AI Incident Response: Adapting Proven Complex Systems Engineering Practices for AI-Enabled Systems

⚡ Quick Summary

This white paper by Heather Frase delivers one of the most operationally grounded contributions to AI governance to date. Instead of proposing yet another abstract risk taxonomy, it translates decades of incident response experience from aviation, cybersecurity, healthcare, financial crime, and reliability engineering into a concrete, seven-step AI incident response loop. The document treats AI systems as what they truly are: complex, evolving systems whose failures rarely look like clean “bugs.” Its core strength lies in reframing AI incidents as organizational learning opportunities rather than isolated crises. By embedding preparedness, standardized reporting, and ecosystem-level coordination into the response process, the framework moves the conversation from reactive harm control to continuous reliability improvement. This is not a conceptual vision paper; it is a practical blueprint that organizations can implement immediately, even with imperfect tooling and partial maturity.

🧩 What’s Covered

The paper starts by diagnosing a fundamental gap: most organizations deploy AI systems without the foundational structures needed to respond when those systems fail in the real world. It explains why AI incidents differ from traditional software or cybersecurity incidents, emphasizing non-determinism, user-dependent behavior, system-of-systems interactions, and failures that emerge gradually rather than explosively.

At the heart of the document is a seven-step incident response loop: Detect, Assess, Stabilize, Report & Document, Investigate & Analyze, Correct, and Verify. Each step is paired with explicit preparedness requirements, reinforcing the idea that effective response depends on decisions made long before an incident occurs. The framework adapts established disciplines rather than reinventing them, drawing structure from cybersecurity incident response, analytical depth from systems engineering, metrics from reliability engineering, and foresight from failure mode and effects analysis.

Beyond the internal organizational view, the paper introduces an ecosystem model for AI incident response. It clearly distinguishes the roles of developers, deployers, users, regulators, auditors, standards bodies, and professional organizations, showing how coordinated reporting and analysis enable pattern recognition that no single actor can achieve alone. The document also differentiates between simple and complex incidents, safety versus security events, and misuse versus system failure, offering practical guidance on escalation and coordination.

💡 Why it matters?

AI governance often fails at the moment it matters most: when something goes wrong in production. This paper fills that gap by offering a credible, implementable response model aligned with how complex systems actually fail. It directly supports regulatory expectations under emerging regimes like the EU AI Act, while remaining usable for engineering, risk, and compliance teams today. Most importantly, it reframes incident response as a core reliability capability, not a compliance afterthought.

❓ What’s Missing

The framework is intentionally general, which is a strength, but some organizations may look for more concrete tooling examples, KPIs, or sector-specific playbooks. A companion document translating the seven steps into sample policies, incident templates, or maturity models would further accelerate adoption. Deeper alignment with specific regulatory reporting thresholds could also strengthen its compliance utility.

👥 Best For

AI governance leads, risk and compliance teams, incident response and SRE functions, AI product owners, regulators, auditors, and standards bodies looking for a serious operational foundation for AI incident response. Particularly valuable for organizations deploying agentic or high-impact AI systems.