Wrap Up

Points to remember

• Deployment is the moment trust is tested—not where it's assumed. Systems that perform well in lab conditions can still fail under real-world uncertainty, ambiguity, and misuse.

• Verification checks whether systems meet their design specs; validation asks whether those specs actually work in the world they’re deployed into.

• AI agents acting with tool access (e.g., AutoGPT, impersonation bots) can take irreversible actions if not bounded by permissions, review gates, or fallback plans.

• Privacy violations often stem not from the model but from interfaces, plugins, or logs that leak user inputs across contexts—without user awareness or consent.

• Output-level risks escalate once data leaves the model: hallucinated citations, medical misadvice, or irreversible uploads can create downstream harm.

• Containment is not optional. Without soft shutdowns, version rollback, or kill switches, systems that fail will do so in production, not in testing.

• Real-world failures like Watson for Oncology and Cruise robotaxi dragging incidents exposed the need for system-level stop mechanisms and not just model-level checks.

• Human authority must remain embedded in deployment: reviewers who understand the full system lifecycle must be empowered to stop, override, or retract.

• Governance and technical design are inseparable. ISO/IEC 23894, the EU AI Act, and NIST AI RMF all require that trustworthy deployment include intervention, oversight, and rollback.

• Trust isn’t built by dashboards—it’s built by systems that know how to stop themselves, and people who know when to make that call.