AI Agent Access Control Glossary

Access control enforcement for AI agents is the category of security infrastructure that evaluates and enforces access decisions at the agent tool-call layer, at execution time, based on policy, task context, and scoped credentials.

Execution-time access control evaluates whether an AI agent action is allowed at the moment the agent attempts to invoke a tool, using the current task context, policy, intent signal, and agent identity as inputs to the access decision.

Runtime access control is the practice of making and enforcing access decisions while a workload is executing, as opposed to at configuration, provisioning, or deployment time.

The runtime enforcement layer is the infrastructure component that intercepts agent tool calls at execution time, evaluates access policy, issues scoped credentials, and allows or denies each invocation.

Per-tool authorization decides whether an agent may invoke a specific tool, rather than granting or denying access to an entire server, session, API, or account.

Per-task authorization ties access decisions to the specific task an agent is executing, so that an agent permitted to read customer data for a summarization task is not automatically permitted to do the same for an unrelated task.

Per-call enforcement means that every individual tool invocation by an AI agent is evaluated against access policy at the moment of the call, rather than relying on session-level or configuration-time grants.

Just-for-task access is the principle that an AI agent should receive only the access needed for the specific task it is currently executing—and no more—with credentials that expire when the task ends.

Scoped runtime access is the practice of granting an AI agent only the access it needs during execution—dynamically scoped to the current task, tool, and runtime policy context—rather than pre-provisioning broad static access.

Scoped ephemeral credentials are short-lived credentials narrowed to a specific task, tool action, resource, and runtime policy boundary—combining the least-privilege properties of scoping with the limited-exposure properties of ephemerality.

Policy and intent-driven access is the combination of formal access policy and semantic intent evaluation to produce access decisions for AI agent tool calls—considering both what the agent is permitted to do and what it is trying to accomplish.

The MCP authorization gap is the missing layer between authenticating access to an MCP server and authorizing a specific tool call for a specific task—a gap that leaves agent tool invocations ungoverned.

Tool calling lets an agent invoke tools using a protocol like MCP. Authorization decides whether the agent should be allowed to use a specific tool right now—a decision that tool-calling protocols do not make.

Agentic IAM is identity and access management designed for agents that reason, choose tools, delegate work, and act dynamically during execution—going beyond static role assignments.

AI agent identity is the persistent or ephemeral identifier that represents an autonomous AI agent as an acting principal within a system, separate from the human user or service that initiated the agent.