Runtime access control for AI agents

Key takeaways

• Runtime access control applies authorization policy when an access request or sensitive action is attempted, not only when a session begins.
• Runtime policy defines the rules, action context supplies relevant facts, a policy decision point computes the authorization decision and an enforcement point applies it.
• Agent authorization may need to account for the originator, acting agent, task or purpose, tool, target resource, requested action, risk, time and approval state.
• Tool-level controls and target-system entitlement controls perform different jobs. Blocking a tool call does not automatically remove privileges already present in the target system.
• P0’s approach connects agent discovery and identity context with runtime policy, supported enforcement and evidence across the action chain.

What question does runtime access control answer?

When a sensitive action is attempted, runtime access control asks whether a particular identity, acting directly or through an AI agent, may use a particular tool to take a particular action on a particular resource for the current task and under the current conditions. The answer may change during a workflow as the action, resource, risk or delegated context changes. Runtime access control therefore governs current authorization rather than treating all access available to the agent as authorization to use it now.

Runtime agentic access control describes this model as applied to agent workflows. The broader category is agentic identity security.

Why do AI agents create a distinct access-control problem?

Traditional access programs commonly assign permissions to a user, role, group or service account in advance. Those permissions may remain available well beyond the task for which they were needed.

AI agents inherit this existing problem and amplify it. An agent can authenticate once, plan several steps, invoke multiple tools and change several systems without a person reviewing every action. A support agent might read a customer record, call a billing API and update a system of record within one task. A valid session does not establish that every action inside it is authorized.

Agents create three connected access-control challenges:

• Standing privileges and static credentials: Long-lived credentials can give agents more access than a task requires. Short-lived, task-scoped permissions reduce the risk of unintended or abusive actions.
• Rogue actions: Agents may have access to tools that a specific user or task should not be allowed to use. Tool access should reflect who initiated the action and the context of the request.
• Incorrect attribution: Agent and application logs often show which account took an action, but not the user or agent behind it. That breaks provenance and makes audit and forensics harder.

How broad permissions increase the impact of an agent action

The potential impact of an agent action depends on the systems, data and permissions the workflow can reach. Based on the disclosed details, P0 interprets the Composio breach as an example of this access-control risk. Attackers reportedly exposed more than 5,000 cached API keys and OAuth tokens across customer environments. The access-control lesson is not about model intent. Broad permissions and accessible credentials increased what could be reached when the workflow was compromised.

How does runtime access control work?

Runtime access control combines runtime policy, current action context, authorization decisioning and enforcement. Policy determines the permitted outcome, while an enforcement point applies it to the action or permissions involved.

Runtime access control connects four distinct functions:

1. Runtime policy defines the conditions under which an action may proceed.
2. Action context supplies the facts relevant to the current request.
3. Authorization decisioning evaluates the context against policy and produces an outcome.
4. Enforcement applies the outcome at a control point capable of allowing, limiting, stopping or revoking the action or access.

These functions can be coordinated through an authorization control plane for AI agents, which centralizes policy and decision-making while distributing enforcement across relevant control points.

This separation matters. NIST SP 800-162 distinguishes the policy decision point that computes an authorization decision from the policy enforcement point that applies it. NIST SP 800-207 similarly separates policy decision components on the control plane from enforcement in the data plane. The policy decision point determines the outcome, while the enforcement point applies it.

What is runtime policy?

Runtime policy is a set of evaluable rules applied when access or an action is attempted. It can express conditions such as:

• which agent may act for a particular originator
• which tool the agent may invoke
• which operation is allowed on which resource
• whether the action serves an approved task or business purpose
• whether current risk, time or environmental conditions are acceptable
• whether the action can proceed automatically or requires review
• how long temporary privileges may remain active

Runtime policy does not mean every action needs human approval. Routine actions that satisfy policy can proceed automatically. Review is one possible decision outcome for exceptions or higher-consequence actions.

What is action context?

Action context is the set of current facts available for policy evaluation. Relevant inputs may include:

Not every input is mandatory for every decision. The inputs should match the sensitivity of the action and the capabilities of the workflow and target system.

What decisions can the system make?

After evaluating policy, an authorization system may:

• Allow the requested action as submitted.
• Limit the action to a narrower resource, operation or duration.
• Require review before the action proceeds.
• Deny the action.
• Require revocation of access that should no longer remain active.

The appropriate outcome depends on policy and current context, not merely on whether the agent possesses a usable credential.

Runtime decision example

Consider a customer-support representative who asks an AI agent to correct a customer’s billing address.

