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Build an AI-enabled cyberattack response plan around the same incident-management lifecycle used for other cyber incidents, but explicitly include models, prompts, data, agents, tools, identities, suppliers, and autonomous actions. NIST SP 800-61 Revision 3 is the current incident-response foundation; it superseded Revision 2 in April 2025 and connects response activities to the NIST Cybersecurity Framework 2.0. The plan should tell named people how to detect, assess, contain, recover from, and learn from an incident—not just describe policy.
The practical test is whether responders can safely stop a compromised agent, preserve useful evidence, limit access and spread, restore trusted services, and coordinate decisions under pressure. Use the framework below to build a plan around your organization’s systems and rehearse it before an incident.
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What should an AI incident response plan cover?
It should cover the AI system as a connected workflow, not just the model. A prompt-injection event, for example, may involve an application, retrieved documents, a user identity, an agent, a tool, and a downstream data store. If those dependencies are missing from the plan, responders may stop the model while leaving the compromised access path active.
NIST SP 800-61 Rev. 3 is designed to incorporate incident-response recommendations throughout risk management as described by CSF 2.0. For an organization, that means response is not a standalone document: it depends on preparation, governance, protection, detection, response, and recovery practices being operational before an alert arrives.
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Include these assets and dependencies
- Models, model versions, endpoints, hosting environments, and associated code or configuration.
- Agents, orchestration layers, prompts, system instructions, retrieval pipelines, and memory stores.
- Training, fine-tuning, evaluation, and retrieval data, along with their storage and access paths.
- Tools and integrations an AI system can invoke, such as APIs, browsers, code execution, databases, and business applications.
- Human and service identities, credentials, keys, permissions, cloud services, suppliers, and external model providers.
- Business processes and safety-critical actions that depend on AI outputs or autonomous decisions.
Maintain an inventory and dependency map that connects each system to its business owner, technical owner, data, tools, identities, supplier contacts, and recovery requirements. Update it when a model, agent, permission, tool, or supplier changes; a stale map can make containment slower or incomplete.
Who owns decisions during an AI cyber incident?
Assign both operational roles and decision rights in advance. CISA says organizations should maintain clear, executable cyber incident response plans and strategies; that requires naming the people authorized to act, not relying on an assumed chain of command.
| Role | Primary responsibility to define |
|---|---|
| Executive incident owner | Sets business priorities, accepts or escalates risk, and resolves resource or continuity decisions. |
| Incident commander | Coordinates response, maintains the shared incident picture, assigns actions, and tracks decisions. |
| Security operations lead | Leads detection, triage, evidence handling, and technical coordination. |
| AI or model owner | Explains system behavior and dependencies; validates model, prompt, data, and tool integrity. |
| IT and cloud leads | Execute identity, workload, network, logging, backup, and recovery actions. |
| Legal and privacy counsel | Advises on evidence handling, contractual duties, privacy, and jurisdiction-specific reporting. |
| Communications lead | Coordinates approved internal, customer, supplier, regulator, and public messaging. |
| Business-continuity lead | Coordinates workarounds and restoration of affected business processes. |
| Supplier contacts | Provide escalation routes for hosted models, cloud services, integrations, and other dependencies. |
Write down who can pause or disable an agent, revoke credentials, isolate a workload, approve restoration, and authorize external notifications. Specify an alternate decision-maker for each critical role and a way to reach them if normal communications are affected. Define who may approve public statements and authority notifications; incident responders should not have to infer that authority during an active event.
Which AI attack scenarios should the plan prepare for?
