilot/M365 with limits)
- Amber: AI in approved SaaS if data and functions are within policy
- Red: Personal accounts, regulated data uploads to non-covered models, or biased automated decisions
Integration with Your Stack
- SIEM/SOAR: Trigger alerts and auto-actions (revoke tokens, notify user, open tickets)
- DLP/IRM: Add rules for prompts and AI outputs (not just file movements)
- Approved AI Catalog: With purpose, allowed data, and legal/technical guarantees
Just-in-Time Coaching
When someone triggers a risky flow, provide in-context guidance (“Don’t paste PHI here, use this secure assistant instead”). Far more effective than annual training.
Metrics That Matter
- 70–80% reduction in data exposure incidents within 60–90 days
- % of gray flows migrated to secure channels
- Mean time to detect & contain AI risks
- Adoption of approved AI tools without productivity loss (surveys + telemetry)
- Audit-ready compliance evidence
In healthcare and finance organizations we’ve supported, we consistently see a 70–80% drop in unauthorized AI use, with equal or better productivity once secure channels are adopted.
Other articles: Pentesting with AI: The New Generation of Penetration Testing
Common Mistakes (And How to Avoid Them)
- Total AI bans → creates shadow IT. Allow with control instead.
- Trusting the “approved app” → the flow is what matters.
- Static lists → replace with pattern-based, dynamic detection.
- DLPs without AI context → add prompt/feature signals.
- Policy without legal/ethical backing → include Compliance, Legal, and HR early on.
How TecnetOne Helps
- 360° discovery of real AI use (endpoints + SaaS) in under 30 days
- Risk maps by flow (data, functions, applicable regulations)
- Adaptive policies and lightweight edge controls
- Technical implementation across your SIEM/SOAR/MDM/DLP with playbooks
- Responsible AI adoption programs (training, prompt templates, approved AI catalog)
- Ongoing support and quarterly metrics for audits
Final Thoughts
Most AI use in your company is invisible—because it lives inside familiar apps and is often mixed with personal accounts.
The solution isn’t shutting it all down, but seeing it with context, governing by flow, and guiding your teams to safe channels.
With the right strategy, you’ll drastically reduce incidents and stay compliant—without losing the competitive edge that AI offers.
At TecnetOne, we’ll support you end-to-end: visibility, control, and responsible adoption.
Ready to get started?
