Artificial intelligence is moving into business operations faster than most governance programs can realistically absorb.
Many organizations are building their AI governance programs around acceptable use policies, initial vendor reviews, and legal approvals. Those are necessary, but they are not enough.
The biggest operational risk is often not unauthorized AI—it is approved AI that quietly becomes something different over time. We call this “silent drift”.
A business unit adds a new data source, a vendor updates the underlying model, or a plugin is enabled. Suddenly, the AI system operating today no longer matches the system that was originally reviewed and approved.
To help GRC, Security, and Engineering teams bridge this gap, we have put together a new explainer video: The AIBOM Explained.
In this short video designed for executive leadership, we break down how an AI Bill of Materials (AIBOM) serves as the practical bridge between AI governance theory and operational control.
You will learn how to:
Manage Third-Party AI Risk: Move beyond generic vendor questionnaires and achieve component-level transparency into your AI supply chain.
Identify Silent Drift: Use AI inventories to catch unauthorized system changes, capability expansions, and altered data flows.
Build Defensible Evidence: Create a point-in-time record that proves to auditors, regulators, and leadership that AI risks are being continuously managed.
The real question is no longer simply, “Do we allow AI?”. The better question is: Can we prove what is inside our AI systems, what changed, and how it is being governed over time?
Watch the video above, and let us know in the comments: Does your organization have the visibility to detect silent drift in your AI systems?
📖 Read the full deep-dive strategy post here:
