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AI agents can access any data exposed by their runtime, tools, connectors, credentials, delegated user session, and network. That can include local files, cloud documents, email, databases, SaaS records, secrets, and destinations on the public internet. A prompt is not a security boundary. Restrict an agent with a separate identity, deny-by-default permissions, read-only scopes, short-lived credentials, connector and egress allowlists, external policy enforcement, detailed logging, and tested revocation.
What determines what an AI agent can access?
An agent has no inherent right to see data, but it can use every right granted to the process and tools running it. The effective boundary is the union of permissions across all connected systems, not the text in the model prompt. A seemingly narrow agent can gain broad reach when one tool can call another service, when a connector inherits a user’s permissions, or when a shell process can read a directory containing credentials.
Review access as a chain: the agent’s runtime identity, operating-system permissions, tool definitions, connector scopes, API and database roles, delegated user context, secret stores, and outbound network routes. The least restrictive link can determine the blast radius. Adding a new connector or tenant may therefore change access even when existing grants are unchanged.
Which kinds of data can an agent reach?
Local and hosted files
An agent can read, create, alter, move, or delete files visible to its process or to a file-management tool. Visibility may include a local workstation, a container volume, a virtual machine, a shared drive, or an object-storage bucket. File names alone are not a reliable boundary: inherited directory permissions, mounted volumes, symbolic links, backups, and temporary files can expose more than the task requires.
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The U.S. Department of Energy’s GEAR guidance recommends starting with read-only access and granting only the files and operations needed for the current task. Keep sensitive directories outside the agent’s mount or sandbox rather than relying on a behavioral instruction. A prompt that says not to delete files cannot prevent a tool with delete permission from doing so.
Enterprise SaaS and collaboration systems
OAuth delegated permissions can expose mail, calendars, cloud storage, contacts, documents, chats, sites, and other Microsoft 365 resources. Application permissions can grant access without a person being present, sometimes across an entire tenant. SaaS connectors can similarly expose Jira issues, Confluence pages, project attachments, comments, and user data. The important question is not whether a connector is labeled read-only, but which collections and operations its token actually authorizes.
Delegated access also carries the risk of over-broad user context. If an employee can read every project, an agent acting on that employee’s behalf may be able to retrieve every project unless an additional policy narrows the scope.
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Tool calls can traverse several services in one workflow: an agent may read a ticket, query a database, create a spreadsheet, and send a message. Each service may enforce its own role, yet the workflow’s effective permission is their aggregate. Database credentials can expose tables, views, stored procedures, or administrative functions; API keys may allow writes even when the agent’s interface appears informational.
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Inspect both direct and transitive capabilities. A tool that only appears to search records may invoke an automation endpoint that can update records or trigger a deployment. AWS and Microsoft guidance both emphasize reviewing permissions end to end rather than evaluating each grant in isolation.
Secrets and credentials
Anything available to the agent process can become data: environment variables, configuration files, shell history, cached tokens, source-code repositories, logs, browser profiles, and mounted secret stores. A prompt-injection attack or compromised dependency can cause the process to disclose those values. Secrets that are never needed for the task should not be mounted into the runtime.
OWASP recommends short-lived, narrowly scoped tokens and protecting secrets from agent processes. Prefer a broker that issues a task-specific credential just before a call, rather than placing a long-lived bearer token in an environment variable.
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Network destinations
Network reach determines where an agent can send retrieved data. Without egress controls, a process may contact an unexpected web service, paste content into a third-party API, or upload files to an attacker-controlled endpoint. Restrict outbound traffic to approved domains, IP ranges, ports, and protocols, and require an explicit proxy or gateway for external calls.
