Intelligence tagged AI Governance

Most organizations believe they have a handle on how employees use AI at work, the data says otherwise. This article looks at why banning AI outright doesn't solve the problem, and why an AI acceptable use policy is the more effective starting point for managing the risk. Readers will come away with a clear picture of what that policy needs to include, and what a working program looks like beyond the document itself. 

Shadow AI is quietly reshaping enterprise risk. Employees and engineering teams alike are feeding sensitive data into unsanctioned AI tools, often without realizing that regulatory liability, source code exposure, and unchecked agentic access all follow. This piece breaks down what shadow AI actually means, where the legal and security exposure lands, and what due diligence looks like before a new AI tool ever touches your environment.

AI security means something different to a security team than it does to a governance team, and that mismatch is one of the biggest security risks of generative AI in practice. This article walks through six questions that expose where the disconnect happens. Each question comes with a practical answer, covering a key security concern for nearly every team currently trying to figure out how generative AI has affected security at the enterprise level.

AI risk doesn't belong to Security or Governance alone, it lives at the boundary between them. Join Echelon Risk + Cyber and Cranium AI on June 23rd as we break down why misaligned security and governance functions are leaving enterprises exposed, what that misalignment is costing, and how to close the gap. Walk away with six diagnostic questions and a 90-day roadmap to get your teams operating as one.

Most organizations don't have an AI governance problem. They have a visibility problem. Unknown tools, unclear ownership, and undocumented data use are already creating risk, they just haven't surfaced yet. Here's how to get ahead of it.