An AI agent isn’t defined by a single identity — it’s composed of many. Each brings its own access patterns, lifecycle requirements, and security risks. These identities can consume sensitive data, initiate actions, and operate autonomously. 

With AI, enterprises must now account for new classes of identities:

• AI Agents – copilots, bots, and reasoning engines that act on behalf of users.
• MCP Servers – orchestration layers connecting agents and models.
• Tools - external capabilities or functions that an AI agent can call to extend its reasoning and actions 
• Model Endpoints – APIs that serve LLMs, often versioned with different contexts.
• Agent Frameworks – platforms that define agent behavior.

What makes Identity Security for AI Agents different from broader NHI security?

Although AI agents are technically part of the non-human identity group, their autonomous nature and ability to make independent decisions introduce a level of dynamism that traditional approaches can’t fully address. Securing them requires strategies that recognize their unique role bridging human and non-human identities. Unlike static service accounts, these agents continuously learn, adapt, and interact across complex infrastructure and intelligence layers—leaving conventional NHI tools, built for fixed and predictable entities, inadequate for managing their risks.