Thought for the week: Preparing for AI incidents

Many organizations have a relatively well-developed crisis response plan, including a cybersecurity incident response plan. These plans have been tested through tabletop exercises and real-world situations, such as cyber incidents involving ransomware, compromised credentials, data theft and supply chain incidents. Not many organizations, however, have already updated their playbook to handle artificial intelligence incidents.

Against that backdrop, the AI Incident Response Framework V1.0, published last fall by the Coalition for Secure AI, remains a useful resource for organizations considering how incident response practices may need to evolve as AI systems become more widely deployed.

What are some types of AI incidents, and how might they differ from  traditional cyber incidents?

AI incidents can present in many different ways and raise novel issues that are challenging to field on the fly without preparation. For example, for your own organization, how would you respond to an incident involving data poisoning where training data manipulation alters model behavior and results in inaccurate outputs in customer interactions/communications? How would the organization respond if performance or robustness failures resulted in nonsensical, policy-violating, or unsafe generations for use with patient or other sensitive data subject populations? Or how would the organization respond if algorithmic bias resulted in systematic impacts across protected classes among job applicants or employees? 

The answers vary depending on the industry vertical of the organization, the severity of the incident and other specifics. Regardless of the specifics, it would be helpful to start evaluating how to expand the playbook to cover AI incidents, particularly as the organization gets deeper into more powerful agentic AI use cases.

Key AI-specific considerations to build into an incident response plan

The AI Incident Response Framework is primarily designed to equip security teams with capabilities to effectively respond to AI-specific incidents. It also contains definitions and concepts that are readily accessible to business leaders, legal/compliance, human resources and other functions. Among other provisions, the framework maps AI-specific considerations across the six NIST Cybersecurity Framework 2.0 functions, as follows:

Govern: Establish AI-specific incident response policies: include prompt injection, misuse of retrieval-augmented generation pipelines and training data exposures. Ensure third-party responsibilities, such as model vendors or SaaS orchestration, are contractually defined.

Identify: Inventory large language model tools, APIs, model endpoints, external connectors and data sources. Capture attack surfaces such as embeddings, memory modules and tool chains. Threat modeling must include jailbreaking and adversarial prompts.

Protect: Implement robust input/output guardrails, system prompts and behavioral constraints. Ensure LLM telemetry, prompt logs and personally identifiable information filters are enabled. Validate training pipelines to prevent data poisoning or unintentional PII ingestion.

Detect: Continuously monitor prompt patterns, output anomalies and system behavior. Correlate logs from LLM APIs, tool executions and chat sessions. Integrate LLM-as-a-judge metrics and fine-tuned filters to detect AI-targeted abuse.

Respond: Coordinate across AI/machine learning teams, site reliability engineers, legal and security to investigate and contain AI-specific incidents like data leakage or unauthorized tool invocation. Update response plans to reflect LLM threats and recovery protocols.

Recover: Restore compromised AI services, rollback fine-tuned models or agent states, redact unsafe memory or embeddings. Update retrieval-augmented generation sources and retrain if training data was involved in the breach. Conduct post-mortems with AI-specific lens.

Beyond the content in the AI IRF, what broader considerations should organizations keep in mind when updating their incident response playbooks to address AI incidents?

Several thoughts on this:

In a large enterprise, establishing a new, standalone process for AI incident response could create unnecessary burden and confusion. If the organization already has a functioning incident response playbook, it is better to fold a new sub-procedure for AI incidents into that playbook, particularly since AI incidents may readily overlap with other incident response areas, such as cyber, privacy and IP.

Leverage the organization's incident response muscle memory

When deploying for AI incidents, leverage the organization's muscle memory for incident response. Aim to draw from the same leadership team. Follow the same process for identifying the crisis commander, cascading communications, engaging third party vendors and establishing privileged workflows. Also, bear in mind the board may be particularly keen for updates, particularly if AI is core to the organization's strategy.  

It is helpful to consider going deeper into preparation for the more sensitive AI use cases. Key questions can include how are we really set up to detect an AI incident in this context? What monitoring could we configure to identify output anomalies or other unusual system behavior? If we had an incident, what would we do to capture and preserve ephemeral AI artifacts and evidence before they are lost, such as prompts, outputs, session context, agent memory and tool-call history? How would we go about assuring containment so as to limit the model’s ability to continue generating or acting on unreliable outputs, particularly in cases of data poisoning or algorithmic bias?

Alongside elements of broader AI governance, it would be healthy at this time to start incorporating threads of AI incident response into vendor agreements. It also would be helpful to start to evaluate how target companies in mergers and acquisitions and other transactional contexts are addressing these types of issues.

Of course, it would be difficult for any organization to have a polished and tested AI incident response plan in these early stages. The key is to get started on the journey and continue pressing to enhance the program in light of experience, tools and resources available in the market, and other developments.

Special shout out to Lukas Feiler, CIPP/E, Melanie Condron and Lara Liss, who had a stellar presentation on this topic at IAPP Privacy. Security. Risk. + AI Governance Global 2026, specifically focusing on AI Incident Response: Obligations of Providers and Deployers When AI Fails. Great content and thought leadership.