Agentic AI and Automation Governance: Practical Controls for Data Risk

In enterprises environments, the biggest automation and AI risk is not “AI hype.” It is unmonitored Python running across endpoints and teams, quietly touching company data through internal APIs, databases, and shared drives. Security and GRC need more than policies. They need real-time evidence of what code is running and what it is doing. This session shares a practical governance approach to reducing data risk without blocking productivity: live inventory of Python execution, behavior visibility, data-touch mapping, and audit-ready reporting. If any business-user code touches company data, you should know.

What you will learn:

• See the real problem: where enterprise automation (often Python) creates data exposure that traditional tools do not make explicit (application programming interfaces, databases, shared drives).

• Implement practical controls: how to move from policy to enforcement with a lightweight governance workflow (discover-understand-assess-report).

• Be audit-ready fast: what evidence auditors and GRC teams need — execution proof, ownership, and data-touch context.

Sponsored by: BotCity