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An effective AI-incident plan uses NIST SP 800-61 Rev. 3 (2025) as its lifecycle backbone, then adds an AI-specific annex for prompt injection, retrieval or training-data poisoning, sensitive-data leakage, malicious models, model theft, supply-chain compromise and unauthorized agent actions. Before an incident, inventory every model and connector, assign owners, define severity and escalation, arrange legal and provider contacts, and decide what evidence must be retained. During an incident, preserve prompts, outputs, retrievals, tool calls, identities, versions, hashes and timestamps; contain the smallest practical blast radius; rotate credentials; and reopen access only after adversarial validation.
This guide shows how to turn that approach into an operational playbook for generative-AI applications, predictive models, retrieval-augmented generation (RAG) systems and agents that can act on external services.
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What makes an AI-assisted attack different from a conventional incident?
AI systems add probabilistic behavior, hidden instructions, mutable context and third-party dependencies to the normal incident-response problem. A single user prompt can influence retrieval, model output, tool selection and downstream actions, so an investigation must reconstruct the entire chain rather than inspect only a server log.
The attacker may target the model itself, the data around it or the privileges granted to an agent. A malicious document inserted into a vector index can alter answers weeks later. A stolen API key can be used for model theft or “LLM jacking.” A prompt-injection string can make an otherwise trusted agent disclose data or call a tool. The response plan therefore needs AI-specific evidence, containment options and recovery tests in addition to ordinary endpoint, identity and network procedures.
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NIST SP 800-61 Rev. 3, published in 2025, aligns incident response with CSF 2.0 and emphasizes preparation, detection, response, recovery and information sharing. Use those phases for the main plan, and maintain an annex that names the AI assets, failure modes and evidence described below.
How do you scope and inventory an AI environment?
Start with an inventory that is usable at 3 a.m., not a diagram that is accurate only on the day it was drawn. For every component, record its business owner, technical owner, region, data classification, dependencies and emergency shutdown path.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches| Asset or dependency | Record before an incident |
|---|---|
| Foundation, fine-tuned or predictive model | Provider, exact model and version, deployment location, hash where available, approved use cases, rollback image and owner |
| AI application and orchestration code | Repository and release identifier, system and developer instructions, guardrail configuration, secrets location and deployment process |
| RAG stores and training data | Source systems, index or dataset version, ingestion jobs, document owners, ranking configuration, retention and re-index procedure |
| Agents, tools and connectors | Available actions, destinations, scopes, approval gates, service accounts, rate limits and emergency disable switch |
| Users and identities | Human, service-account and administrator roles; authentication method; privilege boundaries; session and token revocation method |
| Providers and supply chain | Contract contacts, status and audit channels, sub-processors, software and model dependencies, notification obligations and available logs |
| Data and regions | Personal, confidential, regulated and proprietary classes; allowed countries or regions; encryption and deletion controls |
Store the inventory where the response team can reach it if the primary identity or collaboration system is unavailable. Include a simple dependency map: user request → application → model → retrieval source → tool or connector → external destination. That map is what responders use to choose between disabling one connector and taking an entire service offline.
Which AI attack scenarios should the playbook cover?
Write a short scenario card for each threat. Each card should state likely indicators, affected assets, immediate containment, evidence to preserve, notification decisions and a safe recovery test.
| Scenario | What can happen | Indicators to watch |
|---|---|---|
| Prompt injection or jailbreak | Instructions in user input or retrieved content override policy, expose hidden context or trigger an unsafe tool call | Instruction-like text in documents, unusual refusal changes, policy violations, tool calls that do not match the user request, repeated obfuscation |
| RAG or training-data poisoning | Attacker-controlled content changes retrieval rankings, answers, classifications or downstream decisions | New or edited source documents, anomalous embeddings or rankings, conflicting answers after an ingestion job, unexplained index drift |
| Privacy or sensitive-data leakage | Prompts, outputs, logs or model responses reveal personal, regulated or proprietary information | Secret or identifier patterns in output, cross-tenant references, requests for system prompts, unusual export volume |
| Malicious or tampered model | A model behaves differently, contains a backdoor or was replaced in a registry or deployment pipeline | Hash mismatch, unexpected model metadata, changed behavior on known test prompts, unsigned artifact or unexplained deployment |
| Supply-chain compromise | A library, plugin, hosted service or model dependency introduces code execution, data theft or altered behavior | New dependency, publisher or checksum change, suspicious network destination, provider security alert |
| Model theft or LLM jacking | Stolen credentials or exposed endpoints let an attacker copy a model, run unauthorized inference or consume quota | Inference from unfamiliar locations, sudden token or GPU use, new keys, rate-limit spikes and billing anomalies |
| Agent privilege abuse | An agent creates accounts, changes records, sends messages, moves funds or accesses data outside its intended task | Tool calls outside a normal workflow, privilege escalation, destination changes, missing human approval or unusual sequences of actions |
| AI-assisted phishing or social engineering | Generated content improves impersonation, lure quality or targeting of staff and customers | Highly personalized campaigns, new sender infrastructure, suspicious requests that reference internal AI projects |
NIST’s 2025 Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations groups attacks into evasion, poisoning, privacy and misuse categories across generative and predictive systems. MITRE ATLAS’s Spring 2025 update added six case studies and 19 techniques, including RAG poisoning, false RAG-entry injection, retrieval-content crafting, prompt crafting and obfuscation, trusted-output manipulation, model tampering, LLM jacking and malicious models. Treat those counts as coverage guidance, not as measurements of attack frequency.
