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Use NIST’s current incident-response framework
NIST finalized SP 800-61 Rev. 3 on April 3, 2025. It supersedes Rev. 2 and integrates incident-response recommendations with the NIST Cybersecurity Framework (CSF) 2.0 and broader cybersecurity risk management. NIST describes response as an organization-wide capability, not only a technical playbook.
The framework’s six CSF 2.0 functions give the plan a useful structure. Govern, Identify, and Protect support preparedness; Detect, Respond, and Recover describe the main operational response lifecycle. NIST also emphasizes using lessons from all functions for continuous improvement. The right procedures depend on the organization, its technology, and its operating environment, so use the framework as a baseline rather than a universal checklist. See NIST’s incident-response project page and SP 800-61 Rev. 3 planning guidance.
Build the plan in seven steps
1. Set scope, objectives, and decision authority
State which business services, AI systems, environments, and third-party services the plan covers. Name an executive sponsor and identify the people or teams who will lead response and provide specialist input.
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- Incident leadership: the incident commander or equivalent, security lead, and executive decision-maker.
- AI and technology owners: model or AI service owner, IT and cloud operators, application owners, and relevant service providers.
- Business and advisory roles: legal, privacy, communications, business continuity, and affected business units.
- External parties: providers, incident-response or forensic specialists, and other parties the organization may need to engage.
Specify who can declare an incident, suspend an agent, isolate a service, revoke credentials, preserve evidence, decide on notifications, and approve restoration. Include the resources and management support needed to carry out those actions. NIST advises tailoring plans to an organization’s mission, size, structure, and functions; it does not prescribe the same staffing model for every organization.
2. Map AI systems and dependencies
Create an inventory that responders can use to understand what is affected and who can act. For each AI service, record its owner, purpose, model and version, data sources, retrieval stores, prompts and configuration, APIs, hosting provider, logging locations, connected systems, tool permissions, and dependent business processes. Record normal behavior and important dependencies where practical.
This inventory is a planning choice, not a universal NIST-mandated format. Its value is operational: it helps responders investigate whether an incident involves a model, manipulated input, compromised account or infrastructure, connected tool, or downstream service. Include both AI you operate and third-party AI services your organization relies on.
3. Define alert routes and triage criteria
Explain how employees, customers, vendors, and automated monitoring can report a suspected incident, and who receives each report. Give responders a consistent set of questions: what system or business service is affected, what happened and when, what evidence is available, what data or actions may be involved, and who needs to be notified internally.
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Classify events in terms responders can investigate, such as account or software compromise; malicious direct or retrieved input; suspected data or model-integrity problems; sensitive-data disclosure or extraction; harmful agent actions; and synthetic-media impersonation. Assess potential effects on confidentiality, integrity, availability, safety, legal duties, and business operations. NIST identifies relevant AI threat categories, but your organization must set its own severity thresholds, escalation rules, and notification decisions.
4. Preserve evidence before it disappears
Write down who is responsible for collecting and safeguarding evidence, where it is stored, and how responders will record relevant times and actions. Depending on the system and incident, useful evidence may include:
- Alerts, timestamps, identity and access records, network and application logs.
- Prompts, retrieved content, model and system versions, configuration, and deployment changes.
- Tool calls, agent action history, affected data and artifacts, and relevant communications.
- System or data snapshots, when capturing them is feasible and safe before changes are made.
Choose evidence sources for the AI service and its dependencies rather than assuming that a model transcript alone will explain an incident. The sources cited here do not establish one forensic procedure that fits every AI system; define collection and preservation steps with the system owner and relevant security or forensic specialists.
5. Pre-authorize proportionate containment options
Identify who may take each containment action and what operational or safety checks are needed first. Depending on the incident, options may include disabling an integration, narrowing an agent’s permissions, blocking a malicious source, revoking credentials, isolating a service, pausing a model deployment or data pipeline, or switching to a manual or alternate workflow.
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Set decision points for actions that could disrupt a critical service or create safety risks. For connected AI systems, consider not only the model but also the tools, credentials, data sources, and downstream systems it can reach. A system with permission to take consequential actions may require a different containment decision from one that only provides informational output.
6. Coordinate continuity and communications
Align response procedures with business continuity. Define which alternative workflows are available, which services take restoration priority, and who decides whether to notify employees, customers, partners, regulators, or other affected parties. Identify approved internal and external communication channels, including a fallback if normal channels are unavailable.