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Delegated and autonomous agents need different identities
Separate two operating modes before assigning permissions. In delegated mode, the agent acts for a signed-in person and should inherit that person’s allowed boundary, including the person’s tenant, resource, and operation restrictions. In autonomous mode, a schedule, event, or service starts the agent without a person present; it needs its own identity and an explicitly approved resource set.
| Dimension | Delegated mode | Autonomous mode |
|---|---|---|
| Who authorizes the action? | The identified user, within the user’s current permissions | A service owner or approver defines the agent’s standing scope |
| Credential model | Short-lived on-behalf-of token tied to the user and task | Short-lived workload-identity token tied to the agent and job |
| Accountability | Log both the agent and the on-behalf-of user | Log the unique agent identity, named owner, sponsor, and approver |
| Typical risk | Inherited access is broader than the task requires | Standing permissions persist after a job or owner changes |
| Revocation | Disable the agent and invalidate delegated sessions | Disable the workload identity, rotate credentials, and remove grants |
NIST’s 2026 cybersecurity insight warns that sharing credentials undermines IAM accountability because the system cannot reliably verify which individual or service performed a transaction. Microsoft guidance likewise calls for a unique, dedicated agent identity with a named owner or sponsor and approver.
How to restrict an AI agent safely
- Define the task and data boundary. Write down the exact repositories, records, fields, operations, and destinations required. Classify data by sensitivity and identify actions that could cause financial, legal, privacy, or operational harm. If the task only needs invoice totals, do not expose full customer profiles.
- Create a dedicated identity. Use a service account, workload identity, or delegated identity created for the agent, never a developer’s personal account or a shared administrator login. Record the owner, sponsor, approver, purpose, environment, and expiry or review date.
- Choose delegated or autonomous mode explicitly. For a user’s request, issue an on-behalf-of token that preserves the user’s restrictions. For scheduled or event-driven work, grant the autonomous identity only the approved resources. Do not silently switch modes when a user’s token expires.
- Start with deny-by-default and read-only. Block every tool, repository, operation, and network destination until it is approved. Begin with list, search, and read operations. Add create, update, delete, send, or execute capabilities only after documenting why each is necessary.
- Scope credentials to the task. Use OAuth scopes, database roles, object-level policies, and workload-identity conditions that name the permitted resource and operation. Set short expiration times and issue a fresh token per job where practical. Avoid personal credentials, static bearer tokens, and administrative roles.
- Enforce policy outside the model. Put authorization in the operating system, sandbox, API gateway, database, scheduler, or a dedicated policy service. DOE states the principle plainly: “A prompt such as ‘never delete files’ is not an access control.” The enforcement layer must reject an unauthorized call even if the model requests it.
- Approve connectors and MCP servers individually. Treat every connector or Model Context Protocol server as code or a remote service with its own permissions. Review its implementation, owner, authentication, data retention, and requested scopes. Allow only named servers and block unapproved ones. Atlassian documents administrative allow and block controls for Jira and Confluence access; use equivalent controls where available.
- Isolate secrets. Keep production keys, signing material, browser sessions, and unrelated environment variables out of the agent runtime. Use a secret broker that releases one narrowly scoped value for one operation, masks it in logs, and revokes it when the job ends.
- Control network egress. Route traffic through an allowlisted proxy or firewall policy. Permit only required destinations and methods, inspect uploads, and block direct internet access when the workflow does not need it. Separate internal service networks from public endpoints.
- Log and rehearse revocation. Record the agent identity, owner, effective scope, action, resource, timestamp, correlation ID, connector, and on-behalf-of user. Test disabling the agent, invalidating active tokens, rotating credentials, removing connector grants, and cutting network access. Measure how long it takes for a revoked permission to stop working.
- Recalculate aggregate permissions after every change. Re-run the review whenever a tool, connector, tenant, data source, role, or network route is added. Confirm that a harmless-looking addition has not created a path from a low-sensitivity task to a high-sensitivity system.
Controls by enforcement layer
| Layer | Useful controls | What to verify |
|---|---|---|
| Runtime and OS | Sandbox, separate user, read-only mounts, restricted processes, isolated temporary storage | Only the intended folder and binaries are visible; sensitive host paths are inaccessible |
| Identity provider | Dedicated identity, conditional access, workload identity, token expiry, approval workflow | Every token names an owner, audience, scope, and expiration |
| API gateway | Allowlisted routes, schema validation, rate limits, field filtering, human approval for writes | Unexpected methods, fields, tenants, and destinations are rejected before reaching a backend |
| Database | Separate read role, views, row and column policies, transaction limits | Queries cannot bypass views or invoke administrative procedures |
| Connector and MCP administration | Per-server approval, minimal scopes, signed or authenticated endpoints, version review | Unapproved servers cannot be added by a prompt or configuration change |
| Network | Egress allowlist, proxy, DNS controls, upload inspection, private routing | Data cannot leave through an unapproved domain or alternate protocol |
| Monitoring | Immutable audit logs, anomaly alerts, correlation IDs, retention policy | Investigators can identify the agent, user, resource, and exact operation |
Practical scoping examples
Limit an agent to one folder
Mount only the required folder into a dedicated sandbox, make the mount read-only, and run the process under an identity that has no permission on parent directories or sibling folders. Exclude backups, hidden configuration directories, and credential files. If the agent must write results, provide a separate output directory with create-only or append-only semantics and scan files before they leave the sandbox.
