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Secure an AI agent by surrounding the model with deterministic controls: give it a unique identity and only task-specific permissions, restrict outbound network traffic in an independent sandbox, and require a fresh human decision before irreversible or high-impact actions. The model may propose a tool call, but code or a policy service must decide whether that call is allowed.
What secure AI-agent settings must accomplish
An agent can read data, call software tools, execute code, send messages, alter records, or change infrastructure. A conversational instruction is not a security boundary, and a model-generated plan is not an authorization decision. Protection therefore has to be layered around the model.
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- Identity: every agent run and workload has an auditable identity tied to the initiating user, tenant, task, and owner.
- Authorization: a deterministic check evaluates the exact tool, operation, resource, and current approval state for every action.
- Execution isolation: sandbox, filesystem, and network controls remain effective even if the model is manipulated.
- Human control: a reviewer confirms consequential actions immediately before execution, with pause and shutdown controls available.
- Evidence: plans, inputs, decisions, approvals, outputs, and changes are recorded so an operator can reconstruct what happened.
Microsoft Learn’s identity guidance, updated August 1, 2026, summarizes the core rule: bind every tool call to the initiating identity, authorize the exact action and target, use narrowly scoped tokens, and obtain fresh confirmation for irreversible or high-impact actions. Microsoft’s agent-risk guidance likewise recommends allowing only the minimum tools, data, and operations required and denying everything else by default.
Classify actions before granting access
Start with an action-risk matrix. The default state should be deny; an action becomes available only when its class, target, identity, and approval requirements are satisfied. Classification should describe the effect of the operation, not merely the name of the tool.
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| Action class | Typical examples | Default treatment | Approval expectation |
|---|---|---|---|
| Read-only | Query a permitted document or return a status | Permit with task-scoped credentials and tenant filtering | Usually no interactive approval, but log the request |
| Reversible write | Create a draft, add a private task, or update a noncritical record with undo | Permit only on named resources and fields | Policy may allow automatically when rollback is verified |
| Sensitive-data access | Retrieve personal, health, financial, security, or confidential records | Require purpose, field-level scope, and data-minimization rules | Approval or a preapproved workflow, depending on sensitivity |
| External communication | Send email, publish a post, call a customer, or upload a file | Hold the exact content, recipients, and destination for review | Fresh confirmation immediately before sending |
| Financial transaction | Purchase, refund, transfer, or change billing | Block unless amount, account, recipient, and limits match policy | Fresh human approval; use transaction limits and dual control where appropriate |
| Production change | Deploy code, alter a live database, or change an operational setting | Use a constrained change path, maintenance scope, and rollback | Fresh approval from an authorized operator |
| Permission or identity change | Grant a role, create a key, disable a control, or change an access policy | Prohibit from general-purpose agents; expose only a dedicated workflow | Explicit approval and, for critical systems, a second approver |
Reevaluate the class on every call. A tool labeled “update” can be low risk when editing a private draft and high risk when changing a production permission record.
Use task-scoped identity and least privilege
Give each workload a distinct, accountable identity
Create an identity for each agent or workload rather than sharing a human account or a broad service credential. Record the owner, business purpose, tenant, approved tools, environment, and expiration date. Propagate the initiating user and task identifier with every tool request so downstream systems can apply user-level and tenant-level authorization.
Separate identities by environment and function. A support-triage agent should not reuse the identity of a deployment agent, and a test workload should not carry production permissions. This separation limits blast radius and makes an audit intelligible.
Issue narrowly scoped, short-lived credentials
Tokens should name the exact resource, operation, tenant, and validity window. Prefer an ephemeral token minted for one task or action over a long-lived key stored in an agent prompt or configuration file. Restrict methods and fields where the target system supports that granularity, and remove permissions that the workflow does not use.
Rotate credentials, revoke them when a task ends, and expire access that has not been used. Keep secrets outside model-visible context; provide a brokered tool that performs the operation without returning the underlying credential.
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Authorize every action, not only the session
A login-time decision is stale as soon as the plan, target, data, or user context changes. Before each tool call, a policy component should verify the identity, exact operation, resource, tenant, token lifetime, current risk class, and any required approval. Recheck after retries, redirects, queued jobs, and handoffs to another agent.
