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The correct setup depends on which Oracle product is running your agent. OCI Generative AI Agents uses OCI Identity and Access Management (IAM) policies; Oracle AI Data Platform adds content guardrails in a visual agent flow; and Fusion AI Agent Studio uses job roles, workflow security, application roles, and (in some implementations) data-security policies. These are different administration surfaces, so a click path or policy from one cannot be treated as universal Oracle guidance.
Build the control model in three separate layers: authorization (which human or service principal may access a resource or invoke a capability), guardrails (what content the agent may process or return), and workflow approval (which consequential action must pause for an authorized person). Oracle’s shared-responsibility model means your configuration of identity, data access, tools, workflow permissions, approval gates, monitoring, and recovery materially affects the result.
Choose the Oracle surface before changing a permission
Start by identifying where the agent is built and run. Use the following map before opening a policy editor or Security Console.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Oracle surface | Primary access control | Additional control | Where approval belongs |
|---|---|---|---|
| OCI Generative AI Agents | OCI IAM policies on Generative AI Agents resource types | Compartment boundaries, policy conditions, and isolation of agent tools and data | In the application or workflow that invokes an action; IAM alone is not an approval gate |
| Oracle AI Data Platform | The platform’s identity and application authorization around the agent | A connected guardrails node for content moderation, prompt-injection detection, and PII handling | In the consuming workflow or application; a guardrails node does not approve business actions |
| Fusion AI Agent Studio | Fusion job roles, workflow Security settings, application security roles, and applicable data-security policies | Approval frameworks and end-to-end execution traceability | In the Fusion workflow for actions that require human review |
Also distinguish configuration access from runtime access. A person who can build, edit, or publish an agent is not automatically the right person to invoke it in production, and a runtime user should not receive design privileges merely to use a deployed experience.
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Use one control model: principal, capability, data, action, evidence
For every agent capability, write down five things before assigning access:
- Principal: the named user, group, dynamic group, application, or service identity that makes the request.
- Capability: the resource operation or tool the principal needs, such as reading a knowledge base, starting a session, or invoking a workflow.
- Data boundary: the compartment, application, record set, or other scope in which the capability is valid.
- Action risk: whether the result only informs someone, changes data, sends an external message, moves money, or produces another consequential outcome.
- Evidence: the log, execution path, approval record, or other trace that proves who requested, approved, and executed the action.
This inventory prevents a common mistake: granting a user broad access because an agent needs a tool, or assuming a content filter can enforce an authorization rule. IAM and Fusion roles answer “may this principal access or invoke it?” Guardrails answer “should this content be processed or returned?” Approval answers “must a person review this proposed action before it executes?”
Configure OCI Generative AI Agents with least-privilege IAM
OCI Generative AI Agents exposes separate resource types, including agents, knowledge bases, data sources, ingestion jobs, endpoints, work requests, and sessions. A resource-family policy can cover a broader collection, but a family grant is wider than a grant to one resource type. OCI documents cumulative access levels from inspect to read to use to manage; select the lowest level that lets a group perform its assigned work.
1. Identify the principal and its job
Decide whether the request comes from an administrator, an agent developer, an operations group, an application identity, or a dynamic group used by a service. Describe the job in operational terms: for example, “maintain ingestion jobs in the development compartment” or “invoke the production endpoint without changing the agent.” Do not start with a tenancy-wide administrator policy.
2. List the exact resource types and operations
Map the job to the resources it actually touches. A developer may need to edit an agent and its data source; an operations identity may need to inspect work requests and use an endpoint; an ingestion service may need access to a particular bucket and ingestion job. Keep these grants separate when the responsibilities belong to different people or services.
3. Choose the narrowest compartment and condition scope
Scope the policy to the compartment that contains the agent resources instead of the entire tenancy whenever the service supports that boundary. OCI’s documented patterns also show dynamic-group access constrained by compartment, bucket, and tags. Use those conditions only where they are supported for the resource and operation you are securing.
For the cited ingestion-job policy use case, OCI warns that object-name or prefix conditions are not supported. Do not assume a condition that works for object storage will work for an ingestion job or another Generative AI Agents resource. Check the current IAM reference for each resource type before relying on a condition.
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4. Select the access level deliberately
| Access level | Use it when the principal needs to | Typical risk |
|---|---|---|
inspect |
See basic metadata without reading the resource contents | Information disclosure about resource existence or configuration |
read |
Read the resource or configuration but not change it | Exposure of agent, data-source, or knowledge-base details |
use |
Invoke or operate the resource without managing its definition | Runtime effects, consumption, or downstream actions |
manage |
Create, update, delete, or administer the resource | Full configuration and availability impact |
The table describes the meaning of OCI’s documented levels, not a universal role template. A person who needs to invoke a session may need use but not manage; an ingestion operator may need a different combination. Confirm the operation-to-level mapping in the current service documentation.
5. Check inherited and overlapping policies
After writing the narrow policy, inspect group membership, dynamic-group membership, parent-compartment policies, and tenancy-level grants. OCI permissions accumulate; a restrictive-looking statement does not cancel a broader statement inherited elsewhere. Test with the same principal that will run the agent, not only with an administrator account.