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Limit an agent to one application
Register a separate application identity and grant only the API scopes for the named workspace or project. Disable broad tenant-wide application permissions. Filter records and fields at the gateway or data layer, because a connector’s user interface may hide data that its token can still retrieve.
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Permit analysis but block consequential actions
Give the agent read access to the source systems and a write permission only to a staging queue. Require a human or a separately authorized service to approve payments, deletions, production deployments, external messages, or changes to access policy. Log the proposed action and the approving identity together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to monitor access and revoke it
Auditing should show more than a successful login. For each call, capture who or what authenticated, which user context was delegated, which tool and connector ran, the resource and fields touched, the decision that allowed or denied it, and a correlation ID linking the steps in a chain. Protect logs from alteration and avoid recording raw secrets or unnecessary personal data.
Maintain a revocation runbook with owners and response times. It should cover disabling the agent identity, revoking OAuth grants, invalidating refresh and access tokens, rotating exposed credentials, removing connector approvals, blocking egress, and preserving forensic logs. Test the runbook during normal operations, not only after an incident; token caches and queued jobs can otherwise continue running after an account is disabled.
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- Can the system distinguish delegated user actions from autonomous workload actions?
- Can permissions be scoped to a particular resource, record set, field, operation, and tenant?
- Are tools and connectors explicitly allowlisted, with per-server approval and version control?
- Are credentials short-lived, audience-restricted, and isolated from prompts, logs, and shell history?
- Can an administrator enforce network egress destinations independently of the model?
- Do audit records include agent identity, owner, on-behalf-of user, resource, action, and correlation ID?
- How quickly can administrators disable the agent and invalidate all active sessions?
- Does the platform support data-loss prevention, sensitivity labels, field filtering, and human approval for high-impact actions?
Common failure modes and fixes
Relying on instructions such as “do not disclose secrets”
Instructions can be overridden by malicious content, tool output, or a compromised component. Replace the instruction with filesystem isolation, secret exclusion, output filtering, and gateway policy.
Using a personal administrator token
This destroys accountability and gives the agent every privilege the person has. Create a dedicated identity with a named owner and narrowly scoped, expiring credentials.
Granting a broad connector for a narrow question
A request about one project does not justify organization-wide search. Restrict the connector to the project, fields, and read operations required, then verify the token’s effective scope.
Ignoring transitive tool access
A search tool may trigger an automation that sends mail or changes a record. Map every downstream call and apply authorization at each service boundary.
Assuming revocation is immediate
Refresh tokens, cached sessions, queued jobs, and connector-side grants can survive an account change. Include token invalidation, cache expiration, job cancellation, and connector removal in the revocation test.
Security checklist
- Document the agent’s purpose, owner, mode, data classes, operations, and expiry date.
- Use a unique identity rather than shared or personal credentials.
- Start deny-by-default and read-only; add each write capability deliberately.
- Issue short-lived, task-scoped tokens and keep secrets outside the runtime.
- Approve connectors and MCP servers one at a time.
- Enforce authorization, field filtering, and egress controls outside the model.
- Log identity, user context, tool, resource, action, decision, and correlation ID.
- Practice disabling the agent, revoking tokens, rotating secrets, and removing grants.
- Recalculate the combined permission set after every configuration change.
No single cross-industry statistic establishes how often agents receive excessive access; authoritative guidance instead focuses on identity, least privilege, isolation, and auditability. Applying those controls makes an agent’s boundary explicit, attributable, and reversible.
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