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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Don’t write a separate “AI incident” binder. Extend your existing incident response capability so it can handle two things: attacks on AI systems (prompt injection, poisoned data, model extraction, hijacked agents) and attacks using AI (for example, deepfake impersonation aimed at your staff). Use NIST’s current baseline, SP 800-61 Rev. 3, as the backbone. Then add AI-specific owners, evidence, containment decisions and exercises.
This guide walks through that process in order. It covers the baseline, how to size the plan to your exposure, seven build steps, five scenario cards to rehearse, and how to keep the plan current.
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Start from the current NIST baseline
NIST finalized SP 800-61 Rev. 3 on April 3, 2025. It supersedes the 2012 Rev. 2 and places incident-response recommendations inside the Cybersecurity Framework (CSF) 2.0. That matters because it treats response as part of organization-wide risk management, not a standalone technical playbook. NIST’s own wording: the publication “seeks to assist organizations with incorporating cybersecurity incident response recommendations and considerations throughout their cybersecurity risk management activities as described by the NIST Cybersecurity Framework (CSF) 2.0” (NIST SP 800-61 Rev. 3, 2025).
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesNIST’s Incident Response project page describes the structure you can reuse:
- Govern, Identify, Protect: the broader preparation and risk-management functions that support incident response.
- Detect, Respond, Recover: the response lifecycle itself.
- Improvement: lessons from all functions feed back into the program.
NIST also says detailed procedures vary by technology, environment and organization. No document hands you a finished AI response runbook. Rev. 3 gives you the skeleton, and you supply the AI-specific detail from your own systems.
What changes for AI, function by function
| CSF 2.0 function | Existing capability to keep | AI-specific addition |
|---|---|---|
| Govern | Executive sponsorship, resourcing, roles | Name an owner for each AI system and define who may suspend it |
| Identify | Asset inventory, risk assessment | Inventory models, versions, data sources, retrieval stores, prompts, tool permissions and providers |
| Protect | Access control, hardening, logging | Limit agent permissions; log prompts, retrieved content and tool calls so evidence exists later |
| Detect | Alert intake and triage | Triage categories for injection, integrity, disclosure, agent actions and synthetic media |
| Respond | Containment, evidence handling, communications | Containment options such as revoking tool access or pausing a data pipeline |
| Recover | Restoration, monitoring | Validate data and model integrity before restoring |
| Improvement | Post-incident review | Update the AI inventory, controls and scenario exercises after each event |
The right-hand column is a plan-design reading of NIST’s AI threat descriptions, not a format NIST prescribes.
Size the plan to your exposure first
An organization that hosts its own model faces a different incident than one that calls a third-party AI service through an API. The same goes for an AI that only answers questions versus one that can send email or change records. NIST’s planning guidance says plans should reflect each organization’s mission, size, structure and functions (NIST SP 800-61 Rev. 3 PDF). Five questions help you apply that. They are editorial planning aids drawn from the varied risks in NIST’s Generative AI Profile and its January 2026 agent-security request for information.
| Question | If “yes”, the plan needs more of… |
|---|---|
| Do we operate or fine-tune a model, not just consume one? | Training-data provenance checks, model version control, rollback procedures |
| Do we rely on third-party AI services? | Provider contacts, contractual notification routes, a fallback when the service is unavailable, and clarity on what logs you can obtain |
| Can the AI use tools or take consequential actions? | Permission inventories, action-history review, a fast way to suspend the agent |
| Does it touch sensitive or poorly sourced data? | Disclosure analysis, notification decision-makers, data-integrity validation |
| How critical is the service, and how much downtime is acceptable? | Manual or alternate workflows agreed before an incident |
| How much can we do ourselves? | Pre-arranged external incident-response or forensics support where internal capacity is thin |
Build the plan in seven steps
1. Set scope, authority and objectives
List the systems and business services covered. Then name the people: executive sponsor, incident commander, security lead, AI or model owner, IT and cloud operators, legal and privacy, communications, business continuity, and any external parties.
Next, write down who is allowed to do each of these things without waiting for a meeting:
Rank #2
- Declare an incident
- Isolate a service
- Revoke credentials
- Suspend an AI agent
- Preserve evidence
- Notify affected parties
- Approve restoration
NIST says plans should identify necessary resources and management support (SP 800-61 Rev. 3 PDF). Without that, responders hesitate at the moment speed matters.
2. Map AI assets and dependencies
Keep a living inventory with these fields for each AI service:
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- Business owner and technical owner
- Model and version
- Training, tuning and retrieval data sources
- Prompts and configuration
- APIs and tool permissions
- Hosting provider
- Logging coverage
- Downstream systems and business processes it feeds
Record what normal behavior looks like. During an incident, the first question is often whether you’re seeing a compromised server, a manipulated input or genuine model drift, and a baseline helps you tell them apart. NIST doesn’t prescribe an inventory format. These fields follow from the threats it describes in the Generative AI Profile, its March 2025 adversarial machine learning report and the agent-security request.