Restrict network egress outside the model
Network access is an independent security boundary. NIST’s 2025 profile says AI access to networks and environments should be curtailed according to capability and risk, with AI-initiated network changes and privilege use gated by human approval or policy checks. An agent confined to a scratch directory presents less risk than one with production write access or unrestricted outbound connectivity, as noted in Anthropic’s submission to the NIST request for information on agentic security.
Run code and browsing in an isolated environment
Use a sandbox or separate execution environment with a disposable filesystem, non-root privileges, resource limits, and no implicit access to host sockets. Mount only the directories and datasets required for the task. Block metadata services, credential stores, internal control planes, and administrative interfaces unless a narrowly defined workflow requires them.
Allowlist destinations and protocols
Default-deny outbound traffic. Permit only approved domains or IP ranges, ports, protocols, and methods for the current task. Resolve and validate destinations at connection time to reduce DNS rebinding and redirect surprises. Decide whether an upload is allowed based on the destination, data classification, recipient, and approval—not merely on the fact that a domain is trusted.
Apply these rules at a network gateway, firewall, service mesh, or sandbox runtime that the model cannot alter. A prompt that tells the agent to disable a proxy or contact an unapproved host must have no effect.
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Log allowed and denied egress
Record the agent identity, initiating user, destination, port and protocol, timestamp, policy result, reason, and approval context. Alerts should cover repeated denials, sudden destination changes, high-volume calls, attempts to reach sensitive address ranges, and connections made after a task or approval expired.
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Put human approval at the execution boundary
Require confirmation for consequential effects
Pause before external sends, deletion, purchases, deployments, permission changes, production writes, and other irreversible or high-impact operations. Read-only retrieval and safely reversible work can often proceed under policy, but sensitive-data access may still need a workflow-specific approval.
The approval request should show the proposed operation, exact target, data to be sent or changed, recipients, expected effect, reversibility, identity being used, and any relevant limits. Do not ask a user to approve an opaque phrase such as “continue.”
Make approval fresh, scoped, and expiring
Bind a decision to the precise action, target, content, and scope displayed to the reviewer. Expire it when any of those elements changes, when the task times out, or when a queued operation is materially modified. A general instruction such as “handle my invoices” must not authorize a later, different payment.
Provide pause, cancel, and emergency shutdown
Operators need a reliable way to pause an agent, cancel queued calls, revoke its credentials, and stop execution across workers. Design the stop path independently of the model and test it during an active run. If the reviewer, policy service, identity provider, or approval channel is unavailable, fail closed for gated actions rather than executing on an old decision.
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Keep policy independent from the model
OWASP’s AI Agent Security Cheat Sheet states that an agent can propose an action, but a policy service or execution component should independently validate scope, privilege, and approval state before execution. Implement that separation as a broker or enforcement point between the model and each tool.
Use structured tool contracts
Define each tool with an explicit operation, resource identifier, allowed fields, input limits, side effects, and risk class. Reject unknown operations, extra parameters, ambiguous targets, and attempts to smuggle instructions in free-form fields. Normalize paths, URLs, account identifiers, and encodings before policy evaluation.
Defend against prompt injection and confused deputy behavior
Treat retrieved webpages, documents, emails, and tickets as untrusted data. They may contain instructions that try to redirect an otherwise authorized tool. Keep data content separate from control messages, require the original user identity on every call, and prevent the agent from using its permissions on behalf of an unrelated requester. Test scenarios in which malicious content asks for secret disclosure, policy changes, or an external upload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect memory and data stores
Agent memory is part of the security boundary, not a harmless convenience. Partition memory by user, tenant, project, and environment; enforce authorization on reads and writes; encrypt it in transit and at rest; and define retention and deletion rules. Do not let a later session retrieve another user’s notes merely because the model considers them relevant.
Validate new memories before they become durable instructions. Mark their source, confidence, timestamp, and owner, and provide a way to quarantine or remove poisoned entries. Limit what sensitive data can be written to long-term memory and redact secrets from traces and summaries.