How should you assign owners, severity and authority?
Give one incident commander authority to trade availability for safety, and name deputies for AI engineering, security operations, privacy or legal, the affected business unit and communications. The AI product owner supplies system knowledge; the commander controls the incident. Provider contacts and approved information-sharing channels must be reachable without relying on a compromised account.
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Define severity by harm and blast radius
Use locally named levels, but make the thresholds observable. For example:
- Critical: confirmed exfiltration of regulated or high-value data, unauthorized high-impact transactions, model or identity compromise with cross-tenant reach, or an attack that can propagate through shared infrastructure.
- High: a compromised model, index, connector or service account affecting multiple users or a material business process, even when exfiltration is not confirmed.
- Moderate: a contained policy bypass, poisoned document or suspicious tool call with limited scope and intact approvals.
- Low: an attempted attack or benign test that produced no unauthorized access or lasting change.
For each level, specify who must be paged, the maximum time before a commander is appointed, when legal and privacy review is mandatory, what customer or regulator notification criteria apply, and who can authorize shutdown or restoration. Do not let a severity label substitute for a decision: document what is known, what is suspected and what remains untested.
Prepare communication and evidence rules
Write internal, customer, provider, regulator and law-enforcement templates in advance. Include a neutral description of the affected service, time window, data classes, containment status and a contact for follow-up. Define evidence-retention periods with legal and privacy teams, access controls for sensitive logs, and a chain-of-custody form. CISA’s 2025 JCDC AI Cybersecurity Collaboration Playbook recommends integrating voluntary incident and vulnerability sharing into existing response and information-sharing processes; decide beforehand which contacts and approval steps your organization will use.
What logs and evidence are needed to investigate an AI incident?
Capture enough context to replay the decision without collecting more personal data than necessary. Preserve the original records before filtering, redaction or destructive containment changes them.
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- System and developer instructions, policy versions, guardrail decisions and moderation results.
- Model and provider name, exact version or deployment identifier, inference parameters, input and output hashes, and timestamps with timezone.
- Retrieved documents, source identifiers, index or dataset version, ranking scores and the query used to retrieve them.
- Every tool call, parameters, approval decision, returned result, destination and resulting state change.
- User, service-account and administrator authentication, privilege changes, token issuance and revocation events.
- Network destinations, data transfers, rate-limit events, billing or quota anomalies and provider alerts.
- Deployment, configuration, prompt-template, model-registry and dependency changes.
- Evidence-collection actions, collector identity, time, storage location, hash and every subsequent transfer.
Send prioritized events to the SIEM and expose high-confidence actions to the SOAR platform. CISA’s 2025 guidance on SIEM and SOAR implementation emphasizes prioritized log ingestion and predefined automated responses; avoid flooding the system with low-value token streams while omitting the tool and identity events that establish impact.
Protect the logs themselves
Restrict access by role, encrypt data in transit and at rest, separate production operators from investigators, and monitor searches for sensitive content. Apply retention and deletion rules for personal data, secrets and customer material. If a provider cannot supply prompt, tool or version telemetry, record that limitation as a response risk and compensate with application-side logging or stronger approval gates.
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What is the correct detection and triage sequence?
A practical sequence is triage → activate → analyze → assess → report. Keep it fast enough for containment but explicit enough that a second team can reproduce the decision.
- Triage: Validate the signal, identify the AI service and current user or service account, preserve volatile logs, and assign a provisional severity. Do not paste suspected secrets or attacker prompts into an unapproved external model.
- Activate: Page the incident commander and the AI, security, privacy, legal and business owners required by the severity threshold. Open a restricted incident channel and evidence repository.
- Analyze: Reconstruct the request, retrieval, model response, tool calls and identity events. Compare model, index, prompt and policy versions with a known-good baseline. Search for the same indicators across tenants, applications and providers.
