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Build your AI incident-response plan around the lifecycle in NIST SP 800-61 Rev. 3, then add procedures for AI-specific failure and attack modes: prompt injection, poisoned retrieval or training data, sensitive-data leakage, tampered models, compromised providers, model theft, and agents taking unauthorized actions. Before an incident, assign owners, severity thresholds, shutdown paths, evidence rules, and notification contacts. During one, preserve the AI interaction and its surrounding system evidence, establish the blast radius, contain affected identities and components, and verify recovery with adversarial testing.
The plan should cover AI systems you develop and those you obtain from providers, including the agents, data stores, plugins, connectors, and credentials that let them act. Treat AI incidents as security incidents with additional evidence and containment needs—not as a separate process that bypasses your existing incident-response team.
What should an AI incident-response plan cover?
An AI incident-response plan explains how your organization prepares for, detects, contains, removes, and recovers from attacks or compromise involving AI systems. It should connect to your existing security incident process, while adding AI-specific scenarios, evidence sources, owners, and containment actions.
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Use the plan to answer these questions quickly:
- Which AI components and business processes are affected, and who owns each one?
- What evidence must be preserved before a model, index, identity, or workload is changed?
- Who can disable an agent or revoke its access, and how can they do it safely?
- How will the team determine whether data was exposed, altered, or used for unauthorized actions?
- Who decides whether to notify customers, regulators, providers, law enforcement, or information-sharing partners?
- What tests must pass before a system returns to service?
Keep the main incident plan authoritative for roles, command structure, and organizational escalation. The AI annex should supply the technical decision points and evidence checklist that responders would otherwise have to improvise.
Which AI attack scenarios should the plan address?
Include attacks against the model, the data around it, and the privileges it can exercise. A useful scenario list maps each technique to its business impact, the evidence that could reveal it, and the people authorized to contain it.
| Scenario | What may be at risk | Plan for |
|---|---|---|
| Prompt injection or jailbreak | Confidential context, policy enforcement, or downstream tools | Preserve the full conversation and instructions; identify whether the model disclosed data or invoked tools; restrict the affected path without destroying evidence. |
| RAG or training-data poisoning | Answer integrity, decisions, or information retrieved for users | Identify affected documents, index snapshots, ingestion jobs, and model or retrieval versions; quarantine suspect content and validate rebuilt data. |
| Sensitive-data leakage | Personal, regulated, proprietary, or credential data in prompts, outputs, or logs | Determine what information left the intended boundary, who could access it, and whether logs or provider systems also contain it; involve privacy and legal owners. |
| Malicious or tampered model | System behavior, output integrity, or application security | Compare model identity and artifacts with approved records; isolate the deployment and retain relevant hashes, configuration, and access events. |
| Supply-chain or provider compromise | Hosted model services, dependencies, plugins, connectors, or update channels | Identify the affected dependency and provider notices; determine which systems and data rely on it; prepare a controlled disablement or fallback path. |
| Model theft or LLM jacking | Model intellectual property, compute, service availability, or cloud spend | Review access, usage, network, and billing-related alerts available to your organization; revoke unauthorized access and assess whether model assets or endpoints were exposed. |
| Agent privilege abuse or compromise | Actions taken through email, code, files, business systems, or other tools | Revoke or reduce the agent’s identity privileges, disable affected tools, inspect action history, and identify downstream changes that need reversal. |
| AI-assisted phishing or social engineering | Employee accounts, approvals, payment workflows, or sensitive information | Route the event through the normal phishing or account-compromise process, while recording any AI-related indicators that matter to detection or intelligence sharing. |
MITRE ATLAS’s Spring 2025 update includes techniques and case studies involving RAG poisoning, false RAG-entry injection, retrieval-content crafting, prompt crafting and obfuscation, trusted-output manipulation, model tampering, LLM jacking, and malicious models. These labels can help teams make scenarios concrete, but a technique name alone is not a response procedure: connect it to your own systems, access paths, and containment decisions.
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How do you inventory AI systems and define ownership?
