AI Risk Management: A Framework for Enterprise AI Risk
AI risk management is the practice of identifying, assessing, and treating the risks that artificial intelligence creates for an organization, before those risks turn into incidents. As AI moves into decisions that affect customers, finances, and compliance, managing its risk has become a core discipline rather than an optional one. This guide explains what it […]
Shadow AI Detection: How to Find and Govern It
Shadow AI detection is the practice of finding the unapproved AI tools and services employees are already using across an organization, so they can be brought under governance. You cannot govern what you cannot see, which makes detection the first practical step in any shadow AI program. This guide explains how shadow AI detection works, […]
EU AI Act Timeline: The New Deadlines After the 2026 Omnibus
The EU AI Act timeline just changed. On 16 June 2026, the European Parliament approved a package of amendments known as the digital omnibus that postpones the heaviest obligations of the AI Act by one to two years, delays one transparency requirement, and adds a new prohibition. The changes still need formal adoption by the […]
Enterprise AI Governance: A Guide for Boards and Leadership
Enterprise AI governance is the way an organization’s board and senior leadership direct, oversee, and remain accountable for the use of AI across the entire business. As AI moves into decisions that shape revenue, risk, and reputation, oversight of it has become a board-level duty rather than a technical detail. This guide explains what enterprise […]
AI Audit: What It Covers and How to Prepare
An AI audit is an independent review of how an organization builds, uses, and governs artificial intelligence, measured against a standard, regulation, or risk framework. As AI takes on more consequential decisions, this kind of review has become the way organizations prove their AI is controlled, fair, and compliant. This guide explains what it covers, […]
Labeling AI-Generated Content: What the EU AI Act Requires
Labeling AI-generated content is about to become a legal requirement in the European Union. In June 2026, the European Commission published a voluntary Code of Practice to help organizations meet the AI Act transparency obligations that apply from 2 August 2026. This article explains what must be labeled, what the new Code does, and what […]
AI Acceptable Use Policy: How to Curb Shadow AI
An AI acceptable use policy is the document that tells employees which AI tools they may use, what data they may enter into them, and what they must never do. It is the single fastest control an organization can put in place to curb shadow AI, the unapproved use of AI tools that spreads quietly […]
How to Build an AI Governance Framework
An AI governance framework is the set of policies, roles, and processes an organization uses to direct and control how it develops, buys, and uses artificial intelligence. Without one, AI decisions happen in scattered pockets across the business, often with no one accountable for the risk. This guide explains what an AI governance framework includes, […]
NIST AI RMF Explained: A Practical Implementation Guide
The NIST AI RMF, short for the National Institute of Standards and Technology AI Risk Management Framework, is a voluntary framework that helps organizations manage the risks of artificial intelligence across its full lifecycle. Released in 2023 and expanded since through companion profiles, it has become a common reference point for building trustworthy AI. This […]
Agentic AI Security: How to Govern Autonomous AI Agents
Agentic AI security is the practice of managing the risks of autonomous AI agents, systems that do not just generate output but take actions on their own, such as sending emails, moving data, or executing tasks across other software. As organizations deploy these agents, the security and governance questions grow sharply, because an agent that […]