What problem does this system address?
AI systems fail in ways that traditional incident management frameworks do not cover: bias incidents that harm specific populations, hallucination cascades that spread misinformation, privacy breaches from model memorization, and adversarial exploitation of model weaknesses.
I built this framework after an AI-powered recommendation system produced biased outputs for 6 hours before anyone recognized it as an incident. The traditional incident management process triggered on system downtime and error rates. It did not trigger on output quality degradation that affected only specific demographic groups. The system was technically operational. It was ethically failing. Nobody was on call for that type of failure.
How is the system structured?
The framework adapts the SRE incident management model (severity classification, on-call rotation, response playbooks, post-incident review) to 4 categories of AI-specific failures, with detection mechanisms, response procedures, and communication templates for each.
Step 1: AI incident classification
I define 4 incident categories with distinct severity levels. Bias incidents (output disparities exceeding fairness thresholds for specific populations) range from SEV-3 (minor threshold exceedance) to SEV-1 (systematic harm to protected groups). Hallucination incidents (fabricated information in high-stakes outputs) are classified by downstream impact potential. Privacy incidents (model memorization or data leakage) follow existing data breach severity frameworks. Adversarial incidents (deliberate exploitation of model weaknesses) are classified by the scope of the exploitation. Each category has automated detection: fairness metrics monitoring for bias, claim verification sampling for hallucination, canary data testing for privacy, and anomaly detection for adversarial patterns.
Step 2: Response playbooks
Each incident category has a structured playbook. For bias incidents: immediately assess scope (which populations affected, how many users impacted), apply the pre-defined mitigation (model rollback, output filtering, or service degradation to a safer mode), notify affected users when feasible, and preserve evidence for post-incident analysis. For hallucination incidents: activate the guardian agent verification layer, switch to retrieval-only mode (no generative responses), and audit the last 24 hours of outputs. Playbooks are versioned documents stored alongside runbooks in the operations repository.
Step 3: Communication and post-incident review
I adapted the blameless post-incident review from SRE practice. Every SEV-1 and SEV-2 AI incident produces a post-incident report documenting: what happened, who was affected, what the root cause was, what the immediate response was, and what systemic changes will prevent recurrence. Unlike traditional incident reports, AI incident reports include an ethical impact assessment: what harm occurred, to whom, and what remediation is appropriate. Reports are shared with the architecture decision record system to inform future design decisions.
How do you validate it works?
Validation uses tabletop exercises (simulated AI incidents testing response procedures), detection latency measurement (time from incident onset to alert), and trend analysis (incident frequency and severity over time).
I run quarterly tabletop exercises simulating each incident category. The team walks through the response playbook with a realistic scenario. These exercises have revealed 7 gaps in the playbooks that we addressed before real incidents exposed them. Detection latency improved from an average of 6 hours (before the framework) to 23 minutes (after), because automated monitoring catches ethical failures that human observation misses. According to Google’s SRE handbook, incident management maturity is measured by mean time to detection and mean time to resolution. For AI incidents, I add a third metric: mean time to ethical impact assessment.