Data Leak Prevention – the Foundation of Sustainable AI Adoption

Every organisation racing to adopt AI runs into the same quiet problem: the moment your data becomes useful to a model, it also becomes easier to lose. Prompt histories, connected repositories, copilots with generous permissions — the surface area for leakage grows exactly as fast as your ambition does.

This one-hour session makes the case that data leak prevention isn’t the brake on AI adoption. It’s what keeps your foot on the accelerator. We’ll treat DLP not as a compliance checkbox bolted on after the fact, but as the load-bearing layer that decides whether your AI program scales or stalls — and reframes security teams from the department that says “no” to the one that makes “yes” safe to say.

Built for IT and security practitioners, the talk covers where AI actually exposes data, what “good enough” DLP looks like before you roll out copilots and agents, and how to sequence protection so it enables rather than obstructs adoption. Followed by open Q&A.