The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →An AI incident response plan should tell your organization how to detect and assess harm, who can make decisions, how to contain an AI system safely, and how to recover and communicate. Build it around your AI systems and their real-world impacts—not a presumed universal definition of an AI incident—and connect it to your cybersecurity process wherever security is involved.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary organizing framework, not a universal legal procedure. Its Manage function calls for documented and monitored risk treatments, incident response, recovery, change management, and communication. NIST says the AI RMF and its Playbook are voluntary. NIST AI Risk Management Framework
1. Define which systems and incidents the plan covers
Start with an inventory of AI systems, including systems built in-house, vendor-hosted models, AI features embedded in other products, and tools employees use. For each system, record the information responders will need to understand its risks and dependencies:
- Accountable owner, intended purpose, and deployment context.
- Supplier, model, data, tools, and service dependencies.
- Users and populations who may be affected, and the kinds of decisions or actions the system can influence.
- Risk assessment, available monitoring, and any existing safety controls or fallback process.
Write an organization-specific starter list of incident triggers. It might include a security compromise, sensitive-data exposure, harmful or discriminatory outputs, unsafe recommendations or actions, significant performance degradation or drift, unauthorized changes to a model or its data, loss of human oversight, or a vendor incident. This is a practical list to tailor, not a formal NIST incident taxonomy.
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Set impact and severity criteria that reflect each system’s purpose and consequences. Define who may declare an incident and who can activate an emergency response. NIST’s framework is use-case agnostic: the organization must determine how context, impacts, and risk tolerance affect its decisions. NIST AI Risk Management Framework NIST AI RMF Core
2. Assign people and decision rights before an incident
Name an incident lead and alternates, then identify the people or teams needed for the systems in scope. Depending on the likely impacts, that may include:
- The AI or system owner and the product or business owner.
- Security response and operations staff.
- Privacy and legal staff.
- A communications lead and a vendor or procurement liaison.
- Domain specialists who understand the system’s effects on users or affected communities.
Document who can pause or disable a system, switch it to a fallback, preserve records, contact a provider, approve restoration, and authorize external communications. Establish an out-of-band contact route in case the incident disrupts the usual communication systems. Train the designated people and make sure decision authority is clear, including executive accountability for significant AI risk decisions.
Include safe decommissioning responsibilities. NIST’s Generative AI Profile also recommends defining ownership of response functions that depend on third parties. NIST AI RMF Core NIST AI 600-1, Generative AI Profile
3. Detect, report, assess, and declare
Do not rely on automated monitoring alone. Possible detection and reporting inputs include system monitoring, security alerts, human review and appeals, quality or safety evaluations, provider notices, and feedback from users or affected communities. NIST’s Manage function calls for post-deployment monitoring that includes input from users and other AI actors.
Use a consistent intake record so responders can establish what happened and decide what to do. Capture, where lawful and available:
- When the issue was observed, who received the report, and the system and version involved.
- The behavior and operational context, including relevant inputs or prompts, outputs, and actions.
- Which users or groups may be affected, and whether harm or unsafe behavior is continuing.
- Suspected involvement of data, a model, a configuration, a supplier, or an integration.
- Potential severity, immediate safety concerns, reversibility, and any uncertainty in the assessment.
Triage for ongoing harm, scope, reversibility, and security or privacy implications. Record assessment decisions and uncertainty rather than treating an early estimate as confirmed fact. Define how the incident lead decides whether to declare an incident and escalate it under the organization’s severity criteria. NIST AI Risk Management Framework
4. Contain the issue without creating a worse one
Match containment to the harm and the service context. Possible actions include pausing a feature, limiting users or permitted actions, routing outputs to human review, reverting a model or configuration, revoking credentials, restricting data access, or disabling the system. Pre-authorize practical controls and specify who can invoke them.
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Stopping an AI system can itself disrupt a critical service or create risk. Decide in advance when to use a risk-approved fallback—such as manual processing or a previously validated alternative—and who is authorized to make that decision. Record the conditions for disengaging or deactivating a system whose performance or outcomes conflict with its intended use. NIST’s Generative AI Profile recommends testing and managing risks associated with rollover and fallback technologies. NIST AI RMF Core NIST AI 600-1, Generative AI Profile
5. Investigate, fix, and restore service
Preserve relevant evidence and decision records under your organization’s retention and privacy rules. Investigate the cause and scope, considering the model version and configuration, connected data, tools and integrations, supplier changes, available logs, and user impacts. Check whether the same component or dependency is used in other systems or deployments.
