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OCI Generative AI security is a control stack, not a single switch. Oracle documents that the service does not retain customer prompts or inference inputs, does not store inference outputs inside the service, and does not use fine-tuning data to improve general-use cases. You still need to choose the right region, restrict IAM permissions, control network paths, enable runtime guardrails, and decide who owns encryption keys.
This guide explains what Oracle says about data handling, how to configure the surrounding OCI controls, and how to verify that a workload stays within the residency and access boundaries your organization requires.
What OCI Generative AI stores and does not store
Oracle’s data-handling documentation draws a distinction between inference and fine-tuning. For ordinary inference, Oracle states: “No. OCI Generative AI doesn’t retain customer inputs.” It also says that both input and output are not stored inside OCI Generative AI. That means a prompt sent to a model and the response returned by that model are not retained by the Generative AI service as a model-training corpus or conversation history.
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Fine-tuning is handled differently because the service must access training files while a job runs. Oracle says the training data remains in the customer’s tenancy, is not retained beyond the fine-tuning job, and is deleted from the fine-tuning cluster after the job completes. Oracle also says that prompts, responses, fine-tuning training data, and fine-tuned custom models are not shared with the named third-party model providers. Fine-tuning data is not used to improve general-use cases.
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| Data or activity | Oracle’s documented handling |
|---|---|
| Inference prompts and other inputs | Not retained by OCI Generative AI. |
| Inference outputs | Not stored inside OCI Generative AI. |
| Fine-tuning training files | Held in the customer’s tenancy, used for the job, and not retained after the job completes. |
| Fine-tuning cluster copies | Deleted when the fine-tuning job completes. |
| Use for general model improvement | Fine-tuning data is not used to improve general-use cases. |
| Sharing with named third-party model providers | Oracle says prompts, responses, fine-tuning data, and fine-tuned custom models are not shared. |
These statements describe the Generative AI service itself. They do not remove your responsibility for data that your application stores before sending a request, caches after receiving a response, places in a vector store, or writes to logs. Review those surrounding systems separately.
Use IAM and compartments to enforce least privilege
OCI Generative AI access is granted through Identity and Access Management (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. Granting that family broadly is convenient for a sandbox but gives a principal access to many resource types.
Oracle recommends broad manage permissions only for administrators or sandbox groups. A production tenancy should narrow both the resource type and the compartment. For example, an application that only calls an existing endpoint should not automatically be able to create models, alter a private endpoint, manage vector stores, or delete a dedicated cluster.
Separate the operational roles
- Platform administrators: Create compartments, networking, policies, private endpoints, and tenancy-wide controls.
- Model operators: Manage model deployments, dedicated AI clusters, fine-tuning jobs, and model endpoints in an assigned compartment.
- Application deployers: Deploy a hosted application or service integration without receiving authority to redesign the platform.
- Inference callers: Invoke an approved endpoint and read only the resources required at runtime.
Use separate groups and policies for these roles. Review policies whenever you add a project, endpoint, vector store, hosted application, deployment, or work request. This prevents a new resource from silently expanding an existing application’s authority.
Remember the home-region rule
IAM resources are global in a tenancy, but their master definitions reside in the tenancy’s home region. Oracle says the home region contains account and identity information, cannot be changed after provisioning, and holds the master IAM definitions. That fact matters for administration and disaster planning; it is not a universal statement that every workload’s data is processed or stored in the home region.
Keep model traffic inside an intended network boundary
A public service path can expose a larger network surface than a private design. Oracle describes private endpoints as a way to keep model access within a secure network boundary and prevent public exposure. Place the endpoint in the virtual cloud network and subnet used by the calling workload, then restrict routes and security rules so only approved applications can reach it.
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Zero Trust Packet Routing (ZPR) is another documented control. Use it to express which resources are allowed to communicate rather than relying only on broad network reachability. A private endpoint and ZPR address different parts of the problem: the endpoint removes the need for public access, while ZPR constrains permitted packet flows between identified resources.
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Choose an authentication method for each caller
OCI supports API keys, OAuth, and OCI request-signature authentication. Select the method according to the caller and its credential-management capabilities, and avoid embedding long-lived secrets in source code or browser-delivered JavaScript.
Service-to-service applications
Backend services commonly use OCI IAM request-signature authentication or an API key associated with a controlled identity. Store private key material in a protected secret-management workflow, rotate it, and grant the identity only the endpoint and operation it needs.
User-facing hosted applications
Oracle documents three hosted-application options: identity-domain bearer tokens, browser-session OAuth 2.1 with PKCE, and OCI IAM request-signature authentication. Browser sessions should use the OAuth flow designed for public clients, while server-side components can use a server-managed identity and request signing. Keep end-user authentication separate from the application’s permission to invoke a model.
Enable runtime guardrails instead of trusting raw model output
OCI Generative AI guardrails can apply content moderation, prompt-injection defense, and personally identifiable information (PII) handling. Configure them at the point where your application invokes the model, and treat their result as one input to your application’s validation logic.
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Content moderation
Moderation policies can identify disallowed or unsafe content in prompts or responses. Define what your application should do when a policy triggers: block the request, return a safe message, send the case for review, or log a minimal event that does not reproduce sensitive text.
Prompt-injection defense
Prompt-injection controls help detect instructions in retrieved documents or user content that attempt to override the application’s intended behavior. They are particularly important for retrieval-augmented generation, where model context may include material supplied by an untrusted source.
