Cyber Incident Response in eDiscovery: Collecting AI Tool Logs After a Breach

AI tools are increasingly the target of a breach, not just a bystander to one. When incident response teams scope a breach today, AI prompt and output logs are frequently the exact data that was exposed, and just as often the exact data nobody thought to collect.

AI tool logs breach investigation refers to the process of identifying, preserving, and collecting the prompts, outputs, access records, and metadata associated with AI tool usage as part of a cyber incident response, treating that data with the same rigor applied to server logs, authentication records, and email during a breach. Skipping this step leaves a gap in the investigation exactly where the exposure may have originated.

Why AI Tool Logs Are Now Central to Breach Investigations

The business case for including AI systems in incident response scope is measurable. IBM's 2025 Cost of a Data Breach Report found that organizations identified and contained breaches in a mean time of 241 days, the lowest figure in nine years, an improvement the report attributes largely to faster detection and containment powered by AI-enabled security tools. That gap reflects visibility. Organizations that have already built AI activity into their monitoring and response processes detect and scope incidents faster than those treating AI tools as outside their normal logging perimeter.

The legal risk compounds the technical one. A May 2026 ruling in Conservation Law Foundation v. Shell Oil Company found that an expert's AI prompts were part of her discoverable methodology under Federal Rule of Civil Procedure 26, rejecting the argument that a separate discovery stipulation protecting draft materials shielded the prompts from production. The same logic extends to incident response: if AI tools were used to analyze a breach, draft findings, or summarize exposed data, the record of how that work was done may itself become relevant and discoverable later.

What to Collect When an AI Tool Is Implicated in a Breach

Prompt and Output Logs

The full conversation history, not just a final summary or report, is often what reconstructs what data an AI tool actually processed and when. Losing this history makes it difficult to establish the scope of an exposure with any precision.

Access and Authentication Logs

Who accessed the AI tool, from where, and under which credentials matters as much for an AI system as it does for any other application implicated in a breach. These logs establish the timeline investigators need to determine how an exposure occurred.

Data Activity Monitoring Signals

Unusual patterns, such as large-scale exports from an AI tool's connected data sources or access outside normal working hours, often provide the earliest indication that something is wrong. Onna's guidance on data activity monitoring as an early warning system explains how these signals can surface a developing incident before it escalates into a full breach, giving legal and IT teams a head start on scoping the investigation.

Building AI Logs Into Incident Response Data Collection

Extend Legal Holds to Cover AI Activity

Legal hold notices issued during a breach investigation need to name AI tools explicitly, the same way they would name email systems or file servers. Onna's guidance on identifying AI-generated content in data and preserving AI-generated content in collaboration platforms both cover the practical mechanics of making sure this content is not overlooked or auto-deleted while an investigation is underway.

Centralize Collection Across Review Platforms

Breach investigations move fast, and incident response data collection needs to feed directly into the review and analysis work that follows. Onna and Reveal connect collaboration and AI tool data collection directly into Reveal's review environment, so investigators are not manually transferring exported logs between disconnected systems while a breach is still active.

Practical Steps for Incident Response Teams

1. Inventory every AI tool with access to sensitive or regulated data, including tools introduced informally by individual teams.

2. Add AI systems to the standard incident response data collection checklist, alongside servers, endpoints, and email.

3. Issue legal holds that explicitly name AI prompts, outputs, and access logs, not just general system logs.

4. Correlate AI activity logs with other breach timeline evidence to establish a complete picture of what was accessed and when.

5. Preserve this data before retention policies delete it, since many AI tools default to short retention windows that can run out well before an investigation concludes.

Treating AI Systems as Part of the Incident Response Perimeter

The organizations getting ahead of this risk are the ones that stopped treating AI tools as separate from their incident response scope. Once an AI system has touched sensitive data, its logs are evidence, whether the breach happened because of the tool or simply passed through it.

If your organization needs a defensible way to collect AI tool logs during a breach investigation, Onna's team can walk through the right approach for your environment. You can also see how this works in practice by requesting a demo.