The AI Incident Response Plan You Should Have Written Last Quarter

Your AI system went down at 2:47 PM on a Tuesday. Your first alert came from a customer, not your monitoring stack. The model started returning garbage results three hours ago, and nobody noticed because "the API still returned 200s."

If this scenario hasn't happened to you yet, it will. Here's the incident response plan you need before it does.

What Makes AI Incidents Different

Traditional incident response assumes a clear boundary: the server is up or down, the database is connected or it isn't. AI incidents don't work that way.

Your model can be "running" and still be catastrophically wrong. A data pipeline can silently drift for weeks before anyone notices. A prompt injection can turn your customer-facing chatbot into a liability without triggering a single error alert.

That's why your existing incident response playbook — the one written for infrastructure outages — isn't enough.

Tier 1: Detection (Minutes 0-15)

What's actually broken? Before you do anything, classify the incident:

1. Availability failure: Model or API is down, returning errors, or timing out
2. Integrity failure: Model is running but producing wrong, biased, or dangerous outputs
3. Security failure: Prompt injection, data exfiltration, unauthorized access
4. Compliance failure: System is processing data it shouldn't, or failing required audit controls

Integrity and compliance failures are the ones most teams miss. Your monitoring might be green while your model is recommending the wrong treatment plan or leaking PII through prompt responses.

Immediate actions: Activate your AI incident channel (not the generic ops channel). Identify affected model(s), pipeline(s), and data source(s). Determine blast radius: which customers, which workflows, which data.

Tier 2: Containment (Minutes 15-60)

Stop the bleeding. This is where most AI incident response plans fail because the options are more nuanced than "failover to DR."

For availability failures: Route to fallback model or cached responses. Enable graceful degradation (e.g., "AI suggestions temporarily unavailable"). Scale infrastructure if it's a capacity issue.

For integrity failures: Switch to a previous known-good model version. Disable the affected feature, not the entire system. Implement output filtering or guardrails until root cause is found.

For security failures: Immediately revoke exposed credentials or API keys. Disable the affected endpoint. Block the attack vector (e.g., add input sanitization for prompt injection).

For compliance failures: Stop processing the affected data category immediately. Quarantine any outputs that may contain improperly handled data. Notify your compliance officer within 30 minutes.

Tier 3: Assessment (Hours 1-4)

How bad is it, really? This is where you earn your money.

Key questions to answer: How long was the issue active before detection? What data was affected, and how much? Did any customer-facing outputs contain incorrect or harmful results? Is there a regulatory notification requirement? (HIPAA: 60 days. GDPR: 72 hours. SOC 2: "without unreasonable delay.") Can we reproduce the issue in a test environment?

Document everything. Your incident log should read like a medical chart — timestamps, vital signs, actions taken, and reasoning. If a regulator or auditor reads it later, it needs to demonstrate competent response, not panic.

Tier 4: Recovery (Hours 4-24+)

Fix it properly, not quickly.

1. Identify root cause using the same rigor you'd apply to a production database incident
2. Fix the underlying issue in the model, pipeline, or infrastructure — don't just patch symptoms
3. Validate the fix against a representative test suite before deploying
4. Deploy using your standard deployment process (not a "hotfix bypass" that skips testing)
5. Monitor validation metrics closely for 48 hours post-fix

Tier 5: Post-Mortem (Within 5 business days)

Every AI incident gets a post-mortem. No exceptions.

Your post-mortem should answer: What happened (timeline). Why it happened (root cause, not just proximate cause). What we did to fix it. What we changed to prevent recurrence. What gaps existed in our detection, response, or recovery.

Publish it internally. Your team needs to learn from this. If you can't share it externally (regulatory reasons, customer confidentiality), at least share the pattern and the lesson.

Detection Gaps to Close Now

Most AI incidents we see at Revolution share the same detection failures:

1. Monitoring for uptime, not accuracy. Your model returns 200s while producing garbage. Add accuracy monitoring: track output distributions, confidence scores, and business KPIs tied to model decisions.
2. No data drift alerts. Your training data looked nothing like what the model saw in production this month. Implement statistical drift detection on input features and output distributions.
3. Silent pipeline failures. Your data pipeline broke three days ago, and the model has been serving stale results. Add freshness checks on all pipeline inputs.
4. No guardrail violations. Your chatbot said something harmful, and no one flagged it. Implement automated guardrails that log and block unacceptable outputs.

The Template

Here's the minimum viable AI incident response plan. Copy it, adapt it, train on it.

1. Incident classification: Availability / Integrity / Security / Compliance
2. Escalation path: On-call engineer → AI lead → CTO → Legal/Compliance
3. Detection sources: Monitoring, customer reports, guardrail logs, drift alerts
4. Containment playbooks: One per incident type (see Tier 2)
5. Communication plan: Internal channel, customer notification, regulatory notification
6. Recovery checklist: Root cause → Fix → Validate → Deploy → Monitor
7. Post-mortem template: Timeline, root cause, actions, prevention, gaps
8. Regulatory triggers: HIPAA (60 days), GDPR (72 hours), SOC 2, state breach laws

The Bottom Line

You can't prevent every AI incident. But you can prevent every AI incident from becoming an AI disaster. The difference is preparation.

If your incident response plan doesn't specifically address AI integrity, security, and compliance failures — not just availability — it won't work when you need it most.

We've helped companies build and test these plans. If yours doesn't exist yet, or if you're not sure it would hold up under pressure, that's what we do.

Revolution. Your AI doesn't work. We fix that.