This article walks the canonical AI incident classes, the playbook structure that closes them, and the post-incident discipline that turns each event into a permanent improvement to the security posture.
The canonical AI incident classes
A workable AI incident-response program organizes around six recurring incident classes, each with characteristic detection signals, characteristic containment actions, and characteristic post-incident learnings.
Model-extraction incidents are the detection of patterns suggestive of an attacker reconstructing the model through API queries (Article 4). Detection signals include high query volume from a single account or correlated set of accounts, queries that systematically explore the input space, and queries with statistical signatures consistent with surrogate-model training. Containment actions include rate-limit reduction or revocation for the suspect callers, capture of the suspect query traffic for forensic analysis, and (for the most serious cases) temporary endpoint isolation while the response team investigates. Post-incident learning typically tightens the rate-limit policy, strengthens the output-information minimization, and updates the threat model.
Prompt-injection incidents are the discovery that a Large Language Model (LLM) application has been coerced into behaviour the application’s design did not authorize (Article 3). Detection signals include output-filter activations on the LLM response, downstream-system errors traceable to LLM output, user reports of the LLM behaving anomalously, and security-team discovery of indirect injection in retrieved content. Containment actions include immediate disabling of the affected interaction path, isolation of any downstream actions the LLM triggered, and (for indirect injection) identification and quarantine of the poisoned source content. Post-incident learning typically hardens the input separation, tightens the output validation, and reduces the LLM’s downstream authority.
Data-poisoning incidents are the discovery that production training data, a feature store, or a downstream model has been compromised (Article 5). Detection signals include performance regression on holdout evaluation, distribution-monitoring alerts on the data pipeline, and (for backdoors) discovery of trigger patterns in inference traffic. Containment actions include immediate rollback to a prior trusted model version, quarantine of the suspect training data, suspension of automated retraining pipelines, and (for severe cases) recall of any downstream artefacts trained on the compromised data. Post-incident learning typically tightens data provenance, strengthens distribution monitoring, and hardens the holdout evaluation discipline.
Model-theft incidents are the discovery that a model artefact has been exfiltrated or that a stolen-model deployment has been observed externally (Article 4). Detection signals include integrity-verification failures on the model registry, anomalous access patterns to model storage, egress alerts on model-sized data flows, and external observation of suspected stolen-model use. Containment actions include immediate rotation of the storage credentials, audit of every recent access, and (where commercially feasible) initiation of legal and contractual response against the suspected exfiltrator. Post-incident learning typically tightens IAM, strengthens egress monitoring, and accelerates the deployment of integrity controls and watermarking.
Adversarial-evasion incidents are the discovery that adversarial inputs are being used in production to bypass the model’s intended behaviour (Article 2). Detection signals include OOD-detector alerts, downstream consequence reports (fraud that should have been caught, content that should have been moderated, transactions that should have been flagged), and external research disclosure of an attack against the model class. Containment actions include immediate hardening of the input-validation layer, deployment of additional adversarial-detection content, and (for severe cases) shifting affected traffic to a fallback model or to human review. Post-incident learning typically schedules adversarial retraining, expands the red-team scope (Article 11), and updates the threat model.
Supply-chain compromise incidents are the discovery that an upstream dependency, base model, dataset, or framework has been compromised (Article 12). Detection signals include vulnerability disclosures in upstream sources, unexpected behaviour traceable to a recently updated dependency, and (rarely) external notification by the upstream provider. Containment actions include immediate rollback to a prior trusted version of the affected dependency, audit of all artefacts built or deployed during the exposure window, and quarantine of any artefacts that may carry the compromise forward. Post-incident learning typically tightens vendored mirroring, strengthens AI-BOM coverage, and accelerates dependency-monitoring response.
The MITRE ATLAS knowledge base https://atlas.mitre.org/ catalogs the attack techniques each incident class corresponds to and provides the reference taxonomy that incident-response playbooks should align with. The OWASP Top 10 for Large Language Model Applications https://owasp.org/www-project-top-10-for-large-language-model-applications/ provides the LLM-specific incident catalog. The NIST AI Risk Management Framework Cybersecurity profile https://www.nist.gov/itl/ai-risk-management-framework and NIST SP 800-218A https://csrc.nist.gov/pubs/sp/800/218/a/final prescribe AI-specific incident response as a managed practice.
Playbook structure that closes incidents
The playbook for an AI incident class has the same five-section structure regardless of the specific class.
Trigger and detection. The section names the detection signals that bring the incident to the response team’s attention, the data sources that produce each signal (the SIEM from Article 13, model-quality monitoring, external notification), and the criteria for elevating the signal to a declared incident. The clarity prevents the false-positive fatigue that erodes detection programs and the false-negative gap that leaves real incidents unaddressed.
