AI incidents need a new playbook. Here’s how to build one

Most organizations have AI systems in production but lack incident response (IR) playbooks specifically designed for AI failures, relying instead on traditional security frameworks that don't address AI-specific problems. AI incidents fall into two categories with very different causes and defenses: model-originated failures (like hallucinations or bias that happen during normal operation) and externally induced failures (like adversarial attacks or data poisoning), plus hybrid cases where AI errors create legal liability. Traditional security frameworks like the CIA triad (confidentiality, integrity, availability) don't detect many AI incidents because they assume deterministic, static failures, but AI systems produce probabilistic outputs that can't be patched like code vulnerabilities.