6. Validate the boundary with positive and negative tests
- Confirm the intended principal can perform each required operation in the intended compartment.
- Confirm the principal cannot read, use, or manage a neighboring compartment or unrelated resource.
- Test the service identity separately from the human who designed the agent.
- Record the policy version, test identity, resource OCIDs or names, and result so a later change can be compared.
IAM authorization still does not decide whether an external payment, record update, or message should wait for a person. Put that decision in the application or workflow approval mechanism.
Separate an agent’s tools and data from its users
A user’s permission to invoke an agent and the agent’s permission to call a tool are different trust relationships. Oracle’s agent design guidance notes that isolating tools and data can be useful when sensitive or high-impact capabilities should be available only to responsible agents.
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When to use specialist agents
Use a specialist agent when one capability has a materially different boundary: for example, a procurement agent that can prepare a purchase request and a separate finance agent that can submit it. Give each agent only the data sources and tools required for its role. This reduces the blast radius if a prompt, integration, or credential is misused.
When a supervisor/executor pattern fits
A supervisor can interpret a request and route work, while an executor owns a narrowly scoped tool. The pattern adds orchestration, testing, and monitoring overhead, so use it when the separation produces a real security or accountability benefit rather than simply adding more agents.
What this pattern does not do
Agent isolation does not replace user authorization. A user still needs permission to invoke the experience, and the executor still needs permission to access its resource. Test both paths: an authorized user with an unauthorized tool, and an unauthorized user attempting to reach an otherwise valid tool.
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Add explicit guardrails in Oracle AI Data Platform
Oracle AI Data Platform’s additional guardrails are configured in the visual flow. Oracle states: “By default no guardrails are applied to your system beyond the model provider’s native safety controls.” Treat that as an instruction to configure the node deliberately, not as evidence that the platform is unprotected in every respect; IAM, application authorization, and the model provider’s native controls remain separate layers.
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1. Add and connect a guardrails node
- Open the agent’s visual flow in Oracle AI Data Platform.
- Add a guardrails node at the supported point where traffic should be checked.
- Connect the node to the relevant input or response path. An unconnected node does not enforce a rule on another path.
- Select the guardrail categories required by your policy: content moderation, prompt-injection detection, and PII handling.
- Publish or deploy the flow using the platform’s normal release process, then test the live path.
2. Configure content moderation
Content moderation controls whether disallowed or unsafe content continues through the flow. Oracle recommends keeping the Block action for content moderation. If your organization chooses a different behavior where the product permits it, document who approved the exception and what downstream control compensates for it.
3. Configure prompt-injection detection
In this documentation, prompt-injection controls apply to the user input query. Oracle recommends the Block action. Do not describe this node as an IAM policy: it evaluates content at the flow point where it is connected, while IAM determines whether a principal can access the surrounding resource.
4. Choose a PII response
PII can be handled with block, inform, mask, or allow. A new agent allows PII in both input and response by default, so inspect that setting and choose intentionally. Mask can reduce exposure while allowing a workflow to continue; block is appropriate when processing the data is not permitted; inform warns without necessarily stopping the flow; and allow should be reserved for a documented requirement.
The documented guardrails are available only in English. If users submit other languages, do not assume equivalent detection or blocking. Define a language-appropriate compensating control or restrict the supported input language, and verify the behavior in the deployed flow.
5. Test each path and failure mode
- Submit allowed content and confirm it reaches the model or next node.
- Submit a moderation case and verify that Block stops the request rather than merely logging it.
- Submit a prompt-injection test in the user query and verify the configured response.
- Use representative PII in both input and generated response to confirm the selected action on each direction.
- Attempt the same request with a principal that lacks IAM or application authorization; a guardrail pass must not grant access.
Grant Fusion AI Agent Studio runtime access
Fusion AI Agent Studio uses Fusion security constructs rather than OCI resource policies for runtime access. The following sequence reflects Oracle’s documented user-access workflow; product-specific experiences can require additional data-security policies and a security synchronization process.
1. Create a custom job role in Security Console
Create a role for the actual runtime responsibility instead of assigning a broad administrator role. Name and document whether the role is for invoking an agent, administering a workflow, or publishing an application.
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2. Enable the required permission groups and privileges
In the custom role, enable the applicable permission groups, add the Access Intelligent Agent Chat privilege, and add the Fai Genai Agent Runtime Duty role. Verify that the role contains the permissions required by the target workflow rather than copying unrelated privileges.
3. Assign the custom role to users
Assign the role to the users or groups who should use the runtime experience. Keep builders and publishers in separate administrative roles unless their job genuinely combines those duties.
4. Add the role to the workflow Security settings
Open the AI Agent Studio workflow’s Security settings, add the custom role, and publish the workflow. A role assigned only to a user but not recognized by the workflow will not establish the intended runtime path.
5. Associate the role with the application and publish
Associate the same role with the application’s security roles, then publish the application. Test through the application experience, not only from the design interface.