Start with an inventory that reveals both the AI components and the routes through which they can affect people, systems, or data. An incomplete inventory makes it difficult to establish blast radius or find the person who can shut down a compromised capability.
Record each system and dependency
For each AI use case, record the model and provider, deployment environment, business owner, technical owner, and the applications that call it. Add the agents, retrieval-augmented generation (RAG) stores, training or fine-tuning data, plugins, tools, connectors, identities, and third-party dependencies involved.
Record data classes and regions as well. A responder needs to know whether the system handles personal, regulated, confidential, or public information and where relevant processing or storage occurs. Include the model, application, system and developer instructions, guardrails, retrieval configuration, and deployment configuration as versioned components where your environment supports it.
Document emergency controls
For every component, specify the person or role authorized to disable it, the exact control available, and the expected effect. That may mean disabling a connector rather than taking an entire application offline, or suspending an agent identity while leaving read-only access available to users.
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Document fallback behavior too. A shutdown path that silently routes users to an unreviewed model or data source is not a safe containment plan. State whether the business process should pause, switch to a tested alternative, or continue with human handling.
How should you set severity and escalation rules?
Set severity using business impact and exposure, not an AI-specific score alone. Your incident lead should be able to distinguish a suspicious but contained prompt from a confirmed disclosure, unauthorized action, or compromise affecting multiple systems.
Define thresholds in your own policy. The following are decision criteria to adapt, not universal severity ratings:
- Potential exposure: suspicious prompt or output, with no evidence yet of sensitive data disclosure or consequential tool action.
- Confirmed limited impact: evidence of unauthorized disclosure, data alteration, or tool use limited to a known user, dataset, or workload.
- Broad or high-consequence impact: evidence of compromise across identities, systems, customers, sensitive data, or critical business processes—or an inability to bound the impact.
For each threshold, define who can declare an incident, who leads technical response, and when privacy, legal, communications, business continuity, and executive owners join. Give responders a way to escalate uncertainty: if logs are incomplete and the team cannot rule out a material impact, the plan should specify who makes the precautionary decision.
Assign named roles rather than relying on a generic “AI team.” At minimum, identify the incident commander, security operations lead, AI or application owner, identity and infrastructure owners, privacy and legal contacts, communications owner, and provider contact. Provide an on-call route and an alternate for each role.
What should you prepare before an incident?
Preparation turns policy into actions that can be performed under pressure. It includes access to containment controls, evidence procedures, notification decisions, and exercises—not just a written playbook.
Set evidence, legal, and privacy rules
Decide in advance which logs are retained, for how long, who may access them, and how sensitive conversation content is protected. Coordinate the rules with legal, privacy, and records owners because prompts, outputs, and retrieved material can contain personal or confidential information. Define how responders preserve evidence and document custody when an investigation requires it.
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Prepare contacts and communications
Keep current contact routes for AI providers, cloud or infrastructure providers, internal counsel, privacy teams, affected business owners, and approved information-sharing partners. Prepare communication templates for internal escalation and potential external notifications, but do not treat a template as a decision to notify. The incident team must assess applicable obligations and facts with the appropriate legal and privacy owners.
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Exercise realistic cases
Run tabletop exercises that test both technical response and decision-making. For example, ask whether the team can preserve an injected conversation, identify the affected retrieval index, disable an agent’s connector, determine whether data reached an external destination, and agree who authorizes restoration.
Include provider unavailability, incomplete logs, and an action taken by an agent before detection. Australian Signals Directorate/ACSC guidance on careful adoption of agentic AI services (2025) recommends threat modeling, controls for emerging agent capabilities, tested response procedures, third-party reviews, runtime authentication, and clear governance and accountability.
What should you monitor and log to investigate an AI incident?
Collect enough context to reconstruct what the system received, what it used, what it returned, and what it did. A model’s final answer alone is rarely enough to explain whether an attack succeeded or to establish the full blast radius.
- Interaction: original prompt, conversation context, model response, and relevant system and developer instructions.
- Model and configuration: provider and model identity, model version, deployment or configuration changes, and policy or guardrail decisions.
