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OCI Generative AI does not retain customer prompts or inference inputs, and it does not store inference outputs inside the service. Fine-tuning data stays in your tenancy for the job, is deleted from the fine-tuning cluster when the job completes, and is not used to improve general-purpose use cases. Keeping an OCI AI workload private, however, requires more than that service behavior: you must configure IAM, network paths, authentication, guardrails, encryption keys, and the correct processing region.
This guide explains those controls, what Oracle documents about data handling, and how to verify that a deployment meets your organization’s privacy and residency requirements.
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What OCI Generative AI stores—and what it does not
Oracle’s current data-handling documentation states that OCI Generative AI does not retain customer inputs. Prompts and inference outputs are not stored inside the service. This applies to normal inference requests; it does not mean that every surrounding component in your tenancy is automatically storage-free. Application logs, vector stores, files, notebooks, gateways, or monitoring systems can retain information if you configure them to do so.
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|---|---|---|
| Inference prompts and other inputs | Not retained by OCI Generative AI. | Check client-side logs, API gateways, tracing, notebooks, and support exports for accidental copies. |
| Inference responses | Not stored inside OCI Generative AI. | Apply retention rules to application databases, chat histories, analytics, and observability tools. |
| Fine-tuning training data | Held in the customer’s tenancy for the fine-tuning job; deleted from the fine-tuning cluster after completion. | Restrict object-storage access, set lifecycle policies, and delete source files when they are no longer needed. |
| Fine-tuned custom models | Oracle says they are not shared with the named third-party model providers. | Protect model endpoints, projects, files, and deployment permissions with IAM. |
| Use of customer content for general model improvement | Fine-tuning data is not retained beyond the job and is not used to improve general use cases. | Review any separate Oracle services or third-party integrations that receive the same data. |
Oracle also states that prompts, responses, fine-tuning training data, and fine-tuned custom models are not shared with the named third-party model providers. That statement describes OCI Generative AI’s service boundary; it does not override your own data flows to other OCI services or external applications.
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The OCI AI security control stack
Use OCI AI security as a set of independent controls. A privacy claim about the model service is strongest when each layer is deliberately configured and documented.
| Layer | Primary controls | Decision you must make |
|---|---|---|
| Identity and authorization | IAM policies, compartments, groups, dynamic groups, and resource-level permissions. | Which people, workloads, and compartments can create, tune, deploy, invoke, or delete each resource? |
| Network boundary | Private endpoints and Zero Trust Packet Routing (ZPR). | Can traffic remain on approved private paths, with no public exposure? |
| Application identity | API keys, OAuth, OCI request-signature authentication, and hosted-application tokens. | How does each caller authenticate, and how are credentials rotated or revoked? |
| Runtime safety | Content moderation, prompt-injection defense, and personally identifiable information (PII) handling. | Should sensitive content be masked, blocked, redacted, or sent for human review? |
| Cryptography | Oracle-managed AES-256 encryption by default and optional customer-managed OCI Vault keys. | Who controls key rotation, disablement, and recovery? |
| Residency | Model-hosting region, service integration routing, and tenancy realm. | Where is each request processed, and could an integration cross a border? |
| Operations | Audit logs, work-request review, deletion procedures, and periodic policy review. | How will you prove that the configuration still matches the policy? |
IAM: grant the smallest useful permission
OCI Generative AI access is controlled by IAM policies. The aggregate generative-ai-family covers chat, embeddings, reranking, models, dedicated AI clusters, endpoints, private endpoints, API keys, projects, vector stores, files, hosted applications, deployments, and work requests. It is convenient for administration, but it is broader than most production applications need.
Separate administrative and runtime roles
Create distinct groups or dynamic groups for platform administrators, model operators, application deployers, and inference callers. An administrator may manage compartments, policies, clusters, and endpoints. A model operator may create or update models and fine-tuning jobs but not change tenancy-wide identity policies. An application deployer may manage a specific endpoint or hosted application. An inference workload should normally be able to invoke only the endpoint, model, vector store, or project it needs.
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Oracle recommends broad manage access for administrators or sandbox groups. For production, write policies for the required resource types and compartments instead of assigning the aggregate family everywhere. Review policies whenever you add a project, endpoint, vector store, hosted application, or deployment; new resources can otherwise inherit an unexpectedly broad trust boundary.
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Use compartments as a security boundary
Place development, test, and production resources in separate compartments. Keep fine-tuning files and custom models in compartments that only the training pipeline and approved operators can read. Restrict who can create private endpoints or dedicated AI clusters, because those resources determine how applications reach the model service and how much isolation you receive.
