Daviyon DanielsWhen a ransomware attack hits, your incident response plan kicks in. You have playbooks. You have escalation paths. You know who calls whom at 2 AM.
Now ask yourself: what happens when your AI system starts generating discriminatory outputs at scale? What is the process when your LLM-powered customer service bot leaks sensitive data from one user's session into another's? Who owns the incident when your AI vendor's model update quietly changes system behavior in a way that affects your operations for three weeks before anyone notices?
Most organizations do not have answers to those questions. Their IR plans were built for traditional infrastructure threats and have not been updated to account for the fundamentally different failure modes of AI systems.
That gap is a liability.
Why Traditional IR Plans Fall Short
Conventional incident response frameworks — NIST SP 800-61, SANS PICERL — were designed around a core assumption: incidents have discrete causes that can be isolated, contained, and remediated. A vulnerability gets patched. Malicious code gets removed. The affected system is restored from backup.
AI incidents often do not work that way.
When an AI system fails, the root cause may be ambiguous. It might be a training data problem that manifests months after deployment. It might be emergent behavior from a model update your vendor did not document. It might be adversarial input — a prompt injection attack designed to manipulate your system's outputs without triggering any traditional security alert. It might be model drift, where a system that worked correctly at launch has gradually degraded because the real-world data it is operating on no longer matches its training distribution.
None of those failure modes produce a clean forensic artifact. There is no malware hash to block, no IP to null-route, no CVE to patch.
Your IR team also may not know an AI incident is happening. AI failures can be slow, subtle, and statistical. A model that produces biased outputs 8% of the time looks like normal variance until you analyze enough data to see the pattern. By then, the damage may already be significant.
Types of AI Incidents You Need to Plan For
Before you can build an AI-aware IR plan, you need a taxonomy of what you are planning for. At minimum, your plan should address:
Model integrity failures. Unauthorized modifications to model weights, supply chain compromises affecting third-party models, or unexpected behavioral changes introduced by vendor updates. These require controls around model versioning, hash verification, and change management — not just traditional integrity monitoring.
Data exfiltration via AI systems. LLMs can be manipulated into revealing training data, system prompts, or information from other users' sessions. This category also includes insider threats where AI tools become exfiltration vectors — employees feeding sensitive documents into external AI services.
Adversarial attacks. Prompt injection, jailbreaking, and adversarial examples designed to manipulate AI outputs. These attacks exploit the model's reasoning, not its infrastructure, which means your WAF and SIEM will not catch them without specific AI-focused detection.
Harmful or non-compliant outputs. Outputs that are discriminatory, defamatory, legally problematic, or that violate your organization's policies. These may not be "attacks" in the traditional sense, but they carry real legal and reputational risk and require a defined response process.
AI system availability failures. Denial-of-service conditions specific to AI, including resource exhaustion attacks that target computationally expensive inference operations.
What an AI-Aware IR Plan Includes
A solid AI incident response plan is not a separate document — it is an extension of your existing IR framework that adds AI-specific procedures. Here is what those additions need to cover.
AI asset inventory. You cannot respond to incidents involving systems you do not know exist. Your plan needs to be backed by a maintained inventory of all AI systems in use: first-party models, third-party APIs, embedded AI features in SaaS products, and shadow AI usage by employees. The AI RMF from NIST provides a useful governance structure for this.
AI-specific detection capabilities. Traditional SIEM rules will not detect prompt injection or model drift. Your detection layer needs to include output monitoring, behavioral baselining for AI systems, and logging of inputs and outputs at a level of granularity that makes forensic analysis possible. Review NIST AI RMF's MEASURE function for guidance on what to monitor.
Defined roles for AI incidents. AI incidents often involve stakeholders that traditional IR does not: model owners, data science teams, AI vendors, and potentially regulators under emerging AI laws. Your escalation matrix needs to reflect that. Someone needs to own the decision to take an AI system offline — and that decision is often harder than it sounds when the system is customer-facing.
Containment procedures specific to AI. Containment for AI incidents may mean disabling specific features rather than taking a system fully offline. It may mean rolling back to a previous model version. It may mean implementing temporary rate limiting or output filtering while the root cause is investigated. Your plan should enumerate these options so responders are not making them up under pressure.
Communication templates. Who do you notify if your AI system produced discriminatory outputs for 48 hours? What do you tell affected users? What do you tell regulators? Draft those templates now. Under the EU AI Act (for high-risk systems) and emerging US state AI laws, notification obligations are becoming more specific.
Post-incident review process. AI incidents require a different kind of post-mortem. You need to understand not just what happened, but why the model behaved as it did — which may require your data science team, not just your security team.
Testing Your AI IR Plan
A plan you have not tested is a hypothesis. Tabletop exercises are the minimum bar.
Design scenarios that are realistic to your environment. If you use LLM-based tools, run a tabletop around a prompt injection incident. If you use AI for customer-facing functions, model a scenario where the system produces outputs that violate your acceptable use policy at scale.
Go further by including your vendors. Many AI incidents will involve third-party systems, and your response will depend on how quickly vendors can provide information about model behavior, logs, and updates. Find out now whether that is a 4-hour SLA or a 4-week process.
Red team exercises that specifically target your AI systems — including attempts to manipulate inputs and observe outputs — give you empirical data about your actual exposure rather than theoretical risk assessments.
Getting Started
If your IR plan does not mention AI systems, machine learning models, or LLMs anywhere, start there. Review NIST SP 800-61 alongside the NIST AI RMF and identify the gaps. Extend your asset inventory to include AI systems. Add AI-specific roles to your escalation matrix.
At Ayliea, our AI security assessments evaluate your current IR readiness for AI incidents as part of a broader posture review. If you want to understand where your gaps are before something goes wrong, that is what we are here for.