The support representative is the originator: the person who initiates the task and delegates a limited scope of action. The AI agent is the acting agent: the software that selects the billing tool and attempts the update on the representative’s behalf.

The authorization system evaluates both identities and the delegation between them. It also considers the assigned task, requested tool, target customer record, field being changed and current conditions. Policy may allow the action automatically if the representative is permitted to manage that customer’s account, the agent is authorized to perform address corrections on the representative’s behalf and the request is limited to the billing-address field.

If the AI agent instead attempts to export all customer records, change bank details or access an account outside the representative’s assigned scope, the requested action no longer matches the permissions delegated by the originator. The authorization decision may limit, require review or deny the action even though the same representative initiated the task and the same agent, tool and authenticated session are involved.

This illustrates the difference between available permissions and current authorization. A credential or tool may make an operation technically possible, but runtime policy determines whether this AI agent, acting for this originator, is authorized to perform that specific operation now.

Runtime access control versus static permissions

Static permissions establish what an identity is generally able to access in advance. Runtime access control determines whether a specific access request or sensitive action is authorized under the current conditions. The approaches can work together, but they answer different questions.

Runtime access control does not make roles or pre-existing entitlements irrelevant. Those controls can contribute information about identity, permissions and business context to policy evaluation. The distinction is that their existence alone does not settle whether a particular action should proceed.

How does identity travel through an agent workflow?

An agent often uses permissions associated with a human, a service account or another agent before a request reaches its target. Evaluating only the final credential can collapse that chain and obscure who authorized the task.

Standards can help transport identity and delegation context:

• OAuth 2.0 Token Exchange (RFC 8693) enables one service to exchange a token for another token that can represent delegated context.
• The Model Context Protocol authorization specification defines an OAuth-based authorization model for protected MCP servers and clients.

These mechanisms move or represent authorization-related context. They do not, by themselves, determine whether a specific downstream action complies with enterprise policy. Runtime authorization uses the available context to make that decision.

Blended identity context is the model for carrying the identities and delegation context into policy evaluation. It does not mean copying all of the originator’s permissions to the agent or restricting every decision to the intersection of permissions both identities already hold. Policy may authorize narrower, temporary permissions for the task.

Where is runtime access control enforced?

Enforcement must occur at a point capable of controlling the requested action or the permissions used to perform it. In an agent workflow, enforcement points can include:

Decisioning and enforcement remain distinct across these layers. A policy engine evaluates relevant context, including the relationship between the originator and acting agent, and produces an authorization decision. A gateway, proxy, application control or target-system integration applies that decision at the control point it governs.

Two forms of enforcement are especially important:

• Action-level enforcement allows, limits, pauses for review or blocks a specific operation before it executes.
• Target-system entitlement enforcement provisions, limits or revokes the privileges available inside the target system, where native entitlement changes are supported.

Action-level and target-system entitlement enforcement govern different aspects of access. Controlling a tool call does not automatically narrow a broad target-system credential or remove persistent entitlements after the task. Evidence should connect the relevant identities, request, policy, authorization decision, enforcement result and action outcome.

What is the full action chain?

P0 uses full action chain to describe the complete path an agentic task follows from its originator to its outcome. Depending on the workflow, it may include:

1. Originator: The human, service account, workload or agent that initiates or delegates the task
2. Acting agent: The agent that executes or coordinates the task
3. Tool: The API, MCP server, plugin or connector used to complete the task
4. Target resource: The application, data store, cloud resource or other protected system the tool reaches
5. Requested action: The command, query, change or transaction being attempted
6. Outcome: What happened, under which policy and using what permissions

Examining the full action chain helps security teams determine which permissions are involved, how delegation context is preserved, where authorization decisions must be enforced and what evidence must be captured. It also reveals where identity context can be lost, permissions can become excessive or access can continue beyond the task that justified it.

In multi-agent workflows, each delegation introduces another acting identity and potential control boundary. The original task and delegation context must remain traceable, while the privileges available to each downstream agent remain bounded by policy.

How does runtime access control differ from adjacent controls?

Runtime access control overlaps with several technologies, but their control points and responsibilities differ.

These controls can complement one another. Discovery establishes what exists and how access is assigned or delegated. Identity systems supply trusted identity information. Token standards carry context. Runtime policy and enforcement determine what may happen now.

What does runtime access control enable?