Understand the home-region limitation
IAM resources are global in a tenancy, but their master definitions reside in the tenancy’s home region. The home region is selected when the tenancy is provisioned and cannot be changed. It contains account and identity information; it is not a universal statement that all application or model data is stored there. Do not use the home-region label as a substitute for checking the region of each AI resource and integration.
Network isolation and caller authentication
Private endpoints and ZPR
Private endpoints keep model access within a secure network boundary and prevent public exposure. Place the endpoint in an approved virtual cloud network and restrict route tables, security lists, and network security groups to the application subnets that require access. Zero Trust Packet Routing adds policy-based constraints so packets are allowed only between explicitly authorized resources. Together, these controls reduce the chance that a leaked credential can be used from an untrusted network.
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Choose an authentication method for each caller
- OCI request-signature authentication: suitable for OCI workloads that can sign requests with an instance, resource principal, or user credential.
- API keys: useful for application integrations that cannot use a resource principal; store them in a secret manager, rotate them, and avoid embedding them in source code.
- OAuth: appropriate when an identity provider issues short-lived access tokens and centralized revocation is important.
- Hosted applications: Oracle documents identity-domain bearer tokens, browser-session OAuth 2.1 with PKCE, and OCI IAM request-signature authentication as supported choices.
Give each application its own identity. Shared keys make attribution and revocation difficult, and they encourage excessive permissions.
Guardrails for unsafe content, prompt injection, and PII
OCI Generative AI guardrails can apply content moderation, prompt-injection defense, and PII handling. Configure them according to the consequence of an error rather than treating a single preset as universally safe.
Content moderation
Set moderation thresholds for the categories relevant to your use case, then decide what happens when a threshold is reached: block the request, return a safe error, or route the case to review. Test both user prompts and model responses, because harmful content can enter through retrieved documents or tool results as well as through direct user input.
Prompt-injection defense
Prompt-injection controls help identify instructions embedded in user content or retrieved documents that attempt to override system or application rules. Keep authorization outside the prompt: a model should not be able to grant itself access to a file, call a tool, or change an account merely because text tells it to do so. Enforce those decisions in application code and IAM.
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PII handling
Depending on configuration, PII controls can mask or redact detected data, block responses that contain it, or prompt users to remove or minimize personal details. Define which identifiers are prohibited, which may be transformed, and which business process receives an exception. Masking in a response does not remove the original value from a client-side log or source document, so apply the same policy to every storage and observability layer.
Oracle warns that model output can contain errors or omissions. Guardrails supplement application validation, deterministic business rules, and human review; they do not guarantee factual accuracy, legal compliance, or complete PII detection.
Encryption and control of keys
Oracle documents double encryption at rest for fine-tuning training data, using Oracle-managed AES-256 by default. Customers can instead use customer-managed private keys through OCI Vault when policy requires direct control over key rotation or disablement. Data in motion is encrypted with TLS 1.2.
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Use the default when your policy accepts Oracle-managed key lifecycle operations and you want the simplest deployment. Confirm that all connected storage and logging services meet the same encryption requirement; the model service’s encryption setting does not automatically configure every downstream repository.
When to use OCI Vault keys
Choose customer-managed keys when separation of duties, external audit evidence, or rapid cryptographic revocation is required. Limit Vault administrators separately from AI operators, document rotation intervals, and test the recovery path before disabling a key. A disabled or deleted key can make encrypted training data or models unusable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Data residency: region selection is only the first check
OCI Generative AI model-hosting regions documented by Oracle include the following locations. Model availability and serving mode vary by region, so confirm the specific model before committing to an architecture.
| Location | OCI region |
|---|---|
| São Paulo | sa-saopaulo-1 |
| Frankfurt | eu-frankfurt-1 |
| Hyderabad | ap-hyderabad-1 |
| Osaka | ap-osaka-1 |
| Riyadh | me-riyadh-1 |
| Abu Dhabi | me-abudhabi-1 |
| Dubai | me-dubai-1 |
| London | uk-london-1 |
| Ashburn | us-ashburn-1 |
| Chicago | us-chicago-1 |
| Phoenix | us-phoenix-1 |
| UK Government South | uk-gov-london-1 (separate realm) |
| EU Sovereign Central | eu-frankfurt-2 (separate realm) |
Oracle documents service integrations that can call Generative AI in another region when the caller and destination differ. Published examples route Brazil to São Paulo, European callers to Frankfurt, India to Hyderabad, Japan to Osaka, Saudi Arabia to Riyadh, the United Arab Emirates to Dubai, the United Kingdom to London, and United States callers to Chicago. These examples are routing guidance, not a guarantee for every service or model.
