Give each AI agent its own identity, restrict it to the data and actions its task requires, and enforce those limits outside the model. Prompts can guide an agent, but they cannot reliably authorize access. Use narrow permissions, isolated execution, protected credentials, approval gates for consequential actions, and tested monitoring and revocation.
Start with a defined task and accountable owner
Before connecting an agent to company systems, document what it is for and what it is allowed to touch. That scope should identify its owner and approver, approved data sources, permitted actions, tools and integrations, and operating environment. The agent should not inherit a vague mandate such as “help with operations.”
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Keep user instructions distinct from the content the agent processes. Webpages, emails, documents, tool descriptions, and tool responses are data, not trusted instructions. They may contain prompt injections that try to redirect the agent or misuse its legitimate permissions. OWASP advises giving agents only the autonomy, tools, and access their task needs, and only for as long as needed (OWASP DevSecOps Guideline: AI Agent and MCP Security).
Give the agent its own identity and narrow permissions
Create a distinct, lifecycle-managed identity for each agent or appropriately bounded workload. Assign a named owner, define its purpose, and review its effective permissions across identity roles, connected tools, and downstream services. Avoid running agents under an employee’s personal account: doing so can blur accountability and expose access unrelated to the task.
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Begin with denial and grant only what the workflow needs. Prefer read-only access; add write permission only where the task requires it, and scope that permission to the specific resources involved. Use short-lived, task-scoped credentials where possible. If an agent acts for a user, bind authorization to that initiating user and check the user’s permission at the service receiving each action. A valid signature between agents does not, by itself, authorize an operation.
Microsoft’s guidance puts the default plainly: “Allow only the minimum tools, data, and operations required. Deny everything else by default.” (Microsoft Learn: Reduce autonomous agentic AI risk.)
Limit what tools can do
Make an explicit allowlist of tools and operations for each workflow. A tool that can read a record does not need permission to edit or delete it. Do not enable unreviewed plugins, integrations, cross-tenant access, administrative actions, or privilege changes by default. Reassess the allowlist when the workflow or connected systems change.
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Enforce authorization in the identity provider and the application or service that performs the operation—not in the prompt and not in the model’s answer. A safety instruction may reduce undesirable behavior, but it is not an access-control boundary. Microsoft recommends defining permitted tools and actions in enforceable policy and validating authorization at the point of use (Microsoft Learn: Reduce autonomous agentic AI risk).
Isolate execution and restrict network access
Generated code can use whatever files, credentials, and network connections its runtime exposes. Run agent workloads in an isolated VM, container, or comparable environment; keep workloads that must not share data separate. Do not mount sensitive home directories or production resources unless the task requires them.
Restrict outbound network traffic to an allowlist of required destinations. Apply controls where connections actually occur: a tool connector may run outside the agent’s shell or sandbox, so a shell-level restriction alone might not cover it. OWASP describes isolation as the security boundary against a manipulated agent and cautions that tools may operate outside a shell sandbox (OWASP DevSecOps Guideline: AI Agent and MCP Security).
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Keep credentials out of the agent environment
Assume code running in an agent’s environment can read any credential placed there. Keep long-lived production and third-party keys outside the sandbox where feasible. Instead, use a trusted application, vault, or proxy to provide narrowly scoped credentials only to approved services and only when needed. Never put long-lived keys in prompts, source code, container images, or logs.
If a credential may have been exposed, revoke or rotate it and review the actions taken with it. Isolation limits what an agent can reach, but it does not hide a secret that has been deliberately made available inside that environment (OWASP DevSecOps Guideline: AI Agent and MCP Security).
Gate high-impact actions and cap autonomous operation
Require fresh human approval before actions that are consequential, external, or difficult to reverse. Examples include sending messages outside the company, deleting data, making purchases, deploying changes, or modifying permissions. Do not let approval for one action silently authorize later actions.
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Set independent limits on steps, loops, and budget, and provide a reliable way to pause or stop an agent. These safeguards matter because prompt injection can steer an agent into using valid permissions for an unintended purpose. Authorization should be deterministic and independent of model output; a human gate is especially important for high-impact actions (Microsoft Learn: Reduce autonomous agentic AI risk).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor access and test revocation
Keep an inventory of agents, their owners, models, tools, data access, and deployment environments. Log enough to reconstruct what happened: agent identity, effective scope, action, resource, relevant human principal, and correlation details. Protect logs from becoming another source of secrets by excluding plaintext credentials and unnecessary sensitive data.
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Choose a deployment model with its control boundaries in view
A SaaS agent, an agent built on a managed platform, and a self-managed agent on infrastructure-as-a-service place different operational responsibilities with the provider and customer. Do not assume that choosing a hosted service transfers responsibility for deciding which company data the agent may use or which actions it may authorize. Microsoft’s shared-responsibility guidance says customers retain responsibility for data, identity and credential scope, action authorization, oversight, and governance; verify the actual boundary for the service you select (Microsoft Learn: Reduce autonomous agentic AI risk).
| Deployment pattern | What to establish before use |
|---|---|
| SaaS agent | Who controls the orchestrator and connectors; how agent identity, per-action permissions, data and memory isolation, sandboxing, egress, audit, and revocation are handled. |
| Managed PaaS agent platform | Which controls the platform provides and which the customer must configure, including tool permissions, data boundaries, action authorization, audit, and credential handling. |
| Self-managed IaaS agent | How the organization will operate the identity, runtime isolation, network restrictions, secrets, monitoring, approval gates, and revocation mechanisms itself. |
These patterns are governance tradeoffs, not a universal ranking. For each, document who operates the orchestrator and connectors, who grants permissions, whether authorization can be scoped per action, and how isolation, egress, audit, and revocation work. If a control cannot be demonstrated or tested, do not treat it as an enforced boundary.
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