# AI Incident Response Readiness Checklist: Are You Ready for an AI Security Incident? > Source: https://netray.co/tools/ai-incident-response-readiness-checklist > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer A practical readiness checklist for AI-specific incident response, covering detection, playbooks by incident type, escalation roles, containment, and post-incident reporting. ## Category on-prem-ai ## Tool **Kind:** checklist **Groups:** #### Detection and alerting **Items:** **Text:** Anomaly detection is tuned specifically for AI systems: unusual prompt volume, oversized inputs, and repeated jailbreak-pattern attempts trigger alerts **Critical:** true Output filtering alerts on generated content matching known-sensitive patterns such as credentials or export-controlled identifiers Alerts route to a monitored channel with defined response time expectations, not to a log nobody actively watches Detection coverage includes both the model layer and the surrounding application (retrieval, tool calls, action execution) #### Playbooks by incident type **Items:** **Text:** A written playbook exists specifically for suspected model weight compromise or unauthorized model substitution **Critical:** true **Text:** A written playbook exists specifically for a successful prompt injection that resulted in an unintended action or data exposure **Critical:** true A written playbook exists specifically for data leakage through generated output, distinct from a traditional data breach playbook A written playbook exists for suspected model weight or fine-tuning data theft Playbooks specify concrete first steps (isolate, preserve logs, notify) rather than general guidance #### Roles and escalation **Items:** **Text:** A named incident commander role exists for AI-specific incidents, with a defined backup **Critical:** true Escalation paths to legal, compliance, and executive leadership are defined with specific triggers, not left to judgment in the moment Contractual and regulatory notification obligations (customer flow-down, CMMC, breach notification law) are pre-mapped so the team is not researching them during an active incident Roles are cross-trained so a single person's unavailability does not stall the response #### Containment and forensics **Items:** **Text:** A documented, tested procedure exists to isolate an AI system from production traffic within minutes, not hours **Critical:** true Log preservation steps are defined so an investigation is not hampered by rotating or overwritten logs during the response A process exists for capturing the exact model version, prompt template, and configuration in effect at the time of a suspected incident Rollback to a known-good model version and configuration has been tested, not merely assumed to work #### Post-incident and reporting **Items:** **Text:** A post-incident review process captures root cause and produces concrete follow-up actions, not just a closed ticket **Critical:** true Findings from AI incidents feed back into the adversarial testing and hardening checklist so the same gap does not recur A template exists for producing customer or regulator-facing incident reports without starting from a blank page Tabletop exercises simulating an AI-specific incident are run at least annually to validate the playbooks under low-stakes conditions **Scoring Note:** AI incident response readiness is defensible when at least 90% of all items are complete and every critical item is closed. The critical items address the two things that determine whether an AI incident stays contained or becomes a serious event: whether you can isolate the system fast enough to stop ongoing damage, and whether a specific playbook exists for the incident type in front of you rather than a generic security incident process that was never adapted for how AI systems actually fail. ## Content **Intro:** This free AI incident response readiness checklist covers the detection, playbooks, roles, and post-incident processes needed to handle an AI-specific security incident, and it is written for security leads, IT directors, and compliance managers who have a general incident response plan but have not yet adapted it for AI systems. It spans five domains: detection and alerting, playbooks by incident type, roles and escalation, containment and forensics, and post-incident reporting. AI incidents fail differently than traditional security incidents: a compromised model can look and behave normally while producing subtly wrong output, and a successful prompt injection may leave no trace in a traditional security log at all. **Sections:** #### Why AI incidents need distinct playbooks **Body:** A traditional incident response plan assumes recognizable failure signatures: unauthorized login, malware signature, unusual network traffic. AI incidents often lack any of these markers. A prompt injection that causes an agent to take an unintended action looks, from a network perspective, like a normal authenticated user making a normal API call, because it is, the attack lives entirely inside the content the model processes. A subtly compromised or tampered model produces output that looks plausible and passes a casual glance, which means detection has to happen through output monitoring and behavioral baselines, not through the signatures traditional security tooling is built to catch. **Bullets:** - Prompt injection incidents rarely trigger traditional network or authentication-based alerts. - A compromised model can produce plausible-looking output for an extended period before anyone notices something is wrong. - Containment requires isolating the AI system specifically, which is a different action than isolating a compromised endpoint or account. - Notification obligations for an AI incident may differ from a standard data breach, particularly around customer flow-down and export control requirements. #### The controls that matter most **Body:** The critical items address speed and specificity, the two factors that most determine whether an AI incident stays contained. A tested isolation procedure that works in minutes prevents an ongoing injection or compromise from causing further damage while the investigation proceeds. Specific playbooks for model compromise, exploited injection, and output-based data leakage matter because a generic incident response process, built around traditional attack patterns, will waste critical early time figuring out what kind of incident this even is before anyone starts responding to it. **Bullets:** - A tested isolation procedure that removes an AI system from production traffic within minutes. - Specific playbooks for model compromise, exploited prompt injection, and output-based data leakage. - A named incident commander role for AI-specific incidents, with a trained backup. - A structured post-incident review that produces concrete hardening follow-up, not just a closed ticket. #### How to work through the checklist **Body:** Start by adapting your existing incident response plan rather than building an entirely separate one; add AI-specific playbooks as a new category within it and pre-map the escalation and notification requirements that apply specifically to AI incidents. Run a tabletop exercise simulating a specific scenario, such as a successful prompt injection resulting in an unauthorized data export, and use it to find gaps in your current playbooks before a real incident does. Feed every finding, from the tabletop and from any real incident, back into your security hardening checklist so the same gap gets closed rather than just documented. #### How Netray prepares customers for AI incidents **Body:** Netray builds AI incident response readiness alongside the AI systems we deploy for manufacturers, rather than treating it as a separate afterthought engagement. We write playbooks specific to your architecture, model compromise for a self-hosted deployment looks different than for an API-backed one, test isolation procedures against your actual production environment, and run tabletop exercises that surface gaps before they become real incidents. For customers with existing incident response programs, we integrate AI-specific playbooks and escalation paths into what you already have rather than asking you to run two parallel processes. **Cta:** Get an AI-specific incident response playbook and a tabletop exercise scoped to your actual deployment. **Faq:** #### How is an AI security incident different from a traditional data breach? **Answer:** The failure mode is different: a traditional breach usually involves unauthorized access to a system or database, with recognizable indicators like anomalous logins or data transfers. An AI incident can occur through legitimate, authenticated use of the system, with the attack living entirely inside the content the model processes, such as an injected instruction hidden in a retrieved document. Detection requires monitoring model behavior and output, not just access patterns, which is why a generic breach playbook is not sufficient on its own. #### How quickly should we be able to isolate a compromised AI system? **Answer:** Minutes, not hours. The longer a compromised or manipulated AI system stays connected to production, the more damage accumulates, whether that is continued data exposure, incorrect actions taken on production systems, or reputational harm from bad output reaching users. Test your isolation procedure specifically, do not assume that a general system takedown process works cleanly for an AI system with its own dependencies and integrations. #### Do we need separate incident response playbooks for each type of AI risk? **Answer:** You need enough specificity that responders are not improvising the first critical steps. At minimum, separate playbooks for model compromise, exploited prompt injection, and output-based data leakage cover the most common and distinct failure patterns, since each requires different first actions: isolating and verifying model integrity, tracing an injection's origin and blast radius, or containing and assessing a data exposure through generated content. #### How often should we test our AI incident response plan? **Answer:** At least annually with a tabletop exercise, and again whenever you make a significant architecture change such as adding new tool access to an agent or switching model providers, since either can invalidate assumptions your playbooks were built on. Treat the plan as a living document that gets updated after every real incident and every exercise that surfaces a gap, not as a document written once and filed away. ## Search terms AI incident response checklist, LLM incident response plan, AI security incident readiness, model compromise response plan, AI data leak incident response, prompt injection incident response --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json