AI compliance means meeting the legal, regulatory, and contractual obligations that govern how an organization develops and uses artificial intelligence. In 2026 that landscape moved quickly, with the EU AI ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
FedRAMP continuous monitoring is about to change, and the timeline is short. In June 2026, FedRAMP issued two Public Notices that reshape how cloud service providers maintain a FedRAMP certification: ...
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. ...
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 ...
