Protecting your organization’s mission-critical data is always a top priority. However, data security has gained additional importance in the AI era.

Enterprise security teams spent years building data loss prevention (DLP) programs around a predictable set of egress channels: email, USB drives, cloud storage, and sanctioned SaaS apps. Generative AI has rewritten those assumptions almost overnight. Today, the same data those DLP controls were built to protect is flowing into AI interfaces that most organizations have no visibility into and no enforcement capability over.

The fastest way to reduce risk at enterprise scale is to standardize on a vulnerability and exposure management platform that unifies asset visibility, prioritizes what matters, and automates workflow to remediate. In this article, we’ll break down the nine essential requirements security leaders should insist on when evaluating an enterprise vulnerability management system, whether it’s an existing tool in their tech stack or a potential new capability.

Among the many updates to Windows Server 2025, there’s a new Forest Functional Level (FFL) for Active Directory. This first new FFL since Windows Server 2016 underscores Microsoft’s long-term commitment to AD.

The difference between an AI feature and an AI-led operating model becomes clear the moment a security problem becomes difficult. In real-world security operations — where the signal is ambiguous, the evidence spans multiple domains, and the attacker is behaving in unfamiliar ways — architecture matters much more.