Phase 8: Production AI Security Architecture · 60 min · dataclasses · enum · argparse
AI Incident Response Playbooks
A guardrail that fails silently and an incident plan that only exists in someone's head fail the same way -- at 2am, with no one sure what to do first.
Hiring signal: AI Security Engineer and Incident Response roles at companies like Microsoft, Anthropic, and OpenAI increasingly require a documented IR process specific to AI failure modes -- not a generic SOC playbook with 'AI' pasted on top. Being able to produce a structured, regulator-ready incident report for prompt injection, data poisoning, and model extraction within minutes of detection, with the right disclosure obligations already identified, is exactly the artifact a hiring panel asks for in an incident-response interview loop.
What you will learn
- Write incident-type-specific detection, triage, containment, eradication, and recovery steps for prompt injection, data poisoning, and model extraction
- Produce a blameless post-mortem that identifies the control-layer gap rather than assigning individual blame
- Compute regulatory reporting obligations (GDPR, EU AI Act, NIST AI RMF) from the specific facts of an incident rather than a static checklist
- Ground every response step in a concrete artifact from an earlier lesson in this phase (architecture layers, monitoring signals, CI/CD gates, supply chain scanner) so the plan is executable, not aspirational
The Problem
Every prior lesson in this phase built a control: an architecture validator, a monitoring signal, a CI/CD gate, a supply chain scanner. Controls fail anyway. Guardrails get bypassed by a novel jailbreak. A retrieval index gets poisoned before anyone notices the output drift. An API key gets used for a slow, patient extraction campaign that looks like nothing until someone finally cross-references the query log. When one of these happens, the question stops being "how do we prevent this" and becomes "what do we do in the next sixty minutes, and who has to be told, and by when."
Most teams answer that question badly, for a specific reason: their incident response plan is either a generic SOC/AppSec runbook that has nothing to say about a poisoned retrieval index or a model that's leaking its system prompt, or it exists only as tribal knowledge in the head of whoever built the guardrail pipeline. Neither survives contact with an actual incident at 2am when that person is on a plane.
AI systems also carry regulatory obligations that a standard security incident often doesn't. The EU AI Act's Article 73 requires providers of high-risk AI systems to report serious incidents to a market surveillance authority — with materially shorter deadlines than most companies' existing breach-notification process assumes. If personal data was exposed, GDPR Art. 33/34's 72-hour clock starts running the moment you become aware, regardless of whether you have a report ready. NIST's AI RMF expects every confirmed incident to feed back into the Govern function's risk register, not just get fixed and forgotten. A playbook that doesn't compute these obligations from the specific facts of the incident either over-reports (burning legal and PR resources on incidents that don't meet the bar) or under-reports (missing a deadline that has real financial and legal consequences).
This lesson builds a playbook generator for the three incident categories unique to AI systems — prompt injection, data poisoning, and model extraction — following the same detection → triage → containment → eradication → recovery → post-mortem structure a mature security team already uses, but with every step grounded in the specific artifacts this phase already built, and a regulatory-obligations calculator that reasons from facts instead of applying a blanket checklist.
Prompt Injection Incident Response
A prompt injection incident starts differently depending on whether it's direct (an attacker-authored prompt) or indirect (a payload smuggled in through a retrieved document, email, or tool output — the XPIA pattern from earlier in the course). The detection signal is usually SecurityMonitor.injection_rate_alerts from Lesson 2 crossing its rolling-window threshold, or a single request flagged critical by the input-layer classifier from Lesson 1.
Triage has one question that determines almost everything downstream: did the injection reach a tool call with side effects? An injection that only changes conversational tone is a high-severity annoyance. An injection that reaches send_email or delete_record through the guardrails-layer tool-authorization gate is critical, and containment means revoking the offending API key or session immediately — before root cause is even understood. Waiting to fully understand an attack before containing it is the single most common mistake in early-stage incident response, for AI incidents as much as any other kind.
Eradication isn't complete when the immediate bypass is patched. The observed payload has to become a permanent regression case in the CI/CD adversarial suite from Lesson 3, or the exact same technique works again the next time someone "temporarily" relaxes a filter to fix a false positive.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Data Poisoning Investigation, Model Extraction Detection and Response, Post-Mortem Templates and Regulatory Reporting, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
Browse all courses · View pricing · DeVenture Academy