Learn what a model card report is and how it supports responsible AI by providing transparency and accountability in AI model development and deployment.

AI governance is how organizations manage AI risk and ensure responsible use. This guide breaks down what good governance looks like—inventory, risk, compliance, oversight—and compares 6 AI governance software platforms and what they are best positioned for.

Discover how ISO 42001 is reshaping AI governance. Explore its core principles, global relevance, and how to move from principles to certification readiness—structuring controls, assessing system-level risk, and aligning AI management with certification goals.

Guru Sethupathy explores why AI risks—such as bias, misuse, and security failures—are rooted in the application layer. He emphasizes that robust AI governance and guardrails at the app level are critical to unlocking value while avoiding hidden AI pitfalls.

AI regulation is accelerating—from the EU AI Act to emerging U.S. enforcement. This article breaks down four key developments in 2025 that will shape how organizations govern, test, and deploy AI across high-risk and enterprise-wide use cases.

Artificial intelligence governance is no longer just a safeguard. It’s how leading organizations reduce risk, accelerate adoption, and build trust—especially in high-stakes sectors like finance, healthcare, and HR where the cost of failure is high.