Enterprise AI Security and Governance Summit 2026

Traditional security and compliance programs were built for deterministic systems, but AI introduces probabilistic behavior, model drift, and output risk that standard controls do not fully contain. This session explores the compliance gap senior leaders are now facing: existing cloud, SaaS, and data governance practices can cover some AI risks, but not the full set of issues created by generative systems, autonomous workflows, and changing model behavior. The practical challenge is not whether to govern AI, but how to govern it without pretending yesterday’s control framework still fits today’s risk profile.

We will connect governance design to real regulatory and operational pressure points: privacy, auditability, human oversight, documentation, and sector-specific obligations, with a focus on executive decision-making, acceptable experimentation and uncontrolled exposure, non-negotiable minimum controls, and how to avoid the false comfort of “AI-ready” labels that mask weak enforcement. Senior leaders will come away with a clearer lens for questioning whether their current compliance posture can actually withstand AI-assisted decisioning.

Join Obuks Akpede, Data Governance Consultant at Adastra Corporation, to learn a practical maturity rubric that helps leaders benchmark proactive governance against reactive approaches.

Key Takeaways:
- Why AI risk splits into infrastructure-layer risk and operational probabilistic risk.
- Which legacy controls still work for AI and where they stop being sufficient.
- How to translate AI governance into audit trails, logging, oversight, and regulatory evidence.
- What minimum enterprise maturity should exist before broader AI adoption is allowed.