Correct the root cause or remove compromised components, then validate the fix against the triggering scenario and relevant safety, privacy, and security checks. Before restoration, define approval criteria, how the system will be monitored after it returns, and how any residual risk will be communicated. Include procedures for previously unknown risks, changes to the system, and recovery in the plan.
When the incident has a cybersecurity dimension, use current cybersecurity response guidance alongside the AI-specific plan. NIST finalized SP 800-61 Rev. 3 in April 2025; it supersedes Rev. 2 and integrates incident response throughout the Cybersecurity Framework 2.0. NIST SP 800-61 Rev. 3 NIST AI Risk Management Framework
6. Coordinate vendors and communicate clearly
For systems that depend on an outside provider, document who owns each response function and how to reach the provider during an incident. Review contracts for incident responsibilities, information sharing, and notification terms. Maintain policies for monitoring third-party risks, document vendor-related incidents, and rehearse the response with relevant providers where practical. Test fallback and rollover arrangements rather than assuming they will work under incident conditions. NIST AI 600-1, Generative AI Profile
Prepare internal status channels and audience-specific communication templates for leadership, affected users, relevant AI actors, vendors, and—when warranted—affected communities or other external stakeholders. Communicate what is known, what remains uncertain, what protective action is being taken, how people can report an impact or appeal a decision, and when to expect an update. NIST AI RMF Manage 4.3 states: “Incidents and errors are communicated to relevant AI actors, including affected communities.” NIST AI Risk Management Framework
Have legal and privacy staff assess notification duties against the organization’s locations, sector, affected people, contracts, and system use. Applicable breach-reporting, privacy, data-protection, and other obligations vary; the AI RMF itself is not a legal reporting rule. NIST’s Generative AI Profile recommends reviewing response plans against relevant reporting and privacy laws. NIST AI 600-1, Generative AI Profile
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Rehearse the plan and improve it
Run tabletop exercises based on plausible situations for your systems. For example, practice an unsafe output affecting a customer, sensitive data exposure through an AI workflow, a compromised or changed model dependency, or the failure of an AI-enabled process after shutdown. Include the vendor and service owner when their decisions or systems are part of the response.
Assess whether responders found the right owner, chose containment appropriately, preserved evidence, protected affected people, communicated accurately, and restored service safely. Assign an owner and due date to each improvement. Feed findings into the plan, training, contracts, monitoring, and system-change process. NIST recommends regular rehearsal and retrospective improvement for third-party generative AI response plans; AI RMF Manage 4.2 calls for measurable continual improvement integrated with system updates. NIST AI 600-1, Generative AI Profile NIST AI Risk Management Framework
How to tailor the response choices
There is no single response path that fits every organization or AI system. Use these factors to shape severity criteria, containment authority, fallback arrangements, and communication:
- Impact and reversibility: Consider potential harm to people or operations and whether a response action can be undone.
- Control and dependency: Establish whether your organization controls the model, data, and deployment or depends on a provider.
- Continuity and safety: Compare the consequences of shutdown with continued operation under human review or a fallback.
- Detection and evidence: Determine whether logs, monitoring, appeals, and user reports can reveal the issue’s scope and cause.
- Notification and accountability: Identify who may need communication, subject to applicable law and contractual duties.
- Resources and proportionality: Match response rigor to system risk, organizational capacity, and risk tolerance.
Use current guidance with its limits in mind
NIST AI RMF 1.0 was released on January 26, 2023, for voluntary use, and NIST says the framework is being revised. NIST published its Generative AI Profile, AI 600-1, on July 26, 2024. Its recommendations add practical guidance for third-party generative AI incidents, provider dependencies, fallback, contracts, and rehearsal. The framework is U.S. federal standards-body guidance, but its voluntary, use-case-agnostic approach does not replace legal or sector-specific analysis. NIST AI Risk Management Framework NIST AI 600-1, Generative AI Profile
On April 7, 2026, NIST released a concept note for an AI RMF profile on trustworthy AI in critical infrastructure. It is a concept note, not a finalized sector profile. NIST AI Risk Management Framework
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