Adopting AI Without Putting Customer Data at Risk

Where the real risks are, where they aren't, and how to govern AI without slowing your teams down.

Who it's for: CTOs and VPs of Engineering, CIOs, product leaders, and AI and platform architects, along with the CISOs, DPOs and privacy officers responsible for how their teams use AI.

Most of the fear around AI and data privacy is aimed at the wrong things. Giving an LLM your codebase is not the same as giving it your customers' data. The bigger risks are quieter: tools your teams adopt without asking, and data flows nobody is watching.

The data agrees. IBM's Cost of a Data Breach Report 2026 found that AI-related breaches stem from governance failures, not the models themselves: 92% of affected organizations lacked proper AI access controls. And users are paying attention. In Luciq's own research, 72.4% of mobile app users named privacy and security as their top concern about AI in apps.

Mohamed Noseir, Luciq's Head of Information Security, shares how Luciq's own security team approves and governs the AI tools its teams use, and what other leaders can take from it.

You'll learn:

• Which uses of LLMs, agents and copilots actually lead to data leakage
• Why code access is not customer data access, and what that means for AI in your SDLC
• How to spot shadow AI and the uncontrolled data flows it creates
• How to log and audit AI access: who saw what, and when
• A checklist for finding privacy risks in production

Speaker: Mohamed Noseir, Head of Information Security, Luciq.

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