- Retrieval: retrieved documents, their identifiers or content needed for investigation, ranking or selection details, and index or retrieval version.
- Actions: tool calls and parameters, connector use, approvals, agent identity, and resulting system changes.
- Identity and access: user and service-account events, privilege changes, authentication events, and authorization decisions.
- Data movement: relevant network destinations and evidence of information entering or leaving the system boundary.
- Integrity and time: input and output hashes where appropriate, timestamps with timezone, and chain-of-custody records for preserved evidence.
Correlate AI telemetry with existing SIEM/SOAR data, identity logs, application logs, network events, and provider alerts. CISA and partners’ 2025 guidance for SIEM and SOAR implementation recommends prioritized log ingestion and predefined automated response actions to improve detection and response speed. Prioritize signals that let responders identify affected identities, data paths, model or retrieval changes, and actions with real-world consequences.
Logging has a privacy and security cost. Restrict access to sensitive prompts and retrieved content, protect logs against alteration, and apply retention rules approved by the relevant legal and privacy owners. If a provider does not expose a particular event or artifact, document that visibility limit and decide whether the use case remains acceptable with compensating controls.
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How should responders triage and contain an AI incident?
Use a consistent sequence—triage, activate, analyze, assess, report—while preserving evidence before making destructive changes. Containment should reduce risk without erasing the information needed to understand what happened.
- Triage the signal. Record the alert, reporter, time, affected use case, and immediate safety or business concern. Check for a suspicious prompt, unexpected output, anomalous tool call, identity event, provider notice, or unexpected model or retrieval change.
- Activate the right response. Apply your severity criteria, name the incident commander, and involve the AI owner and relevant security, privacy, legal, and business contacts. Start a timeline and keep decisions and their rationale in the incident record.
- Preserve volatile evidence. Capture conversation context, instructions, retrieval details, tool activity, identities, relevant versions, configuration, timestamps, and network destinations. Preserve evidence before deleting an index, rolling back a model, changing access, or rebuilding a workload whenever doing so is safe and practical.
- Analyze and bound the event. Determine which users, identities, models, indexes, tools, data, and downstream systems may be affected. Look for related activity across the same credentials, connectors, deployment, provider, or data-ingestion path. If you cannot establish a boundary, escalate the uncertainty rather than treating the impact as contained.
- Contain the affected path. Revoke or step down compromised identities, disable specific tools or connectors, block malicious prompts or destinations where appropriate, isolate affected workloads, and quarantine suspect models or indexes. Choose the narrowest control that stops the harm, unless evidence points to broader compromise.
- Assess and report. Determine operational, customer, privacy, legal, and security impact with the appropriate owners. Make required notifications through approved channels, and share actionable information externally only after reviewing authorization and sensitivity.
Do not rely on telling a model to ignore malicious instructions as containment. If an agent can take actions, disable or constrain the identity and tools that grant those capabilities; prompt-level changes can be part of remediation, but they do not revoke access that has already been granted.
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Recovery is complete only when the cause has been removed, affected data and access have been addressed, and the restored workflow has been validated. Simply restarting an endpoint or swapping a model does not establish that a poisoned index, leaked credential, or compromised connector is safe.
Remove persistence and repair trust
Remove malicious or unauthorized changes, rotate affected secrets, revoke tokens and credentials, patch application or policy controls, and replace or roll back compromised models and indexes. Validate data integrity and review ingestion pipelines, deployment processes, and dependencies that could reintroduce the same problem.
Test before reopening access
Test the restored system using adversarial prompts and representative workflows. Verify that the original attack path is blocked, sensitive data is handled as intended, retrieval returns trusted content, and tool permissions match the approved design. Confirm that required logging and alerts work during these tests.
Reopen access in controlled stages when the business impact warrants it. Specify who approves restoration, what evidence they need, and what conditions would trigger another shutdown. If the provider or a dependency remains under investigation, document the residual risk and the owner who accepts it.
What should happen after the incident?