3. Define detection and triage criteria
Create intake routes for employee, customer, vendor and automated alerts. Then give triage staff a short decision list. Is this:
- An ordinary account or software compromise?
- Malicious input or prompt injection?
- A data or model integrity concern?
- Unauthorized disclosure or extraction?
- Harmful action by an AI agent?
- Synthetic-media impersonation?
An event can be several at once. Assess impact on confidentiality, integrity, availability, safety, legal duties and business operations. NIST identifies these threat categories, but the severity thresholds and escalation rules have to be yours.
Rank #3
4. Preserve evidence
Assign who captures each of the following, and make sure it is being logged before an incident, not after:
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- Alerts and timestamps
- Identity and access records
- Network and application logs
- Prompts and retrieved content
- Model and system versions
- Tool calls and agent action history
- Affected data and artifacts
- Configuration changes
- Communications
Where feasible and safe, snapshot affected systems or data before you change anything. Evidence needs differ by system and incident, and the NIST material doesn’t offer one universal forensic procedure for AI. Check early whether your third-party AI providers retain the logs you would need, because you may not control them.
5. Contain proportionately
Write down the authorized actions and who decides on each:
- Disable an integration
- Narrow an agent’s permissions
- Block a malicious source
- Revoke credentials
- Isolate a service
- Pause a model deployment or data pipeline
- Switch to a manual or alternate workflow
Weigh operational and safety consequences before shutting a system off. Indirect prompt injection and harmful agent actions make connected tools and permissions central to containment: cutting what an agent can reach is often a faster, less disruptive first move than taking the whole service down. That is a judgment call for your environment, not a NIST rule.
6. Coordinate continuity and communications
NIST calls for synchronizing the response plan with business continuity. Practically, that means identifying alternate workflows, recovery priorities, who decides on internal and external notifications, and which communication channels are approved.
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For suspected deepfake requests involving money, access or sensitive changes, define trusted-channel verification in advance. That means confirming through a known, independently sourced contact route, not by replying to the request. Also define who can escalate. The joint NSA, FBI and CISA Cybersecurity Information Sheet on deepfake threats (September 12, 2023) covers organizational preparation and response. CISA now marks that page as archived, so treat it as a 2023 reference point and check for newer guidance.
7. Recover and improve
Set the conditions that must be met before a service is restored:
- Data and model integrity validated
- Credentials reissued
- Monitoring for recurrence in place
- Status communicated to stakeholders
Then hold a post-incident review, assign improvement actions with owners, and update the plan, inventory, controls and exercises. NIST’s lifecycle explicitly feeds lessons from all functions into continuous improvement (NIST Incident Response project).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Scenario cards to rehearse
Each card is a short exercise prompt. They come from NIST and joint-government threat descriptions and are not an exhaustive taxonomy or a substitute for a risk assessment of your own systems.
Prompt injection, including indirect injection
Malicious instructions arrive in a direct input or inside retrieved content, such as a document, web page or email, and cause unintended behavior or affect connected systems (NIST AI 600-1). Exercise question: what content was retrieved, what permissions did the system hold, what did it do, and was information exposed?
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Data or model poisoning
Tampered training, tuning or retrieval inputs may change outputs or behavior. Exercise question: can we show the provenance and integrity of each input, and when did behavior first change? NIST’s adversarial machine learning report is the reference taxonomy for attack types.
Privacy, extraction or misuse
Possible sensitive-data disclosure, inference about training data, model extraction or abuse of the service. Exercise question: who determines which data was affected, and who decides on required notifications?
Agent action without an obvious adversarial prompt
An agent can cause harm through specification gaming or misaligned objectives, with no attacker in the loop. NIST’s January 2026 request for information raises this alongside conventional attacks. Exercise question: can we reconstruct the action history, and do we know what permissions made the harm possible? NIST’s later summary analysis of responses (published May 18, 2026) reports that conventional cybersecurity practices remain relevant to agents but need adaptation. That supports extending your existing plan rather than replacing it.
Synthetic-media impersonation
A convincing voice, video or message “from” an executive requests a payment, access or a sensitive change. Exercise question: do staff know the verification route, and is the original message or media preserved for investigation?
Exercise it and keep it current
Run each scenario card as a tabletop with the actual decision-makers in the room, including legal, communications and business continuity, not just security staff. Check that the people named in step 1 can really take the actions you’ve assigned them, and that the logs in step 4 exist. Feed gaps into your improvement actions. Re-review the plan whenever you deploy a new model, give an agent new tools, change a provider or revise your data sources, since each of those changes your incident surface.
One limit to keep in mind: the cited NIST and government sources describe threats and response structure, but they don’t publish figures on how often AI incidents occur or how effective particular response measures are. Build your priorities from your own inventory and risk assessment, not from headline statistics.
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