Read-only limits what an agent can change. Approval required determines which actions must pause for a person or policy decision. Full access is not a universal standard: it may remove sandbox and approval barriers, or simply grant a broad role, depending on the product.
Choose permissions by looking at the agent’s identity, tools, data scope, filesystem, network, credentials, approval rules, and recovery controls—not by trusting a mode name. A safe design usually permits narrowly scoped reads and drafts, confines edits to a working area, and requires an independently enforced policy decision before sending, deleting, provisioning, or changing access.
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What the three permission labels actually control
These labels describe different dimensions of control, so they cannot be ranked as if they were three universal security levels.
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- Read-only is a capability constraint. The agent may inspect permitted files, records, or search indexes, but the constrained interface does not let it write, delete, or update through that surface.
- Approval required is a decision gate. A specified operation pauses until an authorized person or policy service allows it. The agent may still have the technical ability to attempt the operation after approval.
- Full access is product-specific. It can mean a broad application role, unrestricted tools, or— in some implementations—execution without a sandbox and without approval prompts.
A sandbox and an approval policy solve different problems. The sandbox defines technical boundaries such as writable paths and network reachability. The approval policy defines when a request must stop for review. They can be combined: a read-only sandbox can ask for approval before an action outside the sandbox, while a writable workspace can still require approval for network access or production changes. OpenAI’s agent security guidance illustrates these separate controls.
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Look at the complete permission surface
Before selecting a mode, map every boundary the agent can cross. A setting that protects one layer does not automatically protect the others.
Identity and application role
The agent acts as a user, service account, or delegated application identity. That identity determines which tenants, repositories, cloud resources, customer records, and administrative functions are even eligible for access. Give it only the roles needed for the task. Google Cloud recommends an agent identity with necessary permissions rather than a broad standing role, and Microsoft cautions against granting an Azure Owner role without a specific administrative scenario and strong governance.
Tools and side effects
Separate tools that retrieve information from tools that cause an external effect. Search, calculation, and document retrieval are generally lower-impact than sending an email, changing a CRM record, deleting a file, creating infrastructure, or modifying an access policy. A tool can also expose only selected operations or fields; a CRM update might permit changing a contact note while blocking ownership, billing, and permission fields.
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Data and resource scope
Read-only does not mean harmless. A search agent that can read confidential files can still disclose them in its response or pass them to another tool. Restrict data to the authorized user or tenant, approved indexes, required folders, and the minimum customer or production scope. Microsoft’s examples include knowledge-base search limited to approved indexed content and CRM updates limited to an allowed customer scope.
Filesystem and execution sandbox
A sandbox can make some paths read-only, confine writes to a workspace, block access to host resources, or isolate a job entirely. It is a technical enforcement layer, not a promise about the model’s intentions. OpenAI’s documentation describes read-only and workspace-write patterns for Codex, with approvals available when an action falls outside the permitted boundary.
Network egress
Network permission is independent of file permission. An agent that cannot write to your repository may still exfiltrate data if it can call arbitrary external endpoints. Restrict outbound traffic to approved destinations, and treat package downloads, webhooks, DNS access, and cloud metadata endpoints as separate decisions.
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Credentials and secrets
Credentials available inside an execution environment are available to code the agent generates. OpenAI states this plainly: Agent-generated code can access the files, credentials, and network available to its environment.
Keep long-lived secrets outside the environment where possible, broker short-lived credentials through a policy-controlled service, and scope each token to the exact resource and operation required.
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How read-only mode works
In read-only mode, the agent can inspect data that its identity and sandbox expose but cannot write through the constrained surface. This is useful for analysis, code review, incident triage, documentation search, and planning.
What read-only is good for
- Searching an approved knowledge base and citing relevant records.
- Reviewing source code, configuration, or logs without modifying them.
- Drafting a change plan, query, email, or infrastructure patch for a person to apply.
- Comparing files or diagnosing an error while preserving the original workspace.
What it does not guarantee
- It does not prevent disclosure of sensitive information that the agent is allowed to read.
- It does not block network exfiltration if outbound connections remain enabled.
- It does not stop a separate connected tool from performing writes if that tool has its own permissions.
- It does not make generated code safe if the runtime exposes credentials or host resources.
Use read-only as a capability boundary, then apply identity, data-scope, network, and logging controls around it. For example, a read-only support agent should see only the customer’s authorized tickets, not every tenant in the database.
