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AI agents can access any data exposed by their runtime, tools, connectors, credentials, delegated user session, or network path. That can include local files, cloud documents, email, databases, SaaS records, secrets, and data returned by chained APIs. The model prompt is not the security boundary. The real boundary is the combined permission set across every system the agent can reach.
To control that access, give the agent a separate identity, start with deny-by-default read-only permissions, issue short-lived task-scoped credentials, restrict tools and outbound destinations, enforce policy outside the model, and continuously log and test revocation.
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What determines what an AI agent can see?
An agent does not have a universal level of access. It acts through a runtime and a collection of tools. Its effective access is the union of the permissions granted by all of them.
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- Tool permissions: File tools, shell tools, browser automation, database clients, and custom functions can each add operations and data sources.
- Connector permissions: OAuth grants, application roles, SaaS integrations, and MCP servers may expose entire collections or workspaces.
- Credential context: A delegated user token limits the agent to what that user can do; an autonomous workload identity has whatever resources administrators assign to it.
- Network reach: An agent that can make outbound connections may send retrieved data to destinations that were never considered during setup.
Because these permissions are chained, a harmless-looking addition can change the overall risk. A read-only document connector combined with a broadly scoped messaging tool, for example, can still enable sensitive information to leave the original repository.
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Data surfaces an agent may access
Local and hosted files
An agent can read or modify any path visible to its process or file tools. This includes a laptop folder, a mounted network share, a cloud workspace, temporary files, and files created by other tools. If the process runs with a broad operating-system account, the agent may inherit access to configuration files, logs, source code, backups, and user profiles.
Start with a dedicated working directory mounted read-only. Add individual files or folders only when the task requires them, and grant write, rename, or delete operations separately from read access. A prompt saying “do not delete files” cannot replace an operating-system or tool-level prohibition.
Email, documents, and collaboration workspaces
OAuth delegated permissions can expose mail, calendars, cloud drives, sites, chats, and other Microsoft 365 resources available to the signed-in user. Application permissions can instead grant an agent access independent of a human session. SaaS connectors may expose Jira projects, Confluence spaces, ticket attachments, comments, and custom fields.
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Check both the connector’s scope and the underlying workspace permissions. Restrict the agent to named sites, projects, repositories, or collections where the platform supports that granularity. Do not assume that selecting “read” prevents metadata, search results, titles, or comments from being returned.
APIs, databases, and automation tools
Tool calls can traverse several services in one workflow. An agent might query a database, pass the result to a ticketing API, and trigger an automation that writes to storage. The final capability is determined by the most permissive link in that chain, not by the first tool’s label.
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Use separate database roles for each agent and expose views or stored procedures instead of unrestricted tables. Scope API credentials to particular resources and operations. Disable administrative endpoints, arbitrary query execution, and cross-tenant actions unless they are essential and protected by a human approval step.
Secrets and credentials
Anything available to the agent process can become data: environment variables, shell history, configuration files, mounted secret volumes, browser profiles, cached tokens, and responses from a secret manager. A prompt cannot reliably prevent an agent or a compromised dependency from reading a secret that the process can access.
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Network destinations
Network access determines where an agent can send retrieved data. Without egress controls, a tool that can make arbitrary HTTPS requests may upload files to an unapproved service, call a public API, or communicate with infrastructure controlled by an attacker.
Use an outbound allowlist at the network, proxy, or service-mesh layer. Deny direct internet access by default, permit only required domains or service endpoints, and log destination, identity, volume, and result. Treat DNS, redirects, webhooks, and uploaded attachments as part of the egress policy.
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Delegated and autonomous agents need different identities
Delegated, on-behalf-of work
A delegated agent performs an action for a signed-in user. The request should carry that user’s authorization context so the agent cannot exceed the person’s existing rights. The system must preserve which user approved the action and which agent performed it.
Delegation is appropriate for tasks such as summarizing a user’s own mailbox or drafting a response from documents that user can already open. It is not a reason to copy the user’s refresh token into an agent process. Use the platform’s supported delegated flow and limit scopes to the current task.
Autonomous, scheduled, or event-driven work
An autonomous agent runs without a person actively present. Give it a unique workload identity with a named owner, sponsor, and approver. Assign only the repositories, queues, tables, and operations required for its schedule. Do not reuse an employee account, shared service credential, or tenant-wide administrator role.
Document what the identity may do, what it may never do, and which actions require a human. When the owner changes or the workflow ends, disable the identity and remove every grant rather than leaving dormant access in place.
How to restrict an AI agent safely
- Inventory the workflow. List every input, tool, connector, credential, data store, and network destination involved in one complete run. Include indirect calls made by automations and downstream services.
- Create a dedicated identity. Bind the identity to a responsible owner and record whether each request is delegated or autonomous. Avoid shared accounts so actions remain attributable.
- Start deny-by-default. Begin with no files, connectors, API methods, database tables, or outbound destinations. Add one approved resource at a time and record the business reason.
- Make the first grant read-only. Permit listing, searching, and retrieval before enabling creation, editing, deletion, sending, payment, or configuration changes. Separate each operation so write access is not an accidental side effect of read access.
- Issue a short-lived credential. Use a narrowly scoped OAuth or workload-identity token tied to the task. Set an expiry that covers the expected run, then require a new authorization for the next run.
- Constrain the runtime. Run the agent in a sandbox, container, or isolated host with a dedicated filesystem and service account. Mount only required paths; block access to host sockets, other users’ home directories, and unrestricted process inspection.
