Category: Best Practices

AI agents need identity controls, scoped access, and runtime enforcement before they are trusted with production systems.
AI agents need more than working credentials. They need verifiable identity, task-scoped access, and clear attribution.
As AI moves from chat windows to enterprise systems, data leakage becomes an identity and access problem.
Workforce and customer agents may rely on similar identity infrastructure, but the trust models, access patterns, and security risks behind them differ significantly.
AI agents exchange sensitive contexts across MCP servers in seconds. Without context-aware auditing, you can’t trace who accessed what.
NHIM, MIM, and workload IAM each address a different layer of non-human identity security. Learn how they compare and complement each other.
Secrets managers store credentials but can’t close the access gaps that multicloud workloads and AI agents create. Five alternatives can.
MCP standardizes how AI agents connect to tools, but every agent needs delegated authority and precise permission controls to match.
Hardcoded credentials and shared tokens give attackers ongoing access. Dynamic authorization replaces them with real-time decisions.
Two layers protect cloud-native apps: Workload IAM secures machine identity and API security inspects request traffic. Most teams need both.