When approval-required mode should stop the agent
Approval-required mode inserts a review point before a defined operation. The approval can be requested for every action, for actions outside a workspace, for network access, or for high-impact tools such as send, delete, deploy, and permission changes.
Define a precise approval boundary
Write down the action, target, scope, approver, and duration covered by an approval. A useful policy distinguishes between approving one email and approving all outbound mail for a session, or between one database update and unrestricted writes to a production database. Microsoft recommends that policy checks determine the user, tenant, agent, tool, target resource, scope, approval requirement, and logging or escalation path before tool execution.
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A person can approve a malicious or destructive suggestion, especially when a prompt is rushed or the target is difficult to inspect. Google Cloud describes a Human-in-the-Middle pattern in which a human approves each proposed action and warns that users may approve harmful actions without sufficient verification. The action must still be limited by the identity’s rights, sandbox, network policy, and data scope.
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Enforce the gate outside the model
Do not rely on the agent to remember that it needs permission. Microsoft Learn puts the principle directly: The important part is that authorization is enforced outside the model.
A policy service or tool gateway should inspect the request, evaluate the identity and target, record the decision, and only then execute the tool. If the model can call the underlying API directly, an approval prompt in the chat is merely advisory.
What full access can mean
Full access is a broad label, not a portable specification. Confirm whether it grants a role in one application, all tools in a workspace, unrestricted filesystem access, unrestricted network access, or a combination.
In Codex documentation, a dangerous full-access option is described as running without a sandbox and without approvals. OpenAI’s Auto-review article explains that its Codex Full Access mode has no rules enforced by the environment; malicious code execution, data exfiltration, and credential probing are therefore not blocked by that sandbox. That description applies to that product’s mode, not to every product that uses the words full access.
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Compare configurations by their real boundaries
| Configuration | Permitted side effects | Typical approval boundary | Primary controls to verify |
|---|---|---|---|
| Read-only sandbox | Inspection, search, analysis, and drafting without writes through the constrained surface | Actions outside the sandbox or through separate write-capable tools | Readable paths, tenant and record scope, network egress, credential exposure, logs |
| Writable workspace with approvals | Edits inside an allowed workspace; external sends, production changes, or other gated operations can pause | Outside-workspace writes, network access, deployment, deletion, and access changes | Writable paths, tool allowlist, approver identity, one-action versus session approval, rollback |
| Agent-only operation | Actions proceed without waiting for a human at each step | Preconfigured policy checks rather than synchronous approval | Least-privilege identity, isolation, prompt-injection defenses, error handling, monitoring |
| Product-specific full access | May include unrestricted writes, tools, network, or execution without a sandbox | May have no approval barrier | Exact product definition, credential placement, egress limits, kill switch, recovery and audit |
A practical least-privilege design process
- Describe the task in one sentence. For example, “summarize approved support articles” needs less access than “deploy a database migration.”
- List required reads. Name the exact tenant, repositories, folders, indexes, records, and fields. Remove broad search access that the task does not need.
- List every possible side effect. Include writes, sends, deletes, provisioning, permission changes, package installation, web requests, and secret retrieval.
- Create separate identities or tools for separate effects. Give the drafting tool no send permission, and expose a send tool only after policy approval. Keep production administration separate from development access.
- Set the execution boundary. Choose read-only or a specific writable workspace, define allowed paths, and isolate the process from the host.
- Set network policy. Allow only approved endpoints and protocols. Decide whether package registries, webhooks, cloud APIs, and metadata services are needed.
- Broker credentials. Prefer short-lived, operation-specific tokens outside the agent environment. Never place a master key in a directory the agent can read.
- Define approvals. Specify which tool calls pause, who can approve, what target and scope the approval covers, and when it expires.
- Log and test. Record the request, agent decision, policy result, tool call, target resource, and outcome. Test denied reads, denied writes, unexpected destinations, prompt injection, and rollback before production use.
Patterns that fit common workplace tasks
Knowledge-base search
Give the agent read-only access to an approved index, filtered by tenant and user authorization. It can retrieve and summarize articles, but it should not browse the underlying document store or expose records from another customer.
Email drafting
Permit access to the relevant thread and a draft area, but withhold send permission. A separate send operation should verify recipients, attachments, data classification, and the final approval before transmission.
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CRM updates
Expose only allowed fields and customer scope. Require approval for changes to ownership, billing, consent, deletion, or other high-impact fields. Log the before and after values so an operator can reverse an error.
