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Start with your existing incident-response framework
Do not create a separate AI plan that competes with your established security process. Add AI-specific procedures to the organization-wide framework so responders use the same escalation, decision-making, and recovery structure for AI services as for other critical technology.
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NIST SP 800-61 Rev. 3, finalized in April 2025, superseded Rev. 2. It organizes cybersecurity risk management around Govern, Identify, Protect, Detect, Respond, and Recover, with lessons feeding continuous improvement. NIST describes incident response as “a critical part of cybersecurity risk management” that should be integrated across organizational operations. Use that lifecycle as the backbone, then add AI system details where your existing procedures need them.
NIST AI RMF 1.0 is voluntary, and NIST says it is being revised. NIST IR 8596, the AI-specific cybersecurity profile relevant here, is a preliminary draft dated December 2025. Treat its AI response examples as planning considerations—not final requirements or a universal compliance checklist.
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Define the scope and name decision-makers
Write down which systems, processes, data, environments, and suppliers are covered. Include internally developed models and externally hosted AI services whenever they can affect organizational data, operations, or decisions. Scope should reflect business consequences, not just whether a tool is branded as AI.
Set out responsibilities and authorities in the plan. Adapt these roles to your organization; one person may hold several roles, but each responsibility should have a named owner and backup.
- Incident commander: Coordinates response and keeps decisions, actions, and handoffs recorded.
- Security lead: Directs investigation, evidence preservation, and technical containment.
- AI system owner: Explains the system’s purpose, configuration, dependencies, and safe operating options.
- IT or operations lead: Manages service isolation, infrastructure changes, and continuity or fallback processes.
- Privacy and legal contacts: Assess data handling, contractual duties, and applicable notification requirements.
- Communications lead: Coordinates approved messages to employees, customers, partners, or other audiences.
- Provider contacts: Connect the organization with model hosts, cloud services, data providers, and other suppliers.
State who may declare an incident and who can revoke access, disconnect an integration, isolate a service, or shut down an asset. Define approval paths for actions that could disrupt a critical process. NIST’s general incident-response policy guidance calls for clear scope, roles, responsibilities, authorities, severity guidance, and recovery procedures.
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Build an inventory responders can use
Responders need to identify the affected system quickly and understand what depends on it. Keep the inventory accessible during an incident, including when a primary AI service or identity system is unavailable. Record at least the following for each in-scope system:
- System name, owner, business purpose, and criticality.
- Model name or service provider, deployment location, and known model or service versions.
- Interfaces, APIs, tools, plugins, and connected applications.
- Relevant data sources, including sensitive data categories where known.
- Upstream and downstream systems, operational dependencies, and fallback process.
- Logging locations and the person authorized to retrieve records.
- Provider contacts, support route, contract reference, and relevant service commitments.
This is an implementation approach, not a NIST-prescribed inventory format. NIST IR 8596’s preliminary draft supports tracking AI providers and system-specific evidence during response. Keep the inventory current when a provider, model, integration, data source, or business use changes.
Decide what counts as an AI-related incident
Use your organization’s severity and escalation scheme, but define AI-related reports clearly enough that staff know where to send them. AI incidents can include ordinary cybersecurity events affecting an AI service as well as reports about model behavior or AI-enabled defenses. These are adaptable examples, not an exhaustive official taxonomy:
- Suspected compromise of a model, AI service account, or connected tool.
- Sensitive information exposed through prompts, outputs, logs, or a provider service.
- Unexpected model behavior that could affect safety, operations, customers, or important decisions.
- Unauthorized changes to model configuration, access, training data, or connected integrations.
- An attack against an AI-enabled security tool, or a defensive action by that tool that disrupts operations.
- Loss of an AI service that interrupts a critical process or removes a required capability.
For each category, define how staff report it, who performs initial triage, what evidence should be preserved, and which conditions trigger escalation. NIST IR 8596’s draft suggests separate categorization and defined triage and validation criteria for AI-related reports, including explainable escalation criteria for AI-enabled attacks.
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Use a consistent triage and impact assessment
Give the first responder a short checklist and a route to technical specialists. The purpose is to establish what happened and how urgent it is—not to prove the incident’s full cause before escalating.
- Validate the report. Record who reported it, when it was noticed, what behavior or alert prompted concern, and what is known versus unconfirmed.
- Identify affected systems and people. Use the inventory to locate the model or service, connected components, users, and business processes involved.
- Assess data and operational exposure. Determine whether sensitive data may have been submitted, returned, stored, or disclosed, and whether critical operations are affected.
- Estimate scope and duration. Establish the earliest known time, the period of possible exposure or unavailability, and whether the activity is ongoing.