- Allowlist tools and connectors. Approve each function, MCP server, browser integration, and SaaS connector individually. Verify its authentication method, scopes, data retention, update process, and downstream destinations before enabling it.
- Enforce outbound policy. Route traffic through a gateway or proxy that permits only approved endpoints and methods. Block direct internet access and record denied attempts as security events.
- Place approval before consequential actions. Require a person to review destructive file operations, external messages, financial transactions, permission changes, or sensitive-data exports. Present the exact target, payload, and identity in the approval screen.
- Test the boundary. Attempt to open an unapproved file, call an unapproved API, read a secret, reach a blocked domain, and perform a write operation. The correct result is an enforced denial outside the model, not merely a refusal generated by the model.
Why model instructions are not access controls
System and developer prompts can describe safe behavior, but they do not prevent a tool, dependency, or compromised process from using credentials. The U.S. Department of Energy states the distinction directly: “A prompt such as ‘never delete files’ is not an access control.” Authorization must be enforced by the operating system, API gateway, database, scheduler, sandbox, or a dedicated policy service.
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External enforcement also protects against prompt injection. A malicious document can tell an agent to reveal its instructions or upload records; a gateway can still deny the upload, and a database role can still reject an unauthorized query. Keep policy decisions independent of the model’s generated text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Connector and MCP server controls
Treat every connector and Model Context Protocol (MCP) server as code or as a remote service with its own security boundary. Review who operates it, which credentials it receives, what data it can return, and where it can send data. Require authentication and minimum scopes, pin approved servers, and remove unused integrations.
Where a platform provides an allow/block control for Jira, Confluence, or another workspace, use it to approve named projects or spaces rather than enabling the connector globally. Recheck the grant after connector updates because a new tool method or scope can expand effective access.
Logging, monitoring, and revocation
Authorization is incomplete unless you can explain and stop an agent’s actions. Record at least:
- the agent identity and named owner;
- the on-behalf-of user, when delegation is used;
- the tool, connector, or policy decision;
- the resource and operation;
- the request and correlation identifiers;
- the credential or scope used;
- the approval decision and timestamp;
- the destination and amount of data transferred.
Use these records to detect unusual access, such as a scheduled agent reading a new repository or making a first-time external connection. Protect logs from alteration and retain enough context to investigate without copying sensitive payloads unnecessarily.
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Rehearse revocation before production. A useful exercise is to disable the agent identity, revoke active tokens, rotate any exposed credentials, remove connector grants, block network destinations, and verify that queued jobs cannot obtain a new token. Test both normal shutdown and emergency disablement; “disabled” is not proven until a real request fails.
How to evaluate an agent platform or internal control
| Control area | Questions to ask | Evidence of a strong implementation |
|---|---|---|
| Identity | Can delegated and autonomous runs use different identities? Is every identity tied to an owner? | Unique agent identities, named sponsor and approver, and preserved user context for delegated actions. |
| Scope granularity | Can access be limited to one folder, project, table, record type, API method, or operation? | Resource- and operation-level grants rather than tenant-wide roles. |
| Tool and connector governance | Can administrators approve individual tools, MCP servers, and SaaS connectors? | Explicit allowlists, authenticated integrations, and change review. |
| Token and secret handling | How long do credentials live, and can the agent read its own secrets? | Short-lived scoped tokens, brokered retrieval, isolated secret storage, and rapid rotation. |
| Network egress | Can the agent contact arbitrary destinations or upload data directly? | Gateway-enforced allowlists, blocked direct internet access, and destination logging. |
| Auditability | Can an investigator reconstruct who did what, where, and for whom? | Identity, owner, resource, operation, correlation ID, approval, and delegated-user fields. |
| Revocation | How quickly can access be stopped across tokens, connectors, jobs, and network paths? | One documented emergency procedure that is regularly exercised. |
| Data protection | Are sensitivity labels, DLP rules, and human approvals enforced before export or consequential actions? | Policy decisions made outside the model and applied consistently to every tool path. |
Common failure modes and fixes
“The agent only needs one folder,” but the whole drive is mounted
Replace the broad mount with a dedicated directory or read-only view. Verify that symlinks, archives, backups, and temporary files cannot lead back to other users’ data.
A user token works in testing, then exposes too much in production
Separate delegated testing from production autonomy. Create a production workload identity with explicitly approved resources and an expiry policy instead of reusing a powerful employee token.
Read-only access still leaks sensitive information
Read-only prevents modification, not disclosure. Add data classification, row- or field-level filtering, DLP checks, and egress restrictions. Return only the fields needed for the task.
Revoking the connector does not stop an existing run
Invalidate active tokens, cancel queued jobs, disable the agent identity, and block the relevant network path. Confirm each layer independently because cached credentials and already-issued sessions may outlive a connector setting.
Logs show the tool but not the responsible person
Require a unique identity and preserve the on-behalf-of user, owner, approver, resource, and correlation ID in every tool call. Shared credentials create the accountability gap identified by NIST.
Practical baseline for a first deployment
For a new agent, use an isolated runtime, a unique non-administrative identity, read-only access to one approved data collection, short-lived credentials, an allowlisted tool set, and no unrestricted outbound network. Add write or send capabilities only after a documented test demonstrates that unapproved resources and operations are denied. Keep a named owner responsible for reviewing grants, logs, connector changes, and revocation drills.
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