AI Security & Governance Insights

Shadow AI was about which tools your team used. Agentic AI asks a harder question: which autonomous agents are acting on your systems, and who authorized them?

Using AI to screen or rank candidates? The EU AI Act likely makes that high-risk — even if you're outside the EU. What it means for your company, and what to do now.

Traditional DLP catches structured data leaving over defined channels. AI exfiltration is none of those. Here's what AI-native data loss prevention looks like.

The NIST AI RMF is the US standard for governing AI. Here's how to implement its four core functions with practical controls — without a dedicated AI team.

Most AI governance reports lose the board on slide one. Here's how to frame AI risk in terms boards respond to, which metrics to include, and what to expect.

Enterprise clients now add AI questions to vendor security questionnaires. Here are the five categories they ask — and what a defensible answer looks like.

SOC 2 auditors are now asking about AI tool usage, data handling, and access controls. Here is what they want to see and how to build the evidence trail.

Microsoft Purview is strong DLP and data classification. But it was built for files and email — not AI prompts. Here's where the gap is, and why it matters.

Law firms, consultancies, and accounting practices face unique AI governance requirements. Here is a practical checklist of the controls that matter most.