Control an AI agent’s access through its identity, tools, and connected systems—not through a prompt telling it what not to access. Give each agent only the data and actions required for its approved purpose, and make the systems it calls verify permission at the time of every action.
Start by defining the agent’s access boundary
Before enabling an agent to act, identify what it is, what it is allowed to do, and who is accountable for it. Include the agent’s model, tools, plugins, MCP servers, data sources, credentials, and downstream integrations in the inventory. Assign a named business or technical owner and an approver; record the agent’s purpose, approved data scope, dependencies, and operating environment. Revisit the inventory when its workflow, tools, data access, or hosting changes. Microsoft’s least-privilege guidance for AI agents and agent-risk guidance both emphasize managing agent access and risk as part of the system, not just as a prompt-writing task.
Document the boundary in terms an administrator can enforce:
- Purpose and owner: the task the agent is approved to perform and the person responsible for its access.
- Data scope: the specific classes of information and resources it may read or change.
- Permitted operations: the actions it may take, such as reading records, updating a specific field, or sending an approved notification.
- Environment and dependencies: where it runs and which tools, connectors, models, and integrations it relies on.
Give the agent a distinct identity and minimum permissions
The model’s ability to reason about information is different from the agent’s authority to retrieve or change it. Authorization should be enforced by deterministic identity, API, data-store, and tool controls. Give each agent a unique, auditable identity, then grant task-based roles or scopes rather than broad standing access. Use delegated or short-lived credentials where available, and review the agent’s effective access across its roles, tools, and downstream systems: several individually narrow grants can combine into broad reach.
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Deny unreviewed tools, cross-tenant integrations, and guest paths by default. A tool should receive only the permissions it needs for its function; do not give every connector the agent’s full permissions simply because the agent may need them in some tasks. Microsoft’s least-privilege guidance describes agent identities and role-based access controls as ways to limit access, while its shared responsibility guidance stresses authorization as an ongoing control.
Authorize every tool call against its target
Do not let the model decide whether an action is permitted. Allowlist the tools and operations an agent may use, and require the connected service to check the principal, target resource, and requested operation on each call. A permission check at login or session start is not enough: an agent may later act on a different resource or attempt a more consequential operation.
Microsoft Learn’s AI agent shared responsibility model puts the point plainly: “Authorization on every action, not only at session start. Recheck that this action, on this resource, is permitted.” Preserve the initiating user’s identity or delegated authority when an agent acts on that user’s behalf. Otherwise, a broad service identity could let the agent exceed the user’s own rights. The OWASP AI Agent Security Cheat Sheet also recommends constraining agent capabilities and treating tool use as a security boundary.
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Require a human approval or time-bound elevation for destructive, external, or otherwise high-impact actions. Approval should apply to the specific action and target, rather than granting the agent a general permission it can reuse indefinitely.
Separate sensitive data, memory, and untrusted input
Classify data and establish deterministic rules for which classes the agent may use, how long they may be retained, and whether they may appear in outputs. Isolate conversation context and persistent memory by user, session, and tenant. Keep persistent memory to what the task needs, protect it with access controls, and define retention and deletion rules.
Retrieved documents, web or external content, tool outputs, and messages from other agents are inputs, not trusted instructions. They can contain text that attempts to redirect the agent or obtain data outside its approved purpose. Keep authorization checks independent of that content; do not allow an instruction found in a document or tool result to expand access. These boundaries are consistent with the AWS guidance on secure generative AI agents, the OWASP agent security guidance, and Microsoft’s shared responsibility model.
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Keep autonomy observable and reversible
Set limits on how many steps an agent can take, how often it can retry, how long it can run, and how much it can spend. Review changes to models, tools, plugins, and grounding sources rather than allowing dependencies to change silently. Test for prompt injection and other adversarial inputs before production and after significant changes; the Microsoft agent-risk guidance and OWASP guidance cover these risk-management practices.
Maintain an audit trail that can answer who or what acted, with which effective role or scope, on what resource, and under which request. Include a correlation identifier and, when relevant, the user on whose behalf the agent acted. Avoid recording secrets or sensitive data in plaintext. Provide a dependable pause or stop mechanism, and test the full revocation path rather than assuming that disabling the agent is sufficient:
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- Disable the agent or stop its execution.
- Rotate its credentials and invalidate active tokens.
- Remove stale permissions from tools and connected systems.
- Verify that downstream services reject the agent’s identity after revocation.
Assign controls according to the deployment model
SaaS, PaaS, and self-hosted deployments shift operational responsibilities, but none removes the need to set the agent’s permitted data scope and use. The division below is illustrative: check the specific provider’s responsibilities and your own configuration before relying on it. Microsoft’s AI agent shared responsibility model describes the broad differences.
| Deployment | Typical provider role | Customer responsibilities to confirm |
|---|---|---|
| SaaS | The provider may operate orchestration, models, safety systems, and most connectors. | Configure identity, data scope, and usage; confirm how authorization, logging, memory, and revocation work in the specific service. |
| PaaS | The provider supplies a managed runtime. | Own more of the agent instructions, tool selection and permissions, orchestration, memory design, and identity configuration. |
| Self-hosted or IaaS | The provider supplies underlying infrastructure, depending on the arrangement. | Operate more of the agent stack and its controls, including the components managed by a SaaS or PaaS provider in other models. |
When comparing implementations, examine who controls identities and tokens; whether permissions are scoped to each task, tool, data source, and operation; how memory is isolated and retained; what approval and stop controls exist; what is logged and how revocation is tested; and how much orchestration and dependency governance your team must operate. Those dimensions help expose responsibility gaps; they do not establish a universal product ranking.
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