- Assess: Determine what was read, changed, sent or executed; which identities and regions were involved; whether persistence exists; and whether the attack can spread through shared models, indexes, connectors or keys.
- Report: Record facts, assumptions, decisions and gaps. Trigger customer, regulator, law-enforcement or information-sharing notifications when the preapproved criteria are met, while continuing technical containment.
Correlate AI telemetry with identity, endpoint, network, data-loss-prevention and cloud events. A strange answer is not proof of compromise; a normal-looking answer does not clear a system when the retrieval or tool trail is missing.
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How do you contain an AI-assisted attack safely?
Contain the smallest component that stops harmful activity, but be prepared to escalate. Preserve volatile evidence before revoking sessions, deleting poisoned content or replacing a model.
- Stop dangerous actions: Pause autonomous execution, require human approval for high-impact tools, or switch the agent to a read-only mode.
- Restrict identities: Revoke exposed tokens, disable suspicious sessions, step down privileges and isolate compromised service accounts. Keep a verified break-glass account separate from the affected identity system.
- Disable paths, not necessarily the whole service: Turn off a connector, plugin, retrieval source, region or tenant when that removes the attack path. Block malicious destinations and prompt patterns only when the rule will not hide evidence or create unsafe bypasses.
- Quarantine data and models: Freeze suspect indexes, ingestion jobs, datasets and model artifacts. Preserve their hashes and copies for analysis; do not silently overwrite them.
- Protect customers and staff: Rate-limit exposed endpoints, suspend risky workflows, add an interstitial warning or route requests to a known-good fallback. Coordinate provider-side blocks when the attack uses shared infrastructure.
- Confirm containment: Test with benign and adversarial probes, review tool and network telemetry, and check that unauthorized identities can no longer reach the affected path.
Containment may reduce availability or answer quality. Record the safety rationale, business owner approval and conditions for each change so that emergency settings do not become permanent undocumented configuration.
How do you eradicate the cause and recover?
Recovery is more than restoring service. Remove persistence, prove data and configuration integrity, and test the restored system against the attack technique before reopening access.
- Find persistence and root cause: Review code, prompts, policies, dependencies, model registries, index-ingestion jobs, identities and external destinations for unauthorized changes or scheduled re-entry.
- Replace or roll back trusted artifacts: Deploy a verified model, application build, prompt and policy set. Rebuild poisoned indexes or datasets from authoritative sources rather than deleting only the visible malicious entry.
- Rotate secrets and repair access: Replace API keys, signing keys, connector credentials and session tokens; remove excess permissions; and verify that old credentials fail.
- Patch controls: Add input and output validation, retrieval-source allowlists, content provenance checks, tool argument constraints, approval gates, rate limits and egress controls appropriate to the failure.
- Validate representative workflows: Run normal business cases plus adversarial prompts, poisoned-document tests, data-leakage probes, tool-abuse attempts and cross-tenant checks. Confirm that logs contain the evidence responders need.
- Restore in stages: Reopen to a small cohort or low-risk workflow, monitor closely, then expand. Keep the fallback and shutdown path available until the commander signs off.
The 2024 Joint Guidance on Deploying AI Systems Securely from NSA, CISA, FBI, ASD ACSC, CCCS, NCSC-NZ and NCSC-UK addresses controls to protect, detect and respond to malicious activity against externally developed AI systems and their data and services. Apply its supply-chain and monitoring principles when a provider or model artifact is part of the incident.
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What should happen after containment?
Produce a factual timeline from the first indicator through restoration, including hypotheses that were rejected. Document the blast radius, root cause, affected data and decisions, then map every gap to an owner and due date.
- Catalogue indicators of compromise: prompts, document identifiers, hashes, domains, keys, accounts, model versions and tool parameters.
- Assess contractual, legal, privacy, regulatory, customer, safety and financial impact with the appropriate specialists.
- Update detections, allowlists, model and index release controls, identity permissions, provider contracts and runbooks.
- Share actionable intelligence through approved industry, government or provider channels. CISA asks JCDC partners to integrate its playbook into response and information-sharing processes and to provide feedback through its published contact route.
- Track corrective actions to closure and verify them with a retest; do not close the incident because the service is merely available again.
Australian Signals Directorate and the Australian Cyber Security Centre’s 2025 guidance on agentic AI specifically recommends developing and testing procedures to detect, contain and recover from agent compromise. Treat each exercise and real incident as a chance to improve both technical controls and governance.
How should SIEM, SOAR and providers fit into the plan?
