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What is a Model Card Report? Your Guide to Responsible AI
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, Explained
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.
AI Governance Playbook for ISO 42001 Certification
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.
Value And AI Risk Sit At The Application Layer
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.
Where Is The Artificial Intelligence Regulations Landscape Going?
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.
Leveraging AI Governance: Moving From Compliance Burden To Competitive Edge
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.