How to Prepare for an AI Audit in 9 Strategic Steps
Organizations that scramble to prepare for AI audits have the same underlying problem: governance was claimed, not built. This piece is for the compliance and risk professionals who want audit readiness to be a byproduct of their ongoing governance program, not a separate sprint. The structure is here. The documentation requirements are clear. What follows […]
5 Leading AI Governance Frameworks Every Organization Should Know
Most enterprise organizations don’t face one AI governance framework. They face several simultaneously, each with different requirements, different jurisdictions, and different documentation obligations. This piece is for the compliance and risk professionals who need to understand which frameworks apply to their organization and how to govern across all of them without building separate programs for […]
AI Governance Frameworks: NIST AI RMF, EU AI Act, and ISO 42001 Compared
The EU AI Act, NIST AI RMF, and ISO 42001 share enough common ground that one governance program can satisfy all three simultaneously. That’s the finding we see consistently across enterprise implementations: the overlap between these frameworks is substantial enough to eliminate most duplicated work, if the program is designed around shared controls from the […]
How to Establish an Effective AI Governance Committee in 2026
An AI governance committee is a cross-functional group responsible for setting policies, managing risk, and providing oversight for an organization’s AI adoption. It’s the structure that turns ad hoc AI decisions into repeatable, auditable governance. This guide covers who should serve on the committee, what responsibilities it owns, how to draft a charter, and the […]
Who Owns AI Governance: Roles and Responsibilities Explained
AI governance ownership typically falls to senior leadership and cross-functional teams rather than a single role. In most organizations, accountability sits with the CEO, Board of Directors, or Chief Risk Officer, while the actual work happens through collaboration between legal, security, and technology functions. The challenge is that no existing team was designed to hold […]
AI Governance Meets AI Insurance: How Trustible and Armilla Are Advancing AI Risk Management
As enterprises race to deploy AI across critical operations, especially in highly-regulated sectors like finance, healthcare, telecom, and manufacturing, they face a double-edged sword. AI promises unprecedented efficiency and insights, but it also introduces complex risks and uncertainties. Nearly 59% of large enterprises are already working with AI and planning to increase investment, yet only about 42% have actually deployed AI at scale. At the same time, incidents of AI failures and misuse are mounting; the Stanford AI Index noted a 26-fold increase in AI incidents since 2012, with over 140 AI-related lawsuits already pending in U.S. courts. These statistics underscore a growing reality: while AI’s presence in the enterprise is accelerating, so too are the risks and scrutiny around its use.