6. Apply product-specific data security and synchronize
Some Fusion contexts require additional data-security policies and a security synchronization process. Check the documentation for the target product and experience, complete synchronization when required, and retest after propagation. A successful role assignment does not prove that the user can see every underlying business record.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set approval limits without inventing an Oracle-wide threshold
Oracle’s reviewed material describes approval frameworks and human review for higher-impact actions, but it does not publish a universal transaction-value, record-count, or duration cutoff for all Oracle AI agents. Any number your organization uses is a local policy choice unless a product-specific source documents a different rule.
Classify actions before choosing a gate
| Question | Lower-risk indication | Higher-risk indication |
|---|---|---|
| Impact | Provides advice or a draft for a person to review | Changes a financial, legal, employment, customer, or operational record |
| Data sensitivity | Uses public or low-sensitivity data | Uses regulated, confidential, or personal data |
| Reversibility | Can be undone cleanly with an audit trail | Is irreversible, time-critical, or difficult to remediate |
| External effect | Stays inside a work queue | Sends a message, releases funds, changes access, or commits an external transaction |
Require a human checkpoint when the combination of impact, sensitivity, irreversibility, or external effect exceeds your organization’s tolerance. The limit can be categorical rather than numeric: for example, “all access changes require approval” or “all externally binding communications require review.” If you do adopt a numeric limit, label it as your organization’s policy and review it separately from Oracle’s product capabilities.
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Make the approval gate meaningful
- Show the proposed action, affected resource or record, material inputs, and expected result to the approver.
- Require an approver whose role is authorized for that action and independent enough to provide meaningful review.
- Prevent execution until the approval decision is recorded, unless the documented workflow explicitly supports a controlled exception.
- Record the requester, agent or service identity, approver, decision, timestamp, policy version, and execution result.
- Handle rejection, timeout, duplicate submission, and partial failure paths explicitly; do not silently retry a consequential action.
- Verify that the final log connects the approval to the exact action that executed.
Oracle’s March 24, 2026 product announcement describes Fusion Agentic Applications as providing “role-based access, approval frameworks and end-to-end traceability, including step-by-step actions and full execution paths for accountable decision-making.” That statement establishes the product-level governance capabilities; it does not establish that every Fusion workflow exposes identical approval fields or a configurable numeric threshold.
Keep authorization, guardrails, and approval tests separate
A reliable test plan exercises each layer independently and then tests their interaction.
| Test | Expected result | Layer being validated |
|---|---|---|
| Authorized principal reads an allowed resource | Request succeeds within the intended compartment, application, or workflow | Authorization |
| Same principal attempts an out-of-scope resource | Request is denied | Authorization boundary |
| Allowed prompt and response pass through the flow | Agent continues normally | Guardrail placement |
| Moderated or injection test is submitted | Configured Block action stops processing | Content guardrail |
| PII appears in input and output | Selected block, inform, mask, or allow behavior occurs in both directions as configured | PII guardrail |
| High-impact action is requested | Workflow pauses and presents the actual action to an authorized approver | Approval |
| Approver rejects or a tool fails | No unintended execution; the outcome is traceable | Recovery and auditability |
Troubleshoot the common failure modes
The user can open the agent but cannot perform a required operation
For OCI, inspect the resource type, access level, compartment, condition, and inherited policies. For Fusion, verify the custom role, permission groups, runtime duty, workflow Security setting, application security role, and any required synchronization. A user’s ability to open a screen does not prove that the underlying tool or data operation is authorized.
A guardrails node exists but unsafe content is still processed
Confirm that the node is connected to the actual input or response path, that the flow version containing it was published, and that the action is set to Block where required. Test the deployed endpoint rather than only the design canvas.
PII behavior differs between requests and responses
Check both directions. Oracle documents separate input and response handling, and a new agent permits PII in both by default. Confirm the selected action for each path and test with representative data.
An approval appears in the interface but the action executes anyway
Trace the workflow from request to tool invocation. Verify that execution is downstream of the approval decision, that rejection and timeout branches terminate safely, and that a separate integration is not bypassing the gate.
A policy condition works for one resource but not another
Conditions are service- and resource-specific. Review the current IAM reference; in the documented ingestion-job case, object-name and prefix conditions are not supported. Replace an unsupported condition with a supported compartment, bucket, or tag boundary and retest.
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Operational checklist for production
- Identify whether the implementation is OCI Generative AI Agents, Oracle AI Data Platform, or Fusion AI Agent Studio.
- Document configuration access separately from runtime access.
- List every human and service principal, resource type, tool, data source, and workflow action.
- Use the narrowest OCI IAM level and compartment or supported condition; inspect inherited grants.
- Isolate high-impact tools and sensitive data in specialist or supervisor/executor agents when justified.
- Add and connect Oracle AI Data Platform guardrails explicitly; choose moderation, prompt-injection, and PII actions deliberately.
- For Fusion, complete the custom role, required privileges and duty, workflow Security assignment, application association, publication, and any data-security synchronization.
- Define approval categories from impact, sensitivity, reversibility, and external effect; label any numeric value as local policy.
- Require the approver to see the real action and preserve a trace from request through execution.
- Run positive, negative, guardrail, approval, rejection, timeout, and recovery tests with production-like identities and data.
- Review policies, roles, guardrail settings, approval rules, and logs after material agent or application changes.
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