Runtime access control helps organizations:

• Limit the impact of mistaken, manipulated or compromised agent actions
• Preserve originator and delegation context across workflows
• Allow routine, policy-compliant actions to proceed automatically
• Prevent technically available privileges from being treated as current authorization
• Remove task-specific permissions when they are no longer justified
• Produce evidence connecting the requester, policy, decision, action and outcome

What does effective runtime access control require?

An effective implementation should be able to:

1. Give agents and relevant workloads distinct, verifiable identities.
2. Preserve originator and delegation context across service boundaries.
3. Evaluate task or purpose along with identity, tool, target and requested action.
4. Keep authorization policy outside the agent’s own decision logic.
5. Apply authorization decisions at control points capable of stopping or limiting the action.
6. Limit permissions to the task and remove temporary access when it is no longer justified, where supported.
7. Reserve human review for policy-defined exceptions and high-consequence actions.
8. Capture evidence that connects the request, policy, decision, approval, action, outcome and revocation as applicable.

Prompt instructions and model guardrails remain useful, but they are not substitutes for authorization policy enforced outside the agent. The agent should not decide whether its own action is authorized.

How P0 approaches runtime access control for AI agents

The P0 AuthZ Control Plane™ is the runtime authorization layer for centralized sensitive access across AI agents, users and non-human identities.

For agent workflows, P0’s approach connects three functions:

• Discover: Identify relevant agents, access paths, identities and standing privilege.
• Control: Evaluate runtime policy and apply authorization decisions at supported enforcement points.
• Prove: Preserve evidence connecting the requester, agent, policy, decision, action and outcome.

The architecture combines centralized policy with enforcement in the customer’s environment. A self-hosted gateway can intercept supported agent actions, while the SaaS AuthZ Control Plane manages policy, approvals and governance. Where supported, target-system integrations can also provision and revoke narrow entitlements rather than stopping at the tool boundary.

P0 Security delivers runtime access control for AI agents by evaluating policy when sensitive actions are attempted and applying the resulting authorization decisions at supported enforcement points.

Summary

Runtime access control reduces reliance on broad permissions granted in advance by evaluating policy when access or a sensitive action is attempted. Runtime policy defines the rules, action context supplies the relevant facts, an authorization system determines the outcome and enforcement points apply it.

For AI agents, this model helps preserve delegation, constrain permissions and produce evidence across workflows that span identities, tools and enterprise systems. It also distinguishes what an agent can technically reach from what it is authorized to do now.

Frequently asked questions

What is runtime agentic access control?

Runtime agentic access control describes the application of runtime access control to agent workflows. Policy is evaluated using the relevant originator, agent, task, tool, target, action and current context when access or a sensitive action is attempted. An enforcement point then applies the authorization decision.

Does every AI agent action require human approval?

No. Policy-compliant actions can proceed automatically. Human review is one possible outcome for exceptions or higher-consequence actions.

Is runtime access control the same as just-in-time access?

No. Just-in-time access makes narrow, task-scoped permissions available when needed and removes them when the task ends, where supported. Runtime access control is the broader model for determining whether a particular access request or sensitive action should be allowed, limited, reviewed, denied or revoked under current policy and context.

Is runtime access control the same as conditional access?

No. Conditional access commonly governs sign-in or session conditions. Runtime access control evaluates whether a particular action within a workflow should proceed.

Can an MCP gateway provide runtime access control?

An MCP gateway can provide an important enforcement point for tool calls. On its own, however, tool filtering does not necessarily govern the resources and entitlements available to that tool inside the target system. Broader coverage may require policy evaluation and enforcement at multiple layers.

What is blended identity context?

Blended identity context is P0’s model for carrying the identities and delegation context into policy evaluation. It does not mean that an agent simply inherits all of the originator’s permissions.

Does runtime access control replace Zero Standing Privilege?

No. Zero Standing Privilege is an operating model that aims to prevent sensitive privilege from remaining persistently assigned when it is not required. Runtime access control can support that model by determining when narrow, task-scoped permissions are justified and supporting their removal when the task ends.

Sources

1. NIST, SP 800-207: Zero Trust Architecture.
2. NIST, SP 800-162: Guide to Attribute Based Access Control Definition and Considerations.
3. IETF, RFC 8693: OAuth 2.0 Token Exchange.
4. Model Context Protocol, Authorization specification.
5. P0 Security, The Composio breach: Let’s stop blaming the agents. https://p0.dev/blog/the-composio-breach-lets-stop-blaming-the-agents/

Explore related topics

• How to secure AI agents across the full action chain
• Agentic identity security
• Authorization control planes for AI agents
• Just-in-time access
• Zero Standing Privilege