Use the SIEM as the correlation layer and the SOAR platform for bounded, reversible actions. Automate actions such as token revocation, connector disablement, quarantine and notification only when the trigger is high-confidence and the rollback is known. Require human approval for actions that can destroy evidence, affect many customers or create legal obligations.
Define provider responsibilities in contracts and operating procedures: log fields and time precision, retention, model and retrieval version identifiers, abuse-alert channels, incident notification windows, forensic assistance, data-location commitments and the ability to suspend keys or workloads. Test those contacts during exercises rather than discovering that the “24-hour” channel is an unmonitored mailbox.
How do you exercise and maintain the playbook?
Run tabletop and technical exercises using realistic combinations, not isolated prompt examples. A useful scenario might begin with a poisoned RAG document, progress to a prompt injection that causes an agent to call a connector, and end with suspected token theft. Have participants locate the inventory, preserve evidence, choose severity, disable the right path, communicate with a provider and validate recovery.
- Exercise every critical model, index, agent and connector at a cadence set by risk and material change.
- Include on-call staff, product owners, privacy or legal counsel, communications, customer support and provider contacts.
- Measure time to detect, activate, preserve evidence, contain, restore and produce a defensible timeline.
- Record where telemetry, permissions, contact details or approval gates failed, then retest the fixes.
- Update the annex after model, prompt, index, provider, region, connector or regulation changes.
How should you compare incident-response plans or products?
Whether you are evaluating an internal plan, a managed service or SIEM/SOAR tooling, compare capabilities against the failure modes your inventory shows. A polished dashboard is not a substitute for evidence or authority.
| Comparison axis | Questions to ask |
|---|---|
| Blast-radius containment | Can operators disable one tenant, connector, index, model or identity without taking down unrelated workloads? |
| Observability and forensic fidelity | Are prompts, retrievals, tool parameters, versions, hashes, identities and timezones captured and exportable? |
| Human approval | Can high-impact autonomous actions pause for approval, and are approvals recorded with the exact request and result? |
| Rollback and recovery | Are trusted model, prompt, policy and index versions preserved, and can secrets be rotated quickly? |
| Reporting readiness | Can the team produce a timeline, indicator catalogue, data-impact assessment and notification record? |
| SIEM/SOAR integration | Are high-value events prioritized, correlated with identity and network data, and connected to reversible playbook actions? |
| Provider cooperation | Are support, abuse, forensic and emergency-shutdown contacts documented and tested? |
| Exercise cadence | Does the organization run scenario-based tests after material changes and track corrective actions to closure? |
What is the minimum viable AI incident annex?
If you must start small, publish a controlled document with these sections and expand it as systems mature:
- Service and dependency inventory with owners, regions, data classes and shutdown paths.
- Scenario cards for prompt injection, poisoning, leakage, malicious models, supply chain, model theft and agent abuse.
- Severity matrix, on-call roster, decision authority and legal, privacy, provider and communications contacts.
- Evidence schema covering prompts, outputs, retrievals, tools, identities, versions, hashes, timestamps and chain of custody.
- Containment commands or UI paths for identities, connectors, indexes, models and network destinations.
- Recovery checklist with trusted artifacts, secret rotation, adversarial tests and staged reopening.
- Notification and information-sharing criteria, templates and approval records.
- Exercise schedule, metrics, corrective-action register and change-control trigger.
This annex makes the general NIST lifecycle actionable for AI while preserving the organization’s existing security, privacy and crisis-management processes.
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Frequently Asked Questions
How long should AI incident logs be retained?
There is no universal retention period that fits every jurisdiction or data class. Set periods with legal, privacy, regulatory and contractual owners, retain a protected forensic copy for the longest applicable requirement, and document early deletion or legal holds.
Should we shut down every AI system after a prompt-injection incident?
Not automatically. First disable the affected identity, retrieval source, connector or autonomous action and verify containment; take broader services offline only when shared infrastructure or uncertain scope makes targeted isolation unsafe.
Who should lead an incident involving an autonomous agent?
Use the organization’s established incident commander, supported by the AI product owner, security operations, identity, privacy or legal and the affected business unit. The product owner explains behavior, but the commander retains authority over shutdown, communications and restoration.
How can a small team test prompt-injection defenses without risking production data?
Create a segregated test tenant with synthetic documents, nonproduction credentials and disabled high-impact tools. Replay representative workflows with crafted instructions, obfuscation, poisoned retrieval entries and exfiltration attempts, then verify both blocking behavior and the completeness of logs.
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What if an AI provider will not provide prompt or tool-call logs?
Treat the missing telemetry as a material response limitation. Increase application-side logging, restrict provider permissions and autonomous actions, require stronger approvals, and make log fields, retention, emergency contacts and forensic cooperation explicit in the provider agreement.
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