- Preserve relevant records. Capture available evidence before routine retention or system changes make it harder to reconstruct events.
- Escalate using defined criteria. Notify the incident commander and relevant system, security, privacy, legal, or operations owners according to severity.
When estimating magnitude, consider model integrity, exposure of sensitive data, and the duration of model unavailability. These factors are highlighted in NIST IR 8596’s preliminary profile; combine them with your organization’s existing assessment of business impact and severity.
Preserve evidence without collecting indiscriminately
Identify in advance which records each system can provide, where they are stored, how long they are retained, and who can retrieve them. During an incident, preserve relevant records promptly, document collection and access, and protect their integrity. Collect only what is appropriate under your privacy, security, and retention obligations.
Depending on the system and incident, useful records may include:
- Prompts, inputs, outputs, and relevant inference records.
- Model and service versions, configuration, and provenance data.
- Authentication, access, API, and model logs.
- Changes to data sources, permissions, integrations, or system settings.
- Provider notices, support communications, and service status information.
- A timeline of response decisions, containment actions, and recovery steps.
NIST IR 8596’s preliminary draft specifically identifies model logs, inference tables, and provenance data as potentially useful analysis artifacts. Their availability and suitability vary by system, so do not assume a provider retains every record responders may need.
Contain the incident with clear authority
Prepare playbooks for plausible events, and decide in advance who can choose among containment options. The least disruptive action is not always sufficient; the fastest action is not always safe for a critical process. Document the decision-maker, technical owner, expected operational impact, and any required approval for each option.
- Isolate the affected application or restrict access to it.
- Revoke exposed credentials, tokens, or service accounts.
- Disable a tool, plugin, API, or other integration involved in the event.
- Switch to a documented manual or non-AI fallback process.
- Disable or roll back an AI component if that is appropriate to the incident.
Specify how responders will confirm whether containment worked and what evidence they should capture before making changes. For actions that could interrupt essential operations, identify an authorized approver and the continuity owner. NIST’s general guidance emphasizes defined authority and prioritization; disabling or rolling back an AI module is a consideration in the preliminary AI profile, not a blanket instruction to do so.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Coordinate with providers before you need them
Maintain current escalation contacts for model hosts, cloud providers, data providers, and other suppliers involved in an AI system. The plan should name who contacts each provider, how urgent reports are made, what information may be shared, and how provider actions will be coordinated with your own containment and recovery work.
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Plan communications and check notification duties
Set an internal escalation path for technical responders, system owners, executives, privacy and legal contacts, and affected business teams. Define who approves external communications and how staff will avoid sharing unverified claims or sensitive incident details.
Keep a separate notification matrix for the organization’s jurisdictions, sectors, data types, contracts, and other commitments. Assign responsibility for determining whether a notice is required, who must receive it, and what deadline applies. There is no universal notification deadline established by the NIST sources discussed here; requirements depend on applicable law, regulation, incident facts, and agreements. NIST’s guidance directs organizations to account for their legal and regulatory context, so have qualified legal or compliance staff assess the situation rather than treating this plan as legal advice.
Recover safely and feed lessons into the program
Define who approves restoration and what must be true before an affected AI component returns to service. Recovery may involve restoring clean configurations or data, correcting access, switching providers, rolling back a component, or considering retraining where appropriate. The right action depends on the cause and the system; retraining is not a default fix for every incident.
Set validation checks suited to the system and its risk. They may include confirming approved versions and permissions, testing relevant behavior, checking integrations, reviewing logs for renewed suspicious activity, and verifying that the business fallback can be retired safely. Record the evidence supporting the recovery decision and any restrictions or heightened monitoring that remain.
After the incident, document what happened, the decisions made, the impact, and what helped or delayed response. Assign owners and due dates for changes to safeguards, detection, provider arrangements, procedures, or staff training. NIST SP 800-61 Rev. 3 places recovery and continuous improvement within the broader incident-response lifecycle.
Exercise and maintain the plan
Exercise procedures periodically, then revise them based on what participants discover. Tabletop scenarios can test different handoffs and decisions without assuming every AI issue is a breach. Include scenarios such as a suspected data leak through an AI service, a compromised provider or model, harmful or unexpected output with business impact, and the loss of an AI-supported critical process.
During each exercise, record who made key decisions, whether contacts and evidence sources were accessible, where approvals stalled, and how the fallback and recovery process worked. Assign each gap to an owner with a due date. Revisit the plan when a material system, provider, business use, or legal or contractual requirement changes, as well as after a real incident or exercise. NIST recommends documenting procedures, testing or exercising them periodically, and using lessons to improve the wider cybersecurity risk-management program.
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