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Put a moderation gateway between your users and the image-generation API. It can accept a prompt as plain text, identify the tenant, load that tenant’s versioned rules, call a standalone moderation endpoint, and decide whether to allow, review, or block the request—without wrapping the prompt in a Chat Completions or Responses conversation. For stronger protection, check the generated image as well: a safe prompt does not guarantee a safe result.
This separates the classifier’s standard signals from your product’s tenant-specific decisions. It also gives you a place to audit decisions, handle uncertain scores, and apply one tenant’s policy without changing another tenant’s rules.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhat does tenant-aware prompt moderation mean?
Tenant-aware moderation means that the same moderation service can apply different documented safety policies for different customers, organizations, or workspaces. A tenant is the policy boundary: it might be a business customer, a school, a community, or another account grouping in your application. The classifier can return categories and scores, but your gateway uses the authenticated tenant’s policy to interpret those signals.
The key separation is between classification and enforcement. Classification asks what signals the prompt or image contains. Enforcement asks what your application should do for this tenant: allow generation, block it, or send it for review. A classifier’s flagged result is not, by itself, a complete tenant policy.
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“Without Chat JSON” means you do not need to create a Chat Completions or Responses message transcript just to get a moderation decision. OpenAI’s moderation endpoint accepts text as a string or as an item in an array of text and image inputs; omni-moderation-latest supports text and images. Your application can keep its own moderation request and response contract separate from its chat or image-generation request formats.
How should a moderation gateway process an image prompt?
Use a service boundary that authenticates the tenant, resolves the applicable policy, checks the prompt, records a decision, and only then sends allowed requests to image generation. Keep the stages explicit so you can tell whether a decision was made about input text, a reference image, or generated output.
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- Resolve tenant policy. Load the policy associated with the authenticated tenant. It should define enabled categories, thresholds, actions, review requirements, and any locale or age constraints relevant to your product.
- Submit the moderation input. Send the prompt as a plain string or as a text item in a multimodal input array when the request also includes images. Do not manufacture a chat transcript solely to obtain a moderation result.
- Normalize the response. Convert the classifier’s fields into an internal object your application can use consistently: decision, categories, scores, policy version, model identifier, and request identifier. Preserve the category’s applicable input type when available.
- Apply the tenant’s rules. Evaluate the normalized signals against that tenant’s policy. A flagged category can trigger a block, review, transformation, or allowance depending on the rule; scores inform policy and routing rather than representing universal truth.
- Generate only when allowed. Send the original prompt or a policy-approved version to the image-generation API. Link its generation request to the moderation record.
- Check the output before delivery. If your application creates an image, moderate the generated image before showing or distributing it. Record this as an output-stage check, distinct from the original prompt check.
- Handle blocked requests clearly. If generation returns a documented
moderation_blockederror, record the stage as input, output, or unknown when you can establish it, log coarse categories for developers, and give the end user a concise remediation message.
This flow does not require the end user to understand classifier categories. The gateway translates those signals into the behavior that the tenant has chosen and exposes only the explanation appropriate for that user.
What should each tenant’s policy contain?
Keep policy data outside the model prompt and, where practical, outside scattered application conditionals. A versioned configuration makes tenant differences inspectable and gives you a stable record of which rules produced a decision.
| Policy field | What it controls |
|---|---|
| Tenant and policy version | Identifies whose rules were applied and which immutable revision was in effect. |
| Enabled categories | Defines which classifier categories are relevant to this tenant’s use case. |
| Thresholds | Provides tenant-specific score boundaries for actions or escalation. These are policy choices, not universal model truths. |
| Action by category or outcome | Maps signals to allow, review, block, or a policy-approved transformation. |
| Review requirements and queue | States which uncertain or high-risk decisions need a human and where they should be routed. |
| Locale or age constraints | Captures applicable product requirements that affect handling or permitted content. |
| User-facing explanation | Provides a clear, tenant-appropriate explanation when a request is blocked or needs revision. |
| Failure behavior | Defines what to do if moderation cannot return a usable result, according to the tenant’s risk tier. |
Keep prior policy versions immutable rather than overwriting them. If an appeal or incident requires you to reconstruct a decision, the record should identify the exact policy version, model, scores, input types, decision, and any reviewer outcome. A new rule set should have a new version, so you can distinguish changes in policy from changes in model behavior.
For example, one tenant may send a category to review while another blocks it; the classifier can supply the same category signal to both, while the gateway applies different actions. Avoid burying such distinctions in untracked prompt text or in a chain of tenant-specific conditionals that is difficult to audit.
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How do moderation scores and flagged categories become a decision?
The moderation result is an input to your decision process, not a complete product decision. OpenAI’s documented result includes an overall flagged value, per-category flags, category scores, and information about the input types to which a category applies. Use the details that matter to your policy rather than treating one top-level Boolean as the only signal.
An internal contract can preserve those classifier signals alongside the application’s decision context. This illustrative example follows the documented field concepts; the policy version and decision are application-level fields:
{
"tenant_id": "t_123",
"policy_version": "2026-10-01",
"decision": "review",
"categories": {"sexual": false, "violence": true},
"scores": {"violence": 0.78},
"applied_input_types": {"violence": ["text"]},
"moderation_model": "omni-moderation-latest",
"source_request_id": "modr-..."
}The sample values illustrate the shape of a record, not recommended thresholds or an instruction to treat a score as a probability. Do not copy the example score into a production rule without validating it for your category, model, and policy.
When a result is near a tenant’s threshold, route it according to that tenant’s policy. For higher-risk categories, human review can prevent an ambiguous classifier signal from becoming an automatic false positive or false negative. A review outcome should be stored with the original decision record so you can inspect disagreements and improve the policy.
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Log enough to explain and audit a decision, but minimize personal data. Useful fields include policy version, model identifier, category scores, applicable input types, action, request identifier, and reviewer outcome. OpenAI recommends using category and score data for logging, routing, audit trails, and human-review queues. A stable, privacy-preserving user identifier can help associate activity without placing an unneeded direct identifier in moderation records.
Should you use a standalone classifier or a policy-prompted model?
A standalone moderation classifier is usually the simpler starting point when its standard categories represent the decisions you need. A policy-prompted model can help when a tenant’s taxonomy cannot be expressed cleanly with those fixed categories, but it adds policy-prompt design and maintenance work.
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| Approach | Useful when | Trade-off |
|---|---|---|
| Standalone moderation classifier | You can express the tenant’s requirements using the classifier’s standard categories, flags, and scores. | It provides simpler, structured category signals, but the tenant policy must map those signals to actions. |
| Policy-prompted model | A tenant needs a taxonomy or policy distinction that fixed categories do not represent well. | It allows explicit policy definitions and examples, but prompts and examples become part of the system you must version, test, and maintain. |
Amazon Nova is one documented alternative for customizable moderation. AWS describes policy definitions, few-shot examples, structured XML or JSON output for automated systems, and optional explanations for auditability. It can moderate text and image content and return a violation flag, categories, and an explanation that supports allow, flag, remove, or escalation actions. Whichever classifier approach you select, retain the same gateway responsibilities: tenant authentication, policy versioning, logging, and review routing.
Is input-only moderation enough for image generation?
Input-only moderation is less work and avoids an additional output check, but it cannot establish that the generated image is safe. Image generation is stochastic: a safe prompt does not guarantee a safe result. AWS’s reference architecture checks the prompt first and then sends generated image bytes to Rekognition for another moderation check.
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If your system accepts reference images as well as text prompts, make sure the moderation request covers the inputs your policy is intended to govern. The moderation endpoint supports arrays of text and image inputs, and the result includes information about the input types associated with categories. That makes it possible to distinguish a text signal from an image signal in downstream review and audit records.
When should a request be blocked automatically or sent to review?
Automatic blocking is consistent and predictable when a tenant rule clearly prohibits a category. Review is more appropriate when the policy treats uncertain or context-sensitive cases differently, or when false positives would have a meaningful cost. Use score and category details to route borderline results; do not assume one threshold fits every tenant or category.
- Block: Use when the applicable tenant rule makes the category or result disallowed without review.
- Review: Use when a human decision is required by policy or when a result is uncertain and the category is high risk.
- Allow: Use when the tenant policy permits the result, even if a signal is present but does not meet that tenant’s action rule.
- Transform: Use only where the tenant has documented what changes are permitted and how the modified prompt is handled. A transformation should not silently bypass the moderation decision.
For a review route, preserve the prompt or content only as required by your privacy and retention rules, along with the policy version, score details, and reviewer outcome needed to investigate the case. Keep the end-user response distinct from the internal review record: an end user typically needs to know what to change or that a request is awaiting review, not internal classifier details.
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