Foundational educational content
Clear, executive-grade explanations of the core concepts that underpin every AI governance program: governance, trust, and responsibility.
When AI Cyber Risk Becomes a Financial Stability Risk
The Financial Stability Board has elevated frontier AI cyber risk to the G20 agenda. The warning is not a prediction of imminent crisis or a new Canadian law. It is a signal that responsible AI governance must extend beyond policies and model reviews to third-party concentration, incident preparedness and operational recovery.
When AI Capabilities Change, Governance Must Change With Them
The AI vendor may be the same, but the risk may not be. New frontier capabilities, customer-controlled safeguards and pro-innovation policy signals show why organizations need governance that responds to material change—and evidence that controls work in practice.
What the Anthropic–Pentagon Ruling Means for AI Governance
A U.S. federal judge's decision to block the Pentagon's blacklisting of Anthropic does not certify Claude for every government use. Its more important lesson is that AI procurement decisions—including vendor restrictions, safety conditions and risk designations—require evidence, defined authority, due process and continuing review.
When AI Goes Wrong: 8 Real-World Lessons in AI Governance
Eight real-world cases involving organizations such as Air Canada, Samsung, Amazon, McDonald's and others reveal an important lesson: AI adoption without effective oversight can expose organizations to legal, operational, reputational and regulatory risk. Explore what happened—and the governance lessons executives can learn from these incidents.
What Is AI Governance?
AI governance is the operating system organizations use to direct, oversee, and account for the AI they deploy. It is a discipline, not a document.
What Is AI Trust?
AI trust is the demonstrable confidence that stakeholders — customers, regulators, employees, and boards — place in an organization's use of AI. It is the outcome that governance is meant to produce.
What Is Responsible AI?
Responsible AI is the set of principles, design practices, and controls organizations use to ensure AI is fair, transparent, accountable, safe, and respectful of human oversight. It is operational, not aspirational.
The EU AI Act is the world's first comprehensive horizontal AI regulation. It categorizes AI systems by risk (prohibited, high-risk, limited-risk, minimal-risk) and imposes obligations on providers, deployers, importers, and distributors operating in or selling into the EU.
ISO/IEC 42001 is the first international management-system standard for artificial intelligence. It defines requirements for establishing, implementing, maintaining, and continually improving an AI management system (AIMS), with certifiable controls across governance, risk, lifecycle, and operations.
The NIST AI Risk Management Framework is a voluntary, widely adopted framework for managing risks across the AI lifecycle. It is organized around four functions — Govern, Map, Measure, Manage — and is paired with the Generative AI Profile for foundation-model risks.