Roles and engagement. The section names the roles that are engaged on declaration — the incident commander, the AI-system owner (the engineering lead for the affected model), the security analyst, the platform operator, the legal liaison, the communications lead — and the criteria for escalating to senior management or to external response (regulators, customers, law enforcement). The clarity prevents the coordination breakdown that consumes early incident time.
Containment and eradication. The section walks the specific actions to be taken in priority order, with time bounds. For each action the playbook names who executes, what evidence to preserve before executing, and the rollback path if the action makes the situation worse. The clarity prevents the cascading-error pattern in which well-intentioned response actions destroy forensic evidence or create secondary incidents.
Recovery and verification. The section walks the steps to restore normal operation, with explicit verification criteria for each restored component. The clarity prevents the premature-closure pattern in which the team declares the incident closed before the underlying issue is in fact fixed.
Post-incident activity. The section walks the retrospective process, the documentation that is produced, the threat-model update, and the control improvements that the incident drives. The clarity prevents the incident-amnesia pattern in which the same class of incident recurs because no permanent improvement was made the first time.
The European Union’s AI Act, Article 15 https://artificialintelligenceact.eu/article/15/, requires high-risk AI systems to be designed with cybersecurity controls that include the operational ability to respond to incidents. The Act’s broader provisions (the serious-incident reporting obligation in particular) add specific external-engagement requirements to the playbook for incidents affecting high-risk systems. ISO/IEC 42001:2023 Annex A.7 https://www.iso.org/standard/81230.html requires AI Management System operators to establish incident-management processes that explicitly contemplate AI-specific failure modes.
Post-incident discipline
The post-incident retrospective is the leverage point at which an incident becomes a permanent improvement to the posture. The retrospective produces three artefacts.
The incident report. A written record of what happened, when, what response actions were taken, what worked, what did not, and what the residual risk is. The report is the authoritative artefact for audit, regulatory inquiry, and executive briefing.
The control update. The specific changes to controls — to the threat model, to the playbooks, to the engineering practice, to the platform configuration — that the incident motivates. The control update is tracked to closure in the same backlog the engineering team uses for any other change and is verified by the next red-team engagement (Article 11).
The detection update. The specific changes to the SIEM detection content (Article 13), the monitoring thresholds, and the alerting routes that would have caught the incident earlier. The detection update is tracked to deployment and verified by the synthetic test that exercises the new content.
The Gartner AI TRiSM Hype Cycle https://www.gartner.com/en/articles/gartner-top-strategic-technology-trends-for-2024 tracks the maturity of AI-specific incident-management tooling and notes the increasing convergence of AI incident-response platforms with the broader security-operations platform.
Maturity Indicators
Foundational. The organization has no AI-specific incident playbook. AI incidents, when they occur, are handled ad hoc by whichever team notices first. There is no integration between AI-team operational mitigation and security-team forensic investigation. Post-incident retrospectives, where they occur, do not drive permanent control improvements.
Applied. The organization has documented playbooks for at least two of the canonical AI incident classes. Detection signals are defined and routed. Roles for AI incident response are named. The team has executed at least one tabletop exercise.
Advanced. Playbooks exist for all six canonical AI incident classes, integrated with the broader security incident-response practice. Detection content is maintained in the SIEM (Article 13) for each class. Tabletop exercises run on a scheduled cadence. The threat model from Article 1 incorporates lessons from past incidents. The COMPEL Domain D13 maturity rubric Level 4 indicators are met.
Strategic. AI incident response is integrated with the broader risk and governance functions (Article 10 — TRiSM). External notification obligations (regulator, customer, contractual) are tracked and met within their required windows. Post-incident retrospectives drive permanent control improvements that are verified by subsequent red-team engagements. The organization contributes to industry incident-sharing initiatives (the AI Incident Database, sector-specific Information Sharing and Analysis Centers). The incident-response posture is itself audited on a regular schedule by external specialists.
Practical Application
A team that has no AI-specific incident playbooks should produce the first one this quarter, addressing the incident class the threat model from Article 1 identifies as highest priority. The playbook follows the five-section structure above, names the specific tooling and people involved, and is reviewed by the incident commander, the AI-system owner, the security lead, and the governance liaison together.
Once the first playbook exists, the team runs a tabletop exercise against it — the security team simulates an incident matching the playbook’s class, the response roles execute the playbook in real time, and the exercise lead documents what worked and what broke. The exercise typically surfaces missing tooling, ambiguous decision criteria, and coordination gaps that are easy to fix in writing and impossible to discover except through exercise.
The team then iterates: a second playbook for the next-highest-priority class, a tabletop exercise against it, refinement, and so on through the six canonical classes. The cumulative exercise builds the institutional muscle memory that distinguishes a program that responds to incidents effectively from one that learns through pain how to do so the first time. Article 15 of this module shows how the incident-response evidence integrates into the broader compliance posture the AI program must demonstrate.
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