Residency verification procedure
- Record the country, legal entity, and data classes handled by the application.
- Choose a model-hosting region whose commercial availability and serving mode meet the workload’s requirements.
- Inspect every OCI service that invokes Generative AI, including search, agent, hosted-application, and vector-storage integrations.
- Confirm the destination region used by each integration rather than relying on the console’s current region selector.
- Check the tenancy realm, especially for UK Government or EU Sovereign deployments.
- Test with audit records and network telemetry, then retain the configuration evidence for compliance review.
Keep production and backup data in the ordered data-center region where the applicable Oracle service promises that placement. The IAM home region remains a separate identity concept.
Choosing an operating mode
| Choice | Best fit | Security and governance considerations |
|---|---|---|
| On-demand inference | Experiments, variable workloads, and early application development. | Use strict compartments and invocation policies; verify rate limits, model availability, and the endpoint’s network exposure. |
| Dedicated AI cluster | Production systems needing stronger isolation or more predictable performance. | Restrict who can create and manage clusters, place them in the approved region, and monitor work requests and endpoint changes. |
| Public access path | Low-risk prototypes with no confidential input. | Accepts a wider network exposure; never treat TLS alone as a substitute for private routing and least privilege. |
| Private endpoint with ZPR | Confidential, regulated, or internal workloads. | Requires network design and policy maintenance but provides a narrower, auditable path to the model. |
A practical implementation checklist
- Classify the data. Identify PII, regulated records, secrets, intellectual property, and data that must remain in a specific jurisdiction.
- Select the region and model. Confirm that the required model is served in the approved region and record any documented cross-region routing.
- Create compartments. Separate development, production, training data, custom models, and shared services.
- Write least-privilege IAM policies. Avoid tenancy-wide
manage generative-ai-familygrants for application identities. - Build the network boundary. Prefer a private endpoint, approved subnets, restrictive security rules, and ZPR policies.
- Issue per-application credentials. Use request signatures or short-lived OAuth where possible; store API keys as secrets and rotate them.
- Configure guardrails. Enable moderation, prompt-injection defense, and PII actions that match the data classification.
- Choose encryption ownership. Use Oracle-managed AES-256 by default or OCI Vault keys when customer control is required.
- Limit retention around the service. Disable verbose prompt logging unless necessary, set storage lifecycle rules, and delete fine-tuning source files after approved retention.
- Test failure paths. Verify that blocked content, revoked credentials, disabled keys, unauthorized compartments, and unavailable regions fail safely.
- Review continuously. Recheck policies, endpoints, model routing, guardrail versions, and audit evidence whenever a project or integration changes.
Common mistakes and how to correct them
- Assuming “not retained” means “not logged anywhere.” Inspect application, gateway, notebook, and observability settings and redact prompts before they leave the client.
- Using the home region as a residency answer. Document the model-hosting region and each integration’s destination instead.
- Granting the aggregate family to every developer. Replace it with resource- and compartment-specific permissions and separate operators from callers.
- Relying on prompts for authorization. Enforce tool and data access in IAM and application code; treat model instructions as untrusted input.
- Enabling PII masking without testing detection. Use representative samples, measure false negatives, and add deterministic validation for high-impact identifiers.
- Rotating Vault keys without a recovery test. Confirm that workloads can still decrypt required data and that key disablement procedures are understood.
- Assuming region availability is permanent. Model catalog, serving mode, and service integrations can vary by region; revalidate before each production change.
Who is responsible for what?
Oracle describes customer isolation, encryption, security controls, and visibility as core OCI security pillars. Oracle also states that customers retain ownership and intellectual-property rights in their content. The shared-responsibility boundary remains important: Oracle operates the cloud service, while you protect credentials, configure IAM and network controls, secure connected workloads, govern activity in your tenancy, and decide what data enters the system.
For an audit-ready deployment, retain the IAM policy set, compartment map, endpoint and routing configuration, region and model decision, guardrail settings, Vault key ownership record, retention schedule, and evidence from blocked-request and credential-revocation tests. Those artifacts demonstrate not only what OCI Generative AI promises by default, but also how your implementation prevents surrounding systems from undermining that privacy posture.
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