Prevention is essential, but it is no longer enough. If AI systems are in production, incident response must evolve accordingly.
Detection starts with the right telemetry: unusual prompt patterns, policy bypass attempts, anomalous tool-call sequences, retrieval abuse, and unexplained spikes in high-privilege actions. You also need behavioral baselines for agents, not just infrastructure alerts.
Containment requires AI-specific controls: revoke model-to-tool tokens, isolate compromised agent workflows, disable high-risk actions, and switch critical paths to manual approval. Recovery means more than restoring services; it includes cleansing contaminated memory/context, rotating credentials, validating model behavior post-incident, and documenting root cause across model, data, and orchestration layers.
This is why “AI incident readiness” is becoming its own discipline. It sits between SOC, platform engineering, legal, and business operations. Without cross-functional playbooks, organizations lose precious hours deciding who owns what while damage accumulates.
If your AI assistant approved the wrong action at scale tomorrow, would your team know exactly how to detect, contain, and communicate it in the first hour?