Build lifecycle controls and observability
Maintain an inventory of agents, tools, dependencies, identities, sandboxes, policy versions, and data stores. Assign an accountable owner to each item and record why each permission exists. Version policy and tool changes, review them before deployment, and expire unused access.
An audit record should connect the full chain:
- the initiating user, tenant, agent identity, and task;
- the model plan and relevant retrieved data identifiers;
- tool name, normalized arguments, target, and token scope;
- authorization and network decisions, including denials;
- human approval event, approver identity, displayed content, and expiry;
- execution result, returned data classification, errors, retries, and rollback.
Send records to tamper-resistant storage or a SIEM. Alert on scope-escalation attempts, repeated policy denials, unusual destinations, excessive tool calls, dependency changes, and activity outside the agent’s normal schedule.
Implement the controls in a practical sequence
- Map the workflow. List every model, tool, data source, side effect, environment, and human role. Mark which steps can be read-only, reversed, delayed, or cancelled.
- Assign risk classes. Put each operation in the action matrix and write explicit prohibited cases. Start with deny-all permissions and add only demonstrated requirements.
- Create identities. Register an owner, tenant boundary, environment, purpose, and expiration for each workload. Separate development, staging, and production identities.
- Define tool contracts. Specify operations, resources, fields, limits, side effects, and risk class. Reject malformed or ambiguous requests before they reach the tool.
- Broker credentials. Mint short-lived tokens for the exact task, resource, operation, and time window. Never expose reusable secrets to model context.
- Deploy isolation and egress policy. Run execution in a sandbox, mount only required data, and enforce outbound allowlists at a control point outside the model.
- Add approval gates. Present target, content, effect, reversibility, identity, and limits. Bind approval to that exact action and expire it on change or timeout.
- Instrument and exercise shutdown. Log every decision and result, connect alerts to operators, and verify that pause, cancellation, credential revocation, and emergency stop work during execution.
Test failure modes before production use
Security testing must cover the whole chain rather than only the prompt. Run adversarial cases in which a document injects instructions, a tool receives an unexpected redirect, a user changes tenant context, a policy service becomes unavailable, or an approval expires while a job is queued.
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- Confirm that changing one argument invalidates a previously granted approval.
- Confirm that a token cannot read another tenant, call an unlisted operation, or outlive its task.
- Confirm that retries do not duplicate a purchase, deletion, message, or deployment.
- Confirm that denied calls and repeated escalation attempts generate actionable alerts.
- Confirm that shutdown revokes queued and in-flight work according to the documented guarantee.
Document residual risks and the conditions under which an operator must take over. A control that exists only in a design document is not a dependable control.
How to compare implementation options
Whether you assemble services or adopt an agent-security platform, evaluate the enforcement points rather than the model’s marketing claims.
| Comparison axis | Questions to ask |
|---|---|
| Identity and token scope | Can each run carry a unique identity and short-lived token limited to one tenant, resource, operation, and time window? |
| Per-action authorization | Is scope checked for every call, retry, redirect, and handoff, or only when a session starts? |
| Independent egress and sandbox | Can the model alter network rules, reach metadata services, or escape the filesystem? Are allowlists enforced outside the model? |
| Approval experience | Does the reviewer see exact content, target, effect, and reversibility? Do approvals expire on change, and is there an emergency stop? |
| Audit completeness | Can logs join identity, plan, arguments, policy result, approval, network event, execution result, and rollback, with SIEM export? |
| Memory and tenant isolation | Are reads and writes partitioned by user and tenant, with retention, encryption, provenance, and poisoning controls? |
| Dependency and change control | Are tools, packages, policies, and permissions inventoried, versioned, reviewed, and expired when unused? |
| Fail-closed behavior | What happens when the reviewer, policy service, identity provider, network gateway, or logging system is unavailable? |
Operational bottom line
Least privilege, egress control, and human approval solve different failure modes and must be deployed together. Identity and per-action authorization limit what an agent is allowed to do; sandbox and network controls limit where it can reach; approval gates stop consequential effects at the last responsible moment; and logging, lifecycle management, memory isolation, and tested shutdown make the system governable after deployment. Keep the model in the role of proposing actions, and keep final authority in deterministic policy and accountable human decisions.
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