Securing AI agents in the enterprise requires controls beyond the model
Microsoft shares recent lessons from deploying and securing AI agents inside its own enterprise environment. The focus extends beyond model protection and prompting to identities, permissions, governance, monitoring and controlled system access. For companies, the message is significant: as soon as an agent can read data, invoke tools or trigger actions, it becomes a security-relevant part of the IT architecture.
Why it matters: This is where a simple AI assistant becomes a production agentic system. Identity, least privilege, monitoring and traceable approvals become foundational technical controls.
Read the original Microsoft article →