AI Belongs In Your Incident Response Plan Before it's the Incident

AI Belongs in Your Incident Response Plan Before It Becomes the Incident
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Most organizations now have some form of AI in the environment, whether it’s a formal copilot rollout, a vendor feature quietly turned on inside a SaaS tool, or shadow AI (think employees using public tools to move faster). The problem is that many incident response plans (IRPs) still assume incidents are limited to familiar categories like malware, phishing, ransomware, or a straightforward data breach. AI changes the shape of incidents. It changes how they start (e.g., though prompt injection, unsafe automation, or model misuse), what harm looks like (such as misleading or discriminatory outputs, contractual commitments made by an agent, or IP leakage), and where evidence lives (think prompt logs, model configuration, or vendor feature flags). If your IRP doesn’t explicitly account for AI, you can end up treating an AI-driven event like a routine IT issue and only after the fact realize it's a privacy, legal, and business crisis.

Bringing AI into incident response doesn’t require rebuilding your entire program. It requires making AI visible and accounted for in the same operational places you already rely on: (1) what employees report, (2) how your escalation matrix works, (3) what you require from vendors, and (4) what you practice through tabletop exercises. Below are four practical steps that organizations can take to make AI incident response real without creating a bureaucracy that slows the business down.

Remember, this article is for general information and education only. This is not legal advice. You should talk to your own counsel about how the law and rules apply to your specific facts.

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1) Add AI to your incident reporting taxonomy so people actually report it

Incident response starts long before your IR team is engaged. It starts with an employee (or a business owner) noticing something unusual and having enough clarity to report it as an incident rather than dismissing it as “the tool being weird.” If AI isn’t named as something reportable, you’ll see underreporting, delayed escalation, and lost evidence.

A simple fix is to expand your reporting categories and training materials to explicitly include AI incidents and AI-related events, such as: (1) suspected disclosure of confidential or personal data into an AI tool; (2) AI outputs that include sensitive information; (3) prompt log or transcript exposure or inadvertent disclosure; (4) AI-generated content that is materially wrong and used externally; (5) AI-driven decisions impacting individuals; (6) unauthorized enabling of AI features in a vendor product; or (6) signs that an AI agent is taking actions outside authorized parameters. The goal is not to label every odd output as a crisis but rather to ensure your organization captures early signals, preserves evidence, and routes issues to the right owners before any business impact compounds to intolerable levels.

To make this operational, update your incident reporting guidance (and the language in your reporting channels) so it tells employees what to do in plain terms.

‍For example: if the incident involves an AI tool, they should capture the tool name, time and date, what was entered, what came out, whether the output was shared externally, and whether any personal data or confidential information may have been involved. This is the AI equivalent of “take a screenshot and don’t reboot the laptop.” Early details matter and they disappear quickly if the tool does not retain logs.

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2) Build AI into your escalation criteria and severity matrix

Many organizations have an escalation matrix that answers two questions: “How bad is this?” and “Who needs to know right now?” If AI isn’t in the matrix, AI events often fall into the cracks between Security (who sees it as a tooling question), Legal/Privacy (who sees it as a data handling issue), and the business (who sees it as a productivity feature). You want a matrix that creates fast, predictable routing that works when AI is involved.

A practical approach is to add AI-specific triggers into your existing severity definitions. For example, escalation should occur when an AI event involves: (1) personal data (especially sensitive data); (2) employee data; (3) regulated data (health, biometrics, financial); (4) external sharing (even if unintentional); (5) customer-facing outputs; (6) automated decisions affecting individuals; (7) suspected model compromise or prompt injection that could impact integrity; (8) unauthorized tool use; or (9) vendor AI features that were not inventoried duig vendor-onboarding and can’t be disabled quickly. Those triggers should tie directly to who must be looped in, such as Privacy, Legal, Security, Comms, HR (if workforce data or conduct is involved), and Procurement/Vendor Management (if a vendor AI feature or a subprocessor is implicated).

