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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsInvestigate a suspected AI-driven security incident by verifying what happened, correlating AI activity with the identities and systems around it, preserving relevant evidence, and containing the specific threat without automatically shutting down every AI service. An unusual model response alone does not prove compromise. The response should establish whether confidentiality, integrity, or availability was actually or imminently put at risk, and separate confirmed facts from hypotheses.
What counts as a suspected AI-driven security incident?
CISA’s JCDC AI Cybersecurity Collaboration Playbook defines an AI cybersecurity incident as “An occurrence that actually or imminently jeopardizes, without lawful authority, the confidentiality, integrity, or availability of the AI system, any other system enabled and/or created by the AI system, or information stored on any of these systems.” This working definition is useful for scoping: an event can involve the AI service itself, a system it enables, or information associated with either.
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Use your organization’s incident criteria to determine whether the event is confirmed and what response it requires. Do not classify an event as AI-caused merely because an output is unexpected. It may reflect authorized behavior, a configuration or software issue, an account compromise, or activity elsewhere in the connected environment.
How should you triage the report?
Start by establishing what was reported, what systems could be affected, and whether there is a credible risk to confidentiality, integrity, availability, or business operations. CISA’s incident-response guidance frames detection and analysis as determining whether an incident occurred and assessing its type, extent, and magnitude.
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- Record the signal. Preserve the original alert, report, relevant timestamps, and the identity of the person or system that raised it. Note what is directly observed and what remains an interpretation.
- Check authorization. Compare the activity with approved uses, expected users, scheduled jobs, recent changes, and known operating behavior. Ask the relevant system owner whether the action could be legitimate.
- Map the affected environment. Identify the AI service and model, related data, accounts and privileges, workloads, applications, cloud resources, deployment or data pipelines, and any tools or external services the AI system can access.
- Set the time window. Establish the earliest plausible activity and the latest known event. Include relevant activity before the alert; the first observed symptom may not be the start of the incident.
- Assess impact and urgency. Determine whether sensitive information may have been exposed, system or model integrity may have changed, service availability is affected, or downstream business functions are at risk. Consider the AI system’s autonomy and the sensitivity of the resources available to it.
Use NIST SP 800-61 Rev. 3 as the general incident-response reference identified here. NIST finalized it in April 2025, integrated incident-response recommendations throughout the Cybersecurity Framework 2.0, and stated that it supersedes SP 800-61 Rev. 2. CISA’s federal incident-response playbook is organized around preparation, detection and analysis, containment, eradication and recovery, and post-incident activities. Its stated use is for federal executive branch agencies responding to confirmed malicious activity when a major incident has been declared or has not been reasonably ruled out. Other organizations can use it as a reference while following their own policies and obligations; it is not a universal reporting mandate.
What evidence should you preserve?
When circumstances permit, preserve relevant evidence before making changes that could overwrite or alter it. Follow your organization’s evidence-handling procedures, and involve counsel when legal, privacy, contractual, or law-enforcement issues may apply. Preserve what exists; do not assume every system retains every log or that missing telemetry can be reconstructed later.
| Evidence area | Examples to consider | Why it matters |
|---|---|---|
| Conventional security and infrastructure records | Perimeter, network, endpoint, application, cloud, and identity logs; user activity; deployment and data-pipeline records | These records can show access, affected assets, privileges, timing, and activity beyond the AI service. |
| AI service activity | Prompt and completion logs, model and dataset versions, model parameters or configuration, tool or agent activity, retrieval or embedding queries, and decision traces, when available and lawfully retainable | These records can help establish what the system received, which version acted, and what actions followed. |
| System state | Depending on the circumstances and available expertise, volatile-memory capture and forensic disk images | Some evidence may change or disappear when a system is shut down, restarted, or modified. |
| Provenance and change history | Hashes or other provenance metadata collected under established procedures, plus configuration, model, data, and system-change records | These help assess integrity and distinguish approved versions from unexpected changes. |
For each item collected, record what it is, who collected it, when and from where it was collected, and how it was transferred or stored. Maintain accountability for transfers and protect the material as potential evidence. NIST IR 8596, an initial preliminary draft published in December 2025, discusses dataset tracking, model metadata, investigation records, integrity, provenance, and AI inputs and outputs. It is a draft, not a finalized requirement.
