AISPM takes the core concepts of CSPM, continuous discovery, assessment, and enforcement, and applies them to AI agents. The practice covers five capabilities.

Continuous discovery answers the question "what agents are running in my environment?" This sounds simple. In practice, most enterprises can't answer it. Development teams deploy agents through different frameworks, different cloud accounts, and different deployment pipelines. Shadow agents (deployed without security review) are common, particularly in organizations where the agent deployment process is slow or burdensome. Discovery tools scan for agent runtime signatures, MCP server registrations, API call patterns, and resource utilization that indicates agent activity.

Configuration assessment evaluates each discovered agent against security policies. What permissions does this agent have? What tools can it access? What data sensitivity levels does it touch? Is it running with least-privilege permissions, or was it provisioned with overly broad access as a shortcut during development? Configuration assessment compares the current state to the approved state and flags drift.

Permission risk evaluation goes deeper than configuration assessment to analyze the effective permissions an agent holds. An agent might have been provisioned with appropriate direct permissions but inherit additional permissions through tool chains. If an agent calls an MCP server that itself has elevated database permissions, the agent's effective permissions include that database access, even if it wasn't directly provisioned. Permission risk evaluation maps these transitive permission paths and identifies privilege escalation risks.

Policy enforcement at runtime moves security from assessment to action. When an agent attempts an action that violates policy, runtime enforcement blocks the action, logs the attempt, and alerts the security team. This requires an enforcement point in the agent's execution path, typically an AI gateway or policy enforcement proxy that intercepts agent actions before they reach backend systems. Learn more

Behavioral anomaly detection addresses the risks that static configuration assessment can't catch. An agent operating within its approved permissions can still behave anomalously. A sudden spike in data retrieval volume. A change in the pattern of tool calls. Access to data categories that the agent has permissions for but has never accessed before. These behavioral signals can indicate compromise, drift, or misuse, and they require baseline modeling and continuous comparison.

Together, these five capabilities form the AISPM practice. No single tool covers all five today, though vendors like Palo Alto Networks, Wiz, and emerging startups are racing to build comprehensive platforms. In the near term, most enterprises will assemble AISPM from a combination of existing security tools (CSPM, SIEM, PAM) extended with agent-specific capabilities and new agent-native security tools.