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9. Secure GenAI / ML — executive summary

Why this matters

GenAI systems introduce new security risks that bypass traditional controls. These risks do not come from broken infrastructure, but from uncontrolled influence over AI behavior.

Even well-secured environments can produce:

  • Data leakage
  • Unauthorized actions
  • Silent behavior drift
  • Compliance violations

What is different from traditional applications

  • AI behavior is probabilistic, not deterministic
  • Data and prompts influence decisions directly
  • Models evolve after deployment
  • Security failures may not be repeatable

(See the full minibook, starting with chapter 2.)

Key risk areas

  • Prompt and context manipulation
  • Retrieval of untrusted or sensitive data
  • AI-driven tool execution
  • Training and feedback loop poisoning
  • Undetected behavioral drift

Our security approach

  • Threat-model-first architecture reviews (chapter 6)
  • Clear control of who can influence AI behavior
  • MLSecOps governance for models and data (chapter 7)
  • Continuous monitoring of AI behavior, not just uptime
  • Human approval for high-risk AI actions

Leadership takeaway

AI security is not about blocking inputs — it is about controlling influence and accountability.

Organizations that treat AI like traditional software will lose control over time.

For the full argument and principles, see chapter 8 — conclusion.