For a suspected synthetic-media impersonation involving money, access, or a sensitive change, require verification through a known trusted channel and an escalation route that does not rely on the suspect message or media. Preserve the original message and media where possible. Joint NSA, FBI, and CISA guidance addresses organizational preparation and response to deepfake threats; the CISA page is marked archived: Deepfake threats cybersecurity information sheet.
7. Define recovery and improvement criteria
Before an incident occurs, decide what must be verified before service restoration: for example, data and model integrity, configuration, access controls, credentials, integrations, and monitoring. Assign the people who can approve restoration and communicate service status. After an incident, document lessons, assign owners to improvement actions, and update relevant plans, inventories, controls, and exercises.
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NIST’s approach treats improvement as a way to feed lessons from incident response across the cybersecurity program, rather than as a final report that has no effect on future preparation.
Adapt the plan to the AI systems you actually use
Response decisions differ according to how AI fits into the organization. Use these questions when setting scope, priorities, evidence requirements, and containment authority:
| Planning dimension | What to establish | Why it changes the response |
|---|---|---|
| AI relationship | Whether the organization operates a model or service, consumes a third-party service, or does both; name the responsible owner and provider contact. | Control over logs, model changes, evidence, and containment may be divided between the organization and a provider. |
| Tool access and autonomy | Which APIs, accounts, tools, and systems an AI service can access, and which actions it can take. | Containment may need to address permissions and connected services, not just pause model output. |
| Data sensitivity and provenance | What data the system can access, where it came from, and how its integrity is checked. | Suspected disclosure, extraction, or poisoning may require different investigation and notification decisions. |
| Service criticality | Business impact of disruption, acceptable downtime, and viable manual or alternate processes. | Isolation or shutdown may reduce exposure but also interrupt an important service. |
| Internal response capacity | Which tasks internal teams can perform and where provider or external response support is needed. | Evidence access, investigation, and recovery may depend on capabilities outside the organization. |
These are planning dimensions, not a product comparison or prescribed NIST scoring system. Organizations with limited internal incident-response capacity can consider whether to arrange external incident-response or digital-forensics support in advance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Exercise AI-specific incident scenarios
Use scenario exercises to check whether the plan works across security, AI ownership, business continuity, legal and privacy, communications, and executive decision-making. For each exercise, test who receives the alert, what evidence is available, who can authorize containment, what business service is affected, and what must be true before recovery. NIST and government sources describe the following scenarios; they are not an exhaustive threat taxonomy.
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Prompt injection, including indirect injection
Exercise a case in which malicious instructions in a direct prompt or retrieved content cause unintended behavior or affect a connected system. Check what content was retrieved, what permissions the system had, which actions it took, and whether information was exposed. NIST’s generative AI profile discusses these risks: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile.
Data or model poisoning
Test how the organization would assess the integrity and provenance of training, tuning, or retrieval inputs, investigate changes in model behavior or output, and decide whether affected data or models can be trusted. NIST’s adversarial machine-learning taxonomy describes attack and mitigation terminology: NIST announcement on its adversarial machine-learning report.
Privacy attack, extraction, or misuse
Exercise how responders would investigate suspected sensitive-data disclosure, inference, extraction, or abuse; identify potentially affected data; and involve the people responsible for legal, privacy, and notification decisions. Do not assume an output log alone establishes what data may have been exposed.
Agent action without an obvious adversarial prompt
Test a scenario in which an agent takes a harmful action through specification gaming or misaligned objectives, even when responders cannot identify a clear malicious prompt. Review its permissions, action history, and available controls. NIST’s 2026 work on securing AI agent systems notes that conventional cybersecurity practices remain relevant but may need adaptation: CAISI request for information about securing AI agent systems and NIST summary analysis of responses.
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Synthetic-media impersonation
Exercise an urgent request delivered through suspected synthetic audio or video. Test whether staff use an independently known trusted channel to verify high-impact instructions, preserve original messages and media, escalate concerns, and coordinate communications without amplifying unverified claims.
Keep the plan usable as systems change
Assign an owner to maintain the plan and the AI inventory, and update them when systems, providers, data flows, permissions, or business dependencies change. Review exercise and incident outcomes for unclear authority, missing evidence, inaccessible provider contacts, weak continuity options, or restoration criteria that cannot be verified. Record corrective actions with owners and follow through so the plan reflects how the organization can actually respond.
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