Close the response loop with a timeline, an indicator-of-compromise catalogue, a root-cause assessment, a blast-radius record, and a review of legal and business impact. Track corrective actions to completion and update the inventory, detection rules, access controls, response steps, and provider contacts that failed or proved incomplete.
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Share useful incident information through approved channels when appropriate, protecting confidential or personal information. CISA’s JCDC playbook is designed to support voluntary sharing of AI incidents and vulnerabilities, while the organization’s own legal, privacy, and disclosure requirements still govern what it may share.
Review the event with the people who operate the system and those who depend on it. Ask whether detection arrived early enough, whether responders had the necessary evidence, whether containment interrupted the right capability, and whether users or customers had a safe alternative. Feed the answers into the next exercise rather than leaving them as unassigned lessons.
How can you judge whether the plan is ready?
A plan is useful when responders can find the owner, preserve the right evidence, contain the dangerous capability, and make a defensible recovery decision. Use these checks in reviews and exercises:
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- Severity criteria identify escalation and decision owners, including when the impact is uncertain.
- Responders can obtain prompt, retrieval, model, identity, tool, configuration, and time evidence with appropriate access controls.
- Credentials, tools, connectors, models, indexes, and workloads can be isolated independently where the architecture allows it.
- Recovery includes data-integrity checks, adversarial testing, and an approval to restore service.
- Provider cooperation, SIEM/SOAR integration, reporting readiness, and exercise cadence are reviewed as operational capabilities—not assumed.
Compare AI systems and response designs on blast-radius containment, observability and forensic fidelity, human approval for autonomous actions, rollback and recovery, reporting readiness, SIEM/SOAR integration, provider cooperation, and how often the response is exercised. These criteria expose practical differences without treating a single model or product feature as proof of security.
Which guidance can help shape the plan?
Use published guidance to inform, not replace, organization-specific decisions. The relevant publications cover incident lifecycle, AI system security, attack categories, information sharing, monitoring, and agent governance:
- NIST SP 800-61 Rev. 3 (2025): incident-response recommendations integrated with CSF 2.0.
- CISA JCDC AI Cybersecurity Collaboration Playbook (2025): voluntary processes for sharing AI incidents and vulnerabilities.
- Joint Guidance on Deploying AI Systems Securely (2024): NSA, CISA, FBI, ASD ACSC, CCCS, NCSC-NZ, and NCSC-UK guidance on protecting, detecting, and responding to malicious activity against externally developed AI systems and related data and services.
- NIST, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (2025): taxonomy covering evasion, poisoning, privacy, and misuse attacks across generative and predictive AI, with mitigation discussion and limitations.
- MITRE ATLAS Spring 2025 update: a source of AI attack techniques and case studies, including RAG poisoning and model tampering.
- CISA and partners, Guidance for SIEM and SOAR Implementation (2025): prioritized log ingestion and predefined automated response actions.
- Australian Signals Directorate/ACSC, Careful adoption of agentic AI services (2025): recommendations on threat modeling, tested response procedures, runtime authentication, third-party reviews, governance, and accountability.
Frequently Asked Questions
Is an AI-generated phishing email an AI incident?
It is usually handled first as a phishing or account-security incident, even if AI helped create it. Treat it as an AI-system incident as well if evidence shows that an AI service, account, or agent inside your environment was compromised or misused.
What if our AI provider cannot supply the logs we need?
Document which events the provider does and does not expose, then determine whether your application can capture equivalent evidence safely. If the missing visibility prevents you from bounding impact or meeting your obligations, escalate the risk to the business and privacy owners before relying on that service for sensitive work.
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Should responders retain every prompt indefinitely?
No. Retain only what your legal, privacy, security, and records policies require, for the approved period, with access limited to people who need it. Prompt logs can themselves contain sensitive information, so preservation needs safeguards as well as forensic value.
How often should we test an AI incident-response plan?
The cited guidance calls for tested response procedures and exercises, but it does not establish a universal exercise interval. Set a cadence based on how quickly your models, agents, privileges, providers, and data flows change, and exercise again after material architecture or access changes.
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