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How do you determine what happened?
Build a timeline and correlate AI-service activity with identity, endpoint, network, cloud, application, deployment, and data-pipeline records. Examine whether the suspected event involved unauthorized access, malicious or unexpected inputs, changes to data or a model, misuse of connected tools, or a more ordinary account or software compromise.
- Compare the observed activity with a known baseline and relevant approved changes.
- Identify which accounts and privileges were used, which assets were reached, and whether data or downstream systems were affected.
- Document adversary behavior that is supported by evidence, and record plausible competing explanations separately.
- State what cannot be confirmed—for example, because relevant logs, model versions, or change records were not retained.
NIST AI Risk Management Framework Playbook material recommends monitoring deployed systems, documenting incident and error processes, and keeping version history and change records to support improvement. Those records are useful both for investigation and for distinguishing a system failure from unauthorized activity; they do not, by themselves, establish that an incident occurred.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you contain the incident?
Choose containment based on the observed threat, potential impact, and service-continuity needs. Coordinate with the AI system owner and relevant security, IT, legal, privacy, communications, and business-continuity teams. Contact the AI provider when provider-controlled systems, records, or response actions may be involved.
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| Observed concern | Possible proportionate action |
|---|---|
| Compromised credentials or tokens | Revoke or rotate affected credentials and review related access. |
| Compromised or actively targeted workload | Isolate the affected workload where feasible while preserving needed evidence. |
| Excessive or misused agent capability | Restrict the agent’s tools, permissions, or external connections. |
| Suspect model deployment or integration | Pause the affected deployment or disable the vulnerable integration if the risk justifies it. |
| Malicious inputs identified | Block or otherwise control those inputs while investigating their reach and effects. |
These are options, not a fixed response sequence. A broad shutdown can disrupt legitimate operations without addressing the entry point; conversely, keeping a highly connected system online may allow harm to continue. Make and record the decision in light of the evidence and operational consequences.
How do you eradicate, recover, and learn?
- Remove the cause. Address the confirmed vulnerability, unauthorized access, malicious change, or unsafe integration that enabled the incident.
- Restore from known-good assets or versions. Confirm that model, data, configuration, and tool versions are approved, and rotate secrets that may have been compromised.
- Validate dependencies and controls. Check connected services and security controls, and verify that restoration did not reintroduce malicious changes.
- Monitor for recurrence. Watch the restored service and its connected systems for renewed suspicious activity.
- Document the outcome. Record impact, response decisions, evidence, remaining uncertainty, notifications, and lessons. Update incident and error handling, monitoring, and version or change records where needed.
Applicable notifications and reporting duties depend on the organization, jurisdiction, contracts, and facts. The cited guidance does not establish one universal reporting deadline. Determine obligations with the appropriate legal, privacy, compliance, and leadership contacts rather than assuming a single rule applies to every AI incident.
How should you choose the response approach?
There is no universal scoring formula for AI incidents. Use these decision axes to make the trade-offs explicit and document why the selected response is proportionate.
- Evidence volatility: Could logs, memory, or system state disappear if you contain or restart the service before collection?
- Security and business impact: What is at risk across confidentiality, integrity, availability, and downstream operations?
- Autonomy and access: Can the AI system act without human approval, and what tools, sensitive data, or systems can it reach?
- Containment versus continuity: How quickly must activity stop, and what business functions would a pause disrupt?
- Obligations and coordination: Are legal, privacy, contractual, provider, or reporting considerations relevant?
- Response capability: Can the organization collect and analyze the necessary evidence in-house, or is outside incident-response or digital-forensics support needed?
CISA describes Velociraptor as a way to collect and examine artifacts across a network, conduct targeted hunts, and adapt queries during an investigation. CISA also says it does not endorse commercial products or attest to their suitability, so this is an optional example rather than a recommendation. Use any tool only where it fits the environment and established evidence-handling procedures.
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