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Use a narrow role for the specific environment and resource type. Keep production deployment behind a policy check and human review, while allowing the agent to plan or test in an isolated environment. A read-only inventory tool can remain available even when the deployment tool is disabled.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Human-in-the-loop versus agent-only operation
Human-in-the-loop operation asks for approval before each defined action. It is easier to inspect during early deployment and for high-impact workflows, but it can create approval fatigue. Agent-only operation avoids waiting and can handle repetitive low-risk work, but security depends more heavily on programming, isolation, monitoring, and error handling. Google Cloud identifies prompt injection, insecure tool chaining, and poor error handling as risks for autonomous operation.
The choice is not binary across an entire system. Let an agent search, calculate, and draft autonomously; require approval for external communication, deletion, production changes, and access administration. Apply independent authorization and sandbox limits in both modes.
Sandbox, network, and credential safeguards
OpenAI’s sandbox security guidance recommends isolating workloads, restricting outbound traffic to approved endpoints, and keeping application and third-party credentials outside the environment where possible. These controls address different failure paths:
- A sandbox limits what generated code can touch locally.
- Network policy limits where data can leave or what services can be called.
- Credential isolation limits what an attacker or injected instruction can impersonate.
- Least-privilege roles limit the damage even if a token is misused.
Use all four for high-impact agents. A sandbox alone cannot prevent a permitted credential from changing a remote system, and an approval prompt cannot repair an overpowered identity.
Logging, recovery, and operational governance
Managed configuration, constrained execution, network policies, and agent-native logs are complementary controls, according to OpenAI’s description of running Codex safely. Logs should connect the original request to the agent’s proposed decision, policy evaluation, tool execution, resource touched, and final outcome. Include timestamps, identity, approval record, input and output hashes where appropriate, and before-and-after values for updates.
Recovery must be designed before enabling writes. Use versioned files, database transactions, deployment rollbacks, soft deletion where possible, and a tested kill switch. If an agent can modify access controls, ensure an independent administrator can revoke its identity even when the agent or its host is malfunctioning.
Codex examples and a current-product caution
Codex documentation gives concrete examples of how controls can be combined: --sandbox read-only --ask-for-approval on-request pairs a read-only sandbox with approval when an action needs to cross the boundary. A workspace-write configuration can allow edits in a workspace while still requiring approval outside it or for network access. Documentation also describes a dangerous full-access option without sandboxing or approvals. Exact flags and behavior can change, so verify the current product documentation before copying a command.
OpenAI’s Help Center distinguishes policy requirements, which members cannot override, from defaults, which establish starting values within those requirements. Its global, local, and cloud environment settings apply to the particular ChatGPT Work and Codex policy scope described there; do not assume the same precedence in another product.
How to decide what permission to grant
- Choose read-only when the agent can deliver value through inspection, analysis, or drafting and no direct mutation is necessary.
- Choose approval-required for any action with an external side effect, sensitive data movement, production impact, deletion, financial consequence, or access change. Keep the underlying identity and sandbox narrow.
- Choose autonomous execution only for bounded, reversible, well-monitored tasks whose failure impact is acceptable without a person watching every step.
- Treat full access as an exception requiring a written justification, isolated environment, scoped credentials, restricted egress, comprehensive logs, and a tested recovery path.
What vendor statistics can and cannot tell you
OpenAI’s 2026 Auto-review article reports that Codex sessions stopped for human approval roughly 200 times less often in Auto-review mode than in manual approval mode, and that Auto-review approved around 99% of the small fraction of actions routed for review. In an illustrative internal deployment snapshot, 720 out-of-sandbox actions would have interrupted a user under manual approval; Auto-review rejected 7, four continued through a safer path, and three stopped for user input.
Those figures are vendor-reported internal results. OpenAI says the ratios depend on the use case, environment, and sandbox configuration, so they are not a cross-product safety benchmark or a guarantee that an approval policy will catch harmful actions. Use your own logs and incident data to tune thresholds.
The bottom line on safe permissions
Give an agent the smallest identity, data scope, tool set, filesystem, network access, and credential set that can complete its job. Use read-only boundaries to prevent unnecessary mutation, approval gates for consequential actions, and independent policy enforcement so a model cannot bypass either. Reserve broad or full access for isolated, reversible environments with strong monitoring and recovery. The mode name is only a starting point; the real safety decision is the exact boundary around each action.
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