Prepare short scenario cards that describe observable signals, affected assets, first actions, escalation conditions, and recovery checks. Use MITRE ATLAS and OWASP’s GenAI threat taxonomies as inputs to threat modeling, then tailor the scenarios to the systems and consequences your organization actually has.
| Scenario | What responders need to establish |
|---|---|
| Prompt injection | Whether untrusted prompt content influenced an agent, what context it could access, and whether it invoked tools or exposed data. |
| Training-data or retrieval-data poisoning | Which data, model versions, indexes, or downstream outputs may be affected, and how to validate trusted inputs. |
| Compromised model or agent | Whether the model, agent configuration, orchestration code, or execution environment was altered or misused. |
| Unauthorized tool use or privilege escalation | Which identity and permissions enabled the action, what tool calls occurred, and whether connected systems were reached. |
| Credential theft or data exfiltration | Which sessions, keys, identities, data stores, and recipients were involved; whether access remains active. |
| Ransomware or destructive action | Which workloads or data were changed, whether the threat is still executing, and what trusted recovery points remain. |
| Supply-chain compromise | Whether a supplier, hosted model, dependency, or integration is implicated and which other systems rely on it. |
| AI-assisted exploitation of internet-facing systems | Whether exposed services were probed or exploited and whether ordinary intrusion-response procedures must be activated alongside AI-specific steps. |
Score severity using safety, confidentiality, integrity, availability, business impact, regulatory exposure, and the potential spread across connected agents and tools. Define thresholds for escalation and executive involvement. A low-impact anomaly confined to a test environment should not automatically receive the same response as an agent with production write access; conversely, an uncertain event with safety or broad-access implications may warrant prompt containment while facts are gathered.
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How do you prepare before an attack?
Preparation combines safeguards with response readiness. CISA’s international guidance emphasizes limiting agent autonomy, layered defenses, strong identity management, oversight, threat modeling, continuous monitoring, and regular security assessments. Australian Signals Directorate and ACSC guidance adds prompt-log analysis, protected and tested backups, and exercised continuity and disaster-recovery plans.
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- Give agents only the permissions and tools required for their assigned tasks. Separate read, write, and administrative privileges where the workflow allows it.
- Segment AI workloads and their data or tools from unrelated systems so that one compromised component cannot automatically reach everything.
- Set secure configuration baselines for models, agents, integrations, identities, and cloud workloads, and document who approves changes.
- Use strong identity and key management. Know how to revoke a service identity or session and rotate keys without relying on the suspected agent.
- Limit autonomy according to potential impact. Require human review for actions that could create significant or difficult-to-reverse consequences.
Make the system observable
Centralize and protect logs so responders can correlate AI activity with ordinary security telemetry. Establish what is recorded, who can access it, how it is protected from unauthorized alteration, and how it can be retrieved during an incident. The exact logging design depends on the system, but should make it possible to reconstruct relevant activity across prompts, tools, identities, infrastructure, and data.
Make recovery credible
Keep recent backups protected against unauthorized access, modification, and deletion, and test restoration rather than assuming backups will work. Exercise continuity and disaster-recovery procedures for the business processes that rely on the AI system. Record trusted versions or baselines for models, prompts, code, configuration, and relevant data so a recovery team has a way to check what it is restoring.
Pre-authorize limited response automation
Automation can shorten response time, but a broad automated shutdown can disrupt legitimate work or destroy useful evidence. Pre-approve only narrowly scoped, reversible actions, such as pausing a specific agent or disabling a specific tool integration under defined conditions. State who may invoke each action, what evidence it preserves, how responders verify the result, and how to reverse it safely. Do not let an unverified AI-generated alert make an unreviewed high-impact decision.
How should responders detect, analyze, and preserve evidence?
Correlate model and agent activity with identity, endpoint, network, cloud, and data-access telemetry. Treat surprising autonomous behavior as a signal to investigate, not as proof of an attack: a human should validate the event, its context, and its impact before assigning a cause or severity.
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Use a consistent triage sequence
- Validate the alert. Identify the affected system and business process, check whether the activity is reproducible or expected, and confirm the time window using synchronized timestamps.
- Map the path. Identify the model and version, agent configuration, prompt or retrieved context, identity, tools, integrations, and data stores involved.
- Assess impact and spread. Check for unauthorized tool calls, privilege changes, data access or movement, altered artifacts, destructive actions, and activity in connected systems.