This is also where organizations can prevent panic. Your matrix can distinguish between (a) quality incidents (hallucinations caught internally before use), (b) policy incidents (use of unapproved tools, prohibited data in prompts), (c) security incidents (prompt injection, account compromise, suspicious data exfiltration), and (d) privacy/legal incidents (personal data disclosure, discriminatory outcomes, consumer harm). When severity is defined clearly, teams don’t argue about labels. They just get down to executing the workflow.

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3) Treat AI vendor preparedness as part of incident readiness and contract for it

AI incidents often become vendor incidents. Even if your organization didn’t deploy AI directly, vendors may introduce AI features that process your data, store prompts and outputs, or route content through subprocessors. When something goes wrong, your ability to respond depends on whether you can get answers to the following questions quickly:

• What data was involved?
• Where are the logs?
• Was the data used to train models?
• Which subprocessors touched it?
• Can the feature be disabled?
• What is the vendor’s notification timeline?
  

This is why vendor contracting needs AI-specific incident readiness language. A strong baseline is to expand your definition of “Security Incident” or “Privacy Incident” to cover AI realities such as like prompt/output exposure, unauthorized model training on your data, compromise of model configuration or system prompts, or a failure of access controls around AI features. From there, your agreement should require rapid notification, detailed cooperation, preservation of evidence, and clear timelines. It should also require the vendor to disclose whether prompts/outputs are retained, how long they’re retained, whether they are used to improve models, and what controls exist to prevent that.

If your organization has an AI “no-training” position (which many do), contract language becomes a control rather a preference. Vendors should be required to commit that your data will not be used to train or fine-tune models, and that they will not enable new AI features or subprocessors that change data use without advance notice and a right to object. Incident readiness is not just about notification, rather about ensuring you can actually investigate and remediate incidents as they arise.

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4) Run tabletop exercises with AI scenarios and AI injects because AI makes “normal” incidents harder

Tabletops are where a paper plan becomes an operational reflex. If your tabletops never include AI, your incident response muscle memory will be incomplete. AI incidents are rarely isolated. They often complicate existing scenarios. A phishing incident becomes more dangerous if a compromised account is tied to an AI tool that stores prompt history where employees communicate about their benefits. A vendor breach becomes more complex if the vendor’s AI feature routed your data to subprocessors you didn’t realize were in scope. A simple “lost laptop” becomes a bigger issue if the laptop contained a locally synced dataset used to fine-tune an internal model or store cached AI outputs and full-drive encryption wasn't active.

You don’t need to run separate “AI-only” tabletops to get value. In many cases, the best approach is to add AI injects to scenarios you already run.

For example: mid‑scenario you learn an employee pasted sensitive data into a public AI tool, and the tool stores prompts for 30 days. Or maybe, you learn that an AI feature was enabled by default after a product update after a vendor sent a notice of its own data breach. Or  a customer claims an AI-generated output disclosed information about another individual when they call to ask that their data be deleted. These injects force cross‑functional decision-making. Legal and Privacy assess notification risk and contractual obligations, Security identifies evidence sources and containment options, Procurement checks vendor rights and escalation channels, and Comms prepares stakeholder messaging.

The result is “resilience by design” in practice. The organization learns where AI touches data, where controls are missing, and what evidence needs to be retained to respond quickly and defensibly.

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A practical starting point

If you want to make progress in 30 days without boiling the ocean, start here:

1. Update your incident reporting guidance to include AI events.
2. Add AI triggers to your escalation matrix and SOPs.
3. Confirm vendor AI provisions for your critical vendors.
4. Run one tabletop that includes at least one AI inject.

That sequence creates immediate operational benefit and it produces evidence that your program is adapting to real risk, not just yesterday’s threats.

AI is already in your environment. The question is whether your incident response program will recognize it fast enough, escalate it correctly, and respond with the same discipline you apply to traditional incidents. The best time to integrate AI into IR is before your first AI incident forces the issue.