PII handling
Oracle describes PII controls that can mask or redact detected data, block responses containing it, or prompt users to remove or minimize personal details, depending on configuration. Choose the action that matches the data category and legal requirement. Redaction may be appropriate for analytics; blocking may be required for a regulated workflow.
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Protect fine-tuning data with encryption and customer-managed keys
Oracle states that fine-tuning training data is encrypted at rest and in transit. The default is double encryption with Oracle-managed AES-256. If your control framework requires customer ownership of the encryption key, OCI Generative AI can use a customer-managed private key through OCI Vault.
Data in motion uses TLS 1.2, which Oracle explicitly documents. Customer-managed keys add an operational dependency: the authorized service must be able to use the Vault key for the duration of the fine-tuning operation, and your key-rotation or disablement process must account for active jobs.
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Key ownership does not change the retention rule. Oracle says the fine-tuning cluster deletes training data when the job completes, and customers can delete the training data in their tenancy as part of their own lifecycle controls.
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Understand which regions host models
Model availability and serving mode vary by region. Oracle’s Generative AI Regions documentation, updated May 6, 2026, lists these model-hosting regions:
| Location | OCI region identifier |
|---|---|
| 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 |
| EU Sovereign Central | eu-frankfurt-2 |
The last two entries are in separate realms. A region appearing in the console does not guarantee that every model or serving mode is available there; verify the model’s current regional support before designing a workload around it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check for cross-region calls before claiming residency
Residency is determined by the complete call path, not only by the region selected in a console. Oracle documents that some OCI services call into Generative AI and that a request can cross regions when the calling and destination regions differ.
Oracle’s documented routing examples map Brazil callers to São Paulo, European Union callers to Frankfurt, India callers to Hyderabad, Japan callers to Osaka, Saudi Arabian callers to Riyadh, United Arab Emirates callers to Dubai, United Kingdom callers to London, and United States callers to Chicago. These examples are routing guidance, not a guarantee that every integration uses the same path.
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- Record the region where that caller runs and the destination region configured for Generative AI.
- Check the integration’s current regional behavior, including any service-managed routing.
- Confirm that the selected model and serving mode exist in the destination region.
- Document the resulting path in your data-residency assessment and repeat the check after regional or model changes.
For a strict EU, UK, US, or sovereign-region requirement, validate every service that invokes Generative AI rather than assuming that co-locating your application and tenancy home region is sufficient.
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Do not confuse the IAM home region with workload residency
The tenancy home region is fixed after provisioning and stores account and identity information and the master IAM definitions. Oracle separately states that, for Oracle application services, production and backup data remain in the ordered data-center region. Consequently, the home region is an administrative anchor, not a universal destination for application data, prompts, outputs, or backups.
Document these as separate fields in an architecture record: tenancy home region, application-data region, Generative AI model-hosting region, backup region, and any cross-region service route. This prevents an IAM fact from being used as an inaccurate residency claim.
Compare deployment choices using the controls that matter
| Decision axis | Less restrictive choice | Stronger control choice |
|---|---|---|
| Residency path | Rely on a service integration’s default destination | Select and verify a local model-hosting region and document any cross-region route |
| Network exposure | Public service access | Private endpoint with ZPR and restrictive network rules |
| Identity scope | Broad tenancy or generative-ai-family permissions |
Resource- and compartment-level policies for separate operational roles |
| Encryption ownership | Oracle-managed double encryption with AES-256 | Customer-managed OCI Vault key where required |
| Runtime safety | Model moderation and application defaults | Explicit content, prompt-injection, and PII guardrails plus application validation |
| Operational mode | On-demand experimentation | Dedicated AI clusters for production isolation and more predictable performance |
There is no single “private mode” that applies all of these choices. A production design normally combines the stronger option in each row that matches its threat model, regulatory obligations, and operating capacity.
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A practical implementation sequence
- Classify the data. Identify whether prompts, retrieved documents, outputs, or fine-tuning files contain personal, confidential, export-controlled, or regulated information.
- Choose the serving region. Select a region that supports the required model and serving mode, then record the region identifier and realm.
- Map the call path. Identify every OCI service and application that can invoke Generative AI and verify whether its request stays local or crosses regions.
- Create compartments by responsibility. Separate platform networking, model operations, application deployments, and data stores so policies can follow ownership boundaries.
- Write narrow IAM policies. Start with the smallest resource types and compartments required; reserve broad
managepermissions for administrators or isolated sandboxes. - Deploy private access. Use a private endpoint, restrictive security rules, and ZPR controls where the workload must not use public exposure.
- Select caller authentication. Use request signatures, API keys, or OAuth according to whether the caller is a backend service, hosted application, or browser session.
- Configure guardrails. Set content moderation, prompt-injection defense, and PII actions, then define the application’s response to each trigger.
- Set key ownership. Keep Oracle-managed AES-256 when it meets your policy; otherwise configure an OCI Vault customer-managed key and test its permissions and rotation process.
- Test failure and change paths. Verify that unauthorized identities cannot invoke or modify resources, that a blocked PII response is handled correctly, and that changing a model or region triggers a new residency review.
Shared responsibility still applies
Oracle’s security overview identifies customer isolation, encryption, security controls, and visibility as core pillars. Oracle says customers retain ownership and intellectual-property rights in their content. The customer remains responsible for protecting credentials, configuring services, securing workloads, and governing activity in the tenancy.
In practice, Oracle’s non-retention statement does not absolve an organization from securing its own prompts before transmission, its retrieved context, its application logs, its Vault keys, or its IAM principals. Treat those systems as part of the same security boundary when you approve an OCI AI workload.
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