- Set severity and escalation. Apply the documented criteria for safety, confidentiality, integrity, availability, business impact, regulatory exposure, and propagation. Escalate uncertainty when the plausible harm is significant.
- Choose containment with the incident commander. Use the approved action for the scenario, preserve evidence where feasible, and assign an owner to verify that containment worked.
Preserve enough context to reconstruct events
Preserve relevant prompts, model versions and hashes, retrieved context, tool outputs, policy decisions, identity activity, and application, cloud, endpoint, network, and data-access logs. Record the incident timeline, the source of each item, when it was collected, who handled it, and any steps used to preserve it. This supports investigation and helps distinguish a model failure from malicious use or a compromised dependency.
Do not assume that a model’s explanation of its own behavior is a reliable forensic record. Compare it with system logs and independently captured tool and identity events. NIST AI profile material highlights investigation and analysis, anomaly detection, adversarial simulation, automated response, and incident reporting as relevant areas; the practical response still needs evidence that can be reviewed by people.
What should you do if an AI agent is compromised?
First stop the agent’s ability to cause additional harm, then revoke the access it could use and preserve the evidence needed to understand how it was compromised. The exact order depends on immediate risk and whether a containment action could erase evidence, so follow the pre-approved playbook and coordinate with the incident commander.
- Pause or quarantine the affected agent. Use the narrowest available control that prevents further actions, and verify that queued jobs, retries, or alternate instances are not still running.
- Revoke sessions and rotate exposed credentials or keys. Disable or replace affected service identities, tokens, and secrets. Check for other identities or tools the agent could access.
- Disable risky tools and isolate affected workloads. Restrict the agent’s network paths or tool integrations as needed, while preserving a controlled channel for investigation.
- Preserve forensic evidence. Capture relevant logs, configuration, model and prompt versions, tool outputs, and workload state before destructive cleanup where feasible.
- Determine the entry point and persistence. Investigate prompt or data manipulation, compromised credentials, software or supplier changes, and exploited services; remove unauthorized changes and close the exploited path.
- Validate integrity before re-enabling. Check the model, data, code, prompts, configuration, tools, and identities against trusted baselines or known-good sources.
- Restore and reconnect in stages. Restore from recent tested protected backups when needed, monitor activity as components return, and confirm that the original behavior does not recur.
If the agent has access to safety-critical or high-impact systems, containment should prioritize preventing harmful actions even when that means temporarily interrupting the business process. The continuity plan should identify a human or non-AI fallback for that process.
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Contain the affected workflow, determine what untrusted input reached it, and establish whether the event changed outputs, tool actions, stored data, or model artifacts. A suspicious prompt or unusual output alone does not establish that training data or a model was poisoned; investigate the path and verify integrity before making that conclusion.
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For prompt injection
Identify the user input, retrieved documents, external content, and system context that the model or agent processed. Review the associated tool calls and access logs to determine whether the injected content induced an action or exposed information. Restrict or pause the affected agent and relevant tools if the workflow can still act on malicious instructions. Preserve the prompt and retrieved context under the organization’s evidence-handling rules, then correct the source or control weakness before restoring the workflow.
For suspected poisoning
Identify the data or artifact suspected of manipulation: training or fine-tuning data, a retrieval corpus, an index, a model version, or a deployment configuration. Trace provenance and changes, compare relevant artifacts with trusted copies, and assess which outputs or downstream decisions may have been affected. Quarantine suspect data or versions, prevent their reuse, and restore or rebuild from validated sources. Monitor the repaired system for recurrence rather than treating a successful rebuild as proof that the original entry path is closed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should recovery and communications work?
Recovery is complete only when the service is functioning from validated components and responders have checked for recurrence. A system that is back online but still has compromised credentials, poisoned inputs, or an exposed integration is not recovered.
Restore in a controlled sequence
- Remove persistence, patch exploited services, and close the access path identified during analysis.
- Validate the integrity of models, data, code, prompts, configuration, tools, and identities needed for service.
- Restore from recent, tested, protected backups when required, and verify the restored data and service behavior.
- Reconnect components in stages, starting with the minimum dependencies needed to validate the workflow.
- Monitor for repeated anomalous prompts, tool calls, identity activity, or data access; keep an owner responsible for deciding when heightened monitoring can end.
- Return the business process to normal operation only after technical and business owners accept the remaining risk and continuity arrangements are adjusted.
Plan communications before they are needed
Define internal escalation, customer and supplier notices, regulator and law-enforcement reporting, public communications, and approvals for sharing evidence. Legal and privacy counsel should determine applicable duties based on the incident, contracts, and jurisdictions involved; the plan should not assume one universal notification deadline or threshold.
Identify what information may be shared, who can approve it, and how to coordinate with the affected supplier. CISA’s Joint Cyber Defense Collaborative AI playbook describes voluntary sharing of AI incident and vulnerability information among government, industry, and international partners. Include an appropriate information-sharing route in the plan, with review for confidentiality and legal constraints.
How do you test and improve the plan?
Run both discussion-based tabletops and technical exercises. A tabletop tests decisions and coordination; a technical exercise tests whether controls such as agent shutdown, credential rotation, isolation, logging, and restoration actually work. Australian guidance calls for preparing and exercising response plans, including restoration from recent, tested backups protected from unauthorized access, modification, and deletion.
Exercise three distinct situations
- An agent compromise that tests shutdown authority, identity revocation, tool restrictions, and the human fallback for affected work.
- A prompt-injection or data-poisoning event that tests investigation of inputs and artifacts, evidence preservation, integrity validation, and recovery.
- An AI-assisted conventional intrusion against an internet-facing system that tests whether ordinary security response and AI-specific dependencies are handled together.
Make the scenario realistic for your architecture and include security, AI or model owners, IT and cloud teams, executives, legal and privacy, communications, business continuity, and relevant suppliers. Inject decisions about customer or supplier notices and public communications, not only technical clues. Australian guidance notes qualitatively that AI can help malicious actors discover vulnerabilities and develop exploits within hours; it does not establish a numeric attack-rate benchmark, so use that point as a reason to exercise promptly rather than as a response-time target.
Measure what the exercise reveals
Track time to detect, time to human decision, time to contain, restoration time, evidence completeness, approval latency, false positives, and recurrence. Also record whether responders found the right contacts, could access protected logs, knew how to revoke credentials, could pause the correct agent, and could restore a trusted service. The metrics are diagnostic: use them to find bottlenecks and improve the plan, not as unsupported universal performance targets.
After every exercise or incident, assign an owner and due date to each finding. Update scenario cards, system inventories, permissions, logging, automation approvals, continuity procedures, staff training, and threat models where the evidence warrants it. A finding is not closed merely because it appears in an after-action report; verify the corrective action in a later exercise or operational check.
Frequently Asked Questions
Does every unusual AI output count as a cybersecurity incident?
No. An unexpected answer can be a product defect, a normal limitation, or a security event; the distinction depends on evidence of unauthorized access, manipulation, or harmful action. Triage the input, context, identity, and system events before classifying it.
Should incident responders send prompts to a public AI service for analysis?
Not without checking the organization’s data-handling and evidence rules. Prompts and retrieved context may contain sensitive information, and moving them to an external service can create a separate disclosure risk.
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Who decides whether an AI incident must be reported to regulators?
The organization should route that decision through its designated legal and privacy counsel, using the applicable jurisdictions, contracts, and facts of the incident. Security teams should promptly provide the evidence and timeline counsel needs rather than make a blanket reporting decision themselves.
Can an AI system run the incident response by itself?
It can assist with triage or execute pre-approved low-risk actions, but high-impact containment, external communications, and recovery decisions should remain under named human authority. Automation should have clear limits, auditability, and a way to verify and reverse its actions.
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