Build the workflow so Sanity stores only recruitment content that belongs there, candidate records remain in a private, access-controlled system, and a backend service sends Gemini only the minimum information needed for a defined administrative task. Keep the model out of candidate ranking and selection: a recruiter must review generated work, and applicable legal duties depend on the jurisdiction and use case.
What “zero trust” means for this workflow
Neither Sanity nor Gemini makes a recruitment system zero-trust by itself. Treat zero trust as a set of controls to design, test, and maintain: authenticate each actor, grant only the access needed for a task, limit what crosses system boundaries, and verify how the workflow behaves when access or services fail.
A useful boundary is between recruitment content and candidate records. Sanity can manage content such as role descriptions or approved recruiter-facing materials. Candidate applications and other sensitive records belong in a private dataset or another appropriately controlled system. A backend service mediates any permitted task between that system and Gemini.
For a bounded clerical task, the service might extract skills explicitly stated in an application or draft a recruiter summary. It should not ask Gemini to decide whether someone is suitable, rank applicants, or accept or reject a candidate. Generated text is assistance, not evidence of a candidate’s qualifications; retain the underlying source references and make the accountable recruiter responsible for reviewing, correcting, or disregarding it.
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Do not put applicant information in a Sanity dataset that is publicly readable. Sanity’s documentation on authentication and tokens says unauthenticated users have read access to published content by default in many cases. Confirm the access behavior of the actual project and dataset rather than assuming that unpublished status or an intended private use makes records inaccessible.
| Design choice | What it means | Safer fit for this workflow |
|---|---|---|
| Sanity-hosted candidate content | Candidate data is stored alongside or in a Sanity dataset. Access depends on that dataset’s configuration and the permissions of each principal. | Use only if the dataset is appropriately private and the organization can enforce and verify the required access boundaries. Do not place applicant records in a publicly readable dataset. |
| Separate candidate system | Candidate records stay in a private, appropriately controlled system; Sanity remains focused on recruitment content. | Prefer this separation when Sanity’s content workflow does not need to be the system of record for applications. Let the backend fetch only the fields required for each approved task. |
The separation is a design recommendation, not a claim that one storage product is inherently compliant. Decide what information is necessary for the task, where it is stored, who can access it, and how long it is retained.
Build the workflow around separate identities
Use distinct permissions for recruiters or editors, workflow operators, and the service that connects systems. A person’s or token’s effective access is the combination of all grants attached to it, not just the narrow role created for this workflow.
| Identity | Appropriate scope | What to verify |
|---|---|---|
| Recruiter or content editor | Access to the records and content needed for that person’s work. | Check dataset and document access, including any additional roles the user holds. |
| Workflow operator | Ability to manage or monitor the workflow without automatically receiving broad candidate-record access. | Separate operational duties from permission to read or change applicant data wherever the system allows. |
| Backend service identity | Only the read and write permissions required for its defined task. | Review all grants on the identity, including inherited or additional broad permissions; keep credentials out of client-side code. |
| Google Cloud workload identity or service principal | Access required to call the selected Gemini service and any explicitly enabled features. | Apply separation of duties and review the exact IAM roles and controls for the deployed model and configuration. |
Sanity’s roles can scope permissions to datasets and documents, but permissions are additive: a broad role held alongside a restrictive one still provides the broader access. Sanity’s Roles documentation, dated September 9, 2026, makes this an important check for every user and token. Do not validate a restricted role in isolation; test the combined grants that each principal actually has.
Implement the workflow in controlled steps
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Define one narrow task
Write down the specific administrative output Gemini may produce, the minimum source fields needed to produce it, who can request it, and which human must review it. For example, a task could produce a draft summary based only on application text supplied for that purpose. Exclude selection, ranking, and accept-or-reject decisions from the model’s role.
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Keep the service credential on the backend
Sanity recommends a dedicated robot token with appropriate permissions for an application or third-party service. Create a distinct service identity, restrict its scope to the task, and keep the token on the backend rather than in a browser or other client. Rotate it under the organization’s credential process. Sanity’s Authentication and tokens documentation was dated September 23, 2026.
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Use a webhook as a trigger, not as authorization
A Sanity document webhook can notify a service when a document is created, updated, or deleted. Have the backend validate each event before acting, then fetch only the fields permitted for the task. Do not treat an event payload as authority to read or change any record. Sanity documents that mutating a document requires read and write permission for the affected document type; give the service only the permissions it needs. Webhook behavior is described in Sanity’s Webhooks API reference, dated April 15, 2026.
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Call the selected Gemini service through a deliberate cloud boundary
Configure the backend’s access to Gemini using Google Cloud IAM separation of duties. Before sending candidate information, identify the exact model, region, and enabled features, then check which security controls apply to that combination. Google documents controls including data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency, but support varies by model and feature. Do not assume a control applies until the current support information has been checked for the chosen configuration.
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Minimize the request and preserve reviewability
Send only the information needed for the defined task. Label the response as generated, preserve references to the source material, and make it possible for a recruiter to inspect the relevant source and correct or ignore the draft. These safeguards help make the workflow reviewable; they do not guarantee that an output is accurate, fair, or unbiased.
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Decide what is retained
Google Cloud states that it will not use customer data to train or fine-tune AI/ML models without prior permission or instruction. That is not the same as a promise of zero retention across every feature. Google’s Vertex AI and zero data retention documentation, dated January 2, 2026, describes two specific retention behaviors: Grounding with Google Search stores prompts, contextual information, and generated output for 30 days; and in-memory caching for published Gemini models is enabled by default with a 24-hour time-to-live, though it can be disabled at the project level. The same documentation says abuse-monitoring prompt logging may apply to customers governed by Google Cloud Platform Terms. Review current service terms and the features enabled in the actual project against organizational policy before sending applicant information.
Test permissions, failures, and removal of access
Authorization should be tested with the real combinations of roles and service identities used in production, not only with an idealized restricted account. Keep an audit record of relevant access and workflow actions, and give recruiters a clear way to correct or disregard generated material.
- Test access to published and unpublished records, including attempts by unauthenticated users where relevant to the dataset configuration.
- Test a user or token that has both a restrictive grant and a broad grant; confirm the effective access is understood and acceptable.
- Send invalid or unexpected webhook events and verify the backend rejects them rather than treating them as permission to fetch or mutate data.
- Simulate webhook retries and model errors. Confirm the service does not create misleading duplicate or incomplete work, and that a person can see when the task did not complete.
- Remove or narrow a user’s or service’s access and verify that subsequent requests no longer succeed.
- Check that outputs remain labeled as generated and can be traced to the permitted source material used for the task.
These are implementation checks based on the documented access model; they are not a vendor certification or a guarantee of legal compliance.
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The applicable hiring rules cannot be determined without knowing the deployment jurisdiction, employer type, and whether AI is used only for clerical assistance or has a role in evaluating candidates. The technical documentation described here does not settle recruitment-specific duties such as notices, accessibility measures, impact assessments, or human-review requirements. Have qualified HR and legal reviewers assess the specific workflow before it can affect candidate selection, and revisit that assessment if the model, data, region, or workflow role changes.
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