Security incident records may feed vendors’ AI model development

Dark Reading reports that security incident records—closure notes, dispositions, and alert decisions—are increasingly being ingested into security AI systems with permission to use data for model building or validation. The piece explains that while these records traditionally supported accountability and post-incident reconstruction, they can become data inputs for vendor models, potentially exposing sensitive evidence outside the customer’s network.

The authors emphasise that a single closure can be harmless, but a series of records forms a map of signals treated as meaningful, suppressed detections, and analyst rationales, which could reveal how detections were tuned and where gaps exist.

The article, by Gerald Mako and Lemuel Valdez for Dark Reading, notes concrete options and risks around vendor use of customer data. It highlights Microsoft Security Copilot as an example: customers can allow data capture for model improvement or product-performance reviews, with Microsoft stating it does not use such data to train the foundation model or share it with OpenAI, though default settings and provisioning methods vary by workspace setup.

The piece argues that, unless procurement contracts explicitly separate retention for the organisation’s archive from data used for model training, incident records could end up outside the customer environment and be exposed under legal or breach scenarios. It calls for explicit, revocable choices and clear distinctions between evidence retention, model development, and vendor services, to prevent the archive from taking on a second, external job.