Build an AI cybersecurity incident response plan into your existing incident-response program, rather than creating a disconnected AI-only process. Start with the current general foundation: NIST finalized SP 800-61 Rev. 3 on April 3, 2025, superseding Rev. 2 and aligning incident response with the six functions of NIST Cybersecurity Framework 2.0. Then add the AI system details responders need to make safe decisions: dependencies, owners, evidence sources, containment authority, and tested fallback options.
The plan should let a response team answer three questions quickly: what may have been compromised, how to limit harm without needlessly destroying evidence or interrupting critical services, and who has authority to choose the next step.
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Use the incident-response lifecycle as your foundation
NIST SP 800-61 Rev. 3 places incident response within the six functions of CSF 2.0: Govern, Identify, Protect, Detect, Respond, and Recover. Govern, Identify, and Protect support preparation and risk reduction; Detect, Respond, and Recover cover finding, handling, and recovering from incidents. That makes the plan a working part of security governance and operations—not just a document responders open after an alert.
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Use the NIST AI Risk Management Framework (AI RMF) as voluntary context for AI inventory, lifecycle roles, and risk decisions. Its functions are Govern, Map, Measure, and Manage, and it covers AI design, development, use, and evaluation. NIST’s AI RMF Playbook offers suggested actions; it is not a mandatory checklist. The framework’s resource page has indicated that the AI RMF is being updated, so check the current NIST status when adopting it.
Keep the distinction clear: cybersecurity incidents may overlap with model quality, safety, privacy, or policy problems, but those are not automatically security incidents. Define how those functions join the response when there is a suspected compromise, unauthorized access or change, data exposure, or other cybersecurity concern.
Set scope, authority, and system ownership
Define what the plan covers
List the AI-enabled services in scope, including internally developed and third-party systems, and the supporting infrastructure that could affect their security or availability. State what events qualify for incident handling, how suspected AI-related security events are routed, and how the plan connects to existing incident, business-continuity, privacy, safety, and crisis procedures.
Make clear who can declare an incident, lead the response, and authorize consequential actions such as disabling a feature, shutting down a critical service, or rebuilding a system. If different people hold those authorities, name them and establish how decisions are recorded and escalated.
Maintain an AI system and dependency inventory
Responders cannot assess an AI incident from a model name alone. Maintain a usable record for each system and update it whenever architecture, providers, data, or deployment changes.
Rank #2
- Purpose and consequence: business owner, intended use, risk context, affected users, and decisions or services that depend on outputs.
- Model and hosting: model identity and version, provider, hosting environment, internal or external ownership, and deployment path.
- Data: training or fine-tuning dependencies, retrieval corpus and sources, sensitive inputs or outputs, and the pipelines that move or update data.
- Connections and access: APIs, interfaces, plugins, tools, credentials, identity systems, and downstream systems that can act on outputs.
- Operations and evidence: configuration and deployment history, available logs, retention owners, and supplier incident contacts.
- Continuity: a manual or alternate service, if available, and the conditions and procedure for switching to it.
For each dependency, identify who can confirm whether it is affected and who can change or isolate it. Include supplier-managed components even when your team cannot inspect their internals.
Assign roles and escalation paths
Name an incident lead and deputy, security operations and incident handlers, AI or ML engineering, platform and service owners, and the business owner. Include legal, privacy, safety or risk, communications, and executive decision-makers where relevant, as well as external providers. Document after-hours contacts, escalation routes, and decision rights; do not leave responders to discover who can approve an action during an outage.
Define activation, severity, and triage
Set thresholds using impact, not a single model score
Establish severity levels and activation thresholds that reflect both security and operational consequences. A model-quality score alone cannot tell responders whether an incident is a compromise or how serious it is. Consider the combined picture:
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- Confidentiality, integrity, and availability: what may have been exposed, altered, or made unavailable?
- Users and decisions: who is affected, and what decisions or actions rely on the system?
- Data and access: are sensitive prompts, outputs, retrieval sources, or credentials involved?
- Operational and safety consequences: could disruption or incorrect use harm a critical service or people?
- Scope and spread: are connected systems, suppliers, accounts, or other AI services affected?
- Containability: can the suspected access or behavior be bounded, and how quickly?
Define when to activate the incident team, who becomes incident commander, how quickly roles are notified, and what events require escalation. Keep a record of key decisions, their owners, and the time they were made.
Rank #3
Make triage evidence-led
Record the alert source and timestamps, then investigate whether a suspicious change or exposure involves model behavior, access, data, prompts, retrieval content, tool calls, API keys, deployment artifacts, or a supplier service. Compare the event with recent approved changes and known system behavior. The objective is to distinguish malicious activity from drift, a normal update, or a benign failure—not to infer cause from an output alone.
Ask system and domain owners to help establish what changed and what consequences are plausible. Preserve uncertainty in the incident record: what is observed, what is inferred, and what still needs validation.
Preserve evidence while choosing containment
Write down evidence-handling rules before an incident
Specify likely evidence sources, who controls each one, retention periods, time synchronization expectations, access restrictions, and chain-of-custody procedures. Name approved forensic support and explain how responders should preserve volatile evidence before taking actions that might erase it. Limit access to collected sensitive data.
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Pre-authorize containment options and tradeoffs
For each critical service, identify the people authorized to act and the technical steps for each option. There is no universally safest sequence: the right choice depends on the suspected compromise, evidence at risk, service consequences, reversibility, and the expertise required.
| Option | Potential use | Questions to settle in advance |
|---|---|---|
| Revoke credentials | Limit suspected unauthorized access to accounts, APIs, or tools. | Which identities and secrets are in scope? Who can revoke and rotate them? What dependent services could fail? |
| Block an integration or isolate a service | Break a suspected path between the AI system and a tool, API, or other system. | Can the connection be isolated without taking down unrelated services? What records should be captured first? |
| Disable a model or feature | Stop a suspected compromised or harmful capability from being used. | Who can approve disablement? What users or downstream decisions rely on it, and what alternative will they use? |
| Roll back a deployment | Return to a prior version when a recent deployment is suspected. | Is the earlier version trusted and compatible with current data and dependencies? Can the rollback be reversed? |
| Suspend a data pipeline | Stop suspected unauthorized or harmful changes from entering a training, retrieval, or operational data source. | Which pipeline is affected? What evidence or business process could be lost or delayed by pausing it? |
| Switch to manual or alternate operation | Preserve an essential service while isolating or investigating the AI component. | Has the fallback been tested? Who operates it, for how long, and with what capacity or safety limits? |
Set decision criteria for each option, including urgency, evidence-preservation steps, expected service impact, and approval authority. Containment should reduce risk without needlessly destroying evidence or creating greater harm downstream.
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Remove unauthorized access and restore trusted components
Once the team has bounded the incident, define how it will remove malicious access or artifacts, rotate affected secrets, and validate the integrity and provenance of relevant data and models. Identify the trusted sources and approvals needed to rebuild or restore components; do not assume a backup or prior model version is safe merely because it exists.
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Before restoring normal operation, test the system against security and business requirements. Check relevant access controls, integrations, data sources, model and configuration versions, and intended functions. Name the security and system owners who must approve restoration, the monitoring required after return to service, and the conditions that would trigger renewed containment. Keep recovery dependencies and fallback instructions current.
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Plan communications, supplier coordination, and reporting
Specify how the incident lead will provide internal leadership updates and who prepares communications for affected users. Include vendor escalation contacts, insurer or contractual contacts where applicable, and paths for consulting regulators or law enforcement. Assign owners for each communication and record what was sent and when.
Reporting duties cannot be determined in the abstract: they depend on incident facts, jurisdiction, sector, affected data, and contractual terms. Map the organization’s actual obligations and deadlines with appropriate legal review; do not treat a general framework or information-sharing process as a substitute for that analysis.
CISA’s JCDC AI Cybersecurity Collaboration Playbook, announced January 14, 2025, supports voluntary sharing of AI cybersecurity incident and vulnerability information and encourages partners to integrate sharing into response processes. Treat this as an optional coordination path, not a replacement for mandatory notification decisions.
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Run tabletop exercises that test both technical response and decision authority. Useful scenarios include compromised AI credentials, unauthorized or poisoned data changes, exposed sensitive prompts or outputs, a compromised supplier, malicious tool or plugin use, and disruption of an AI-dependent service.
For each exercise or real incident, record what responders did, how long decisions took, which assumptions failed, what evidence was unavailable, and where the fallback or escalation path was unclear. Assign an owner and due date to each corrective action. Feed the lessons into the system inventory, safeguards, playbooks, contacts, and staff training.
Practical plan-writing checklist
- Define covered services, incident boundaries, declaration authority, and connections to existing response procedures.
- Maintain an AI and dependency inventory with business impact, suppliers, logs, access paths, and tested fallback options.
- Name primary and after-hours responders, deputies, approvers, and external contacts.
- Set impact-based severity and activation thresholds, escalation deadlines, and decision logging.
- Document triage questions and evidence sources, handling rules, retention owners, and access controls.
- Pre-authorize containment actions with their service, evidence, reversibility, and approval considerations.
- Define eradication, restoration testing, sign-off, post-recovery monitoring, and fallback procedures.
- Map communications and reporting decisions to the organization’s actual contracts, jurisdictions, sector, data, and legal review.
- Exercise plausible AI-related scenarios and track fixes through completion.
Use NIST SP 800-61 Rev. 3 as the general response foundation and the AI RMF as voluntary risk-management context. NIST IR 8596, Cybersecurity AI Profile, was an Initial Preliminary Draft dated December 2025 in the source reviewed here; it should not be presented as finalized guidance. Frameworks can structure decisions, but the operational plan must fit the organization’s systems, consequences, and authority.
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