Key idea
AI replays context
Models replay documents, deals, HR data, M&A details, and executive mail threads to whoever asks the right question. Prompt injection is not a breach—it is misuse of your own insider.
Research Note · AI & Automation
AI Is the New Insider
Managing model-driven access & leakage
AI assistants integrated with Microsoft 365, Google Workspace, and SaaS act like power users: they read files, summarize threads, explore shared drives, and transform sensitive documents. They do not “break in”—they reuse access that your business already granted them. That makes AI the most dangerous insider your organization has ever invited.
Model memory vs governance
Prompt-driven access
SaaS identity exposure
The core risk isn’t “AI gone rogue.” It’s humans leaking sensitive data to the model—and the model faithfully leaking it to others later.
Executive summary
AI is not software. It’s a colleague who never forgets.
AI copilots behave like internal employees with perfect recall, no political context, and no instinct for confidentiality. They are insiders with distributed memory. If your policy assumes “only the person who saw the secret can leak the secret,” you are already behind.
Risk
Privacy without secrecy
Access controls don’t prevent leakage if users voluntarily paste sensitive content or AI outputs it on request. Governance must treat models as privileged agents, not apps.
Outcome
Security becomes social
The largest failures are cultural: asking the model for “a summary of our layoffs plan,” “draft the M&A offer,” or “generate a list of customer churn candidates.”
Paradigm
The mistake: treating AI like tooling
Legacy security models assume a human → app → storage workflow. AI flips this: human → model → memory → global context. The model becomes the workspace.
Models don’t forget
Even when “no training” is enabled, embeddings, session history, and prompt stitching persist information. The model becomes a shadow knowledge base.
Models remix, not isolate
Ask a model for “a competitive analysis” and it might combine internal R&D documents with public pricing leaks and confidential partner agreements.
Models imply authority
Humans trust AI outputs by default. If the model is wrong, hallucinating, or mixing contexts, senior decisions get corrupted quietly.
Failure modes
Where AI behaves like a malicious insider
These patterns do not involve breaches or malware. They exploit human trust.
Leak
Prompt inheritance
User A pastes internal payroll data. User B later asks “help me plan cost savings” and receives those payroll details abstracted into recommendations.
Leak
Model-to-model laundering
Sensitive outputs from Workspace Copilot get pasted into a public AI model for “formatting,” bypassing enterprise controls completely.
Leak
Training residue
Even “no training” does not mean “no memory.” Local embeddings, security logs, vector databases, and semantic caches retain context.
Strategy
Securing model-driven environments
Treat AI agents like employees with superpowers. Your policy must assume misuse.
1. Identity & scope first
AI must have its own identity, RBAC, and scopes. Never let models act as the user or share user credentials.
2. Context segmentation
Sales AI ≠ HR AI ≠ R&D AI. Prevent cross-domain recall. Separate models, embeddings, storage, and inference.
3. Tone down autonomy
Disable “auto-approval,” “auto-action,” or “make changes” features until governance is mature. Humans should confirm everything.
4. Teach users what not to paste
Enforcement must be cultural: “Never paste private financials, legal drafts, or client secrets into the model.”
Where to start
Practical containment moves
You cannot ban AI. You can make it behave like a well-monitored insider.
Separate enterprise vs external AI
Workspace/M365 copilot is not ChatGPT.com. Treat external models as untrusted vendors.
Audit vector stores
The dangerous secrets are not in prompts. They’re in embeddings and retrieval layers.
Shadow AI hunting
Look for unauthorized browser tools, side-loaded extensions, “admin copilots” for internal automation, and bot accounts.
Executive-first training
Teach the board and C-suite to treat models as leakers, not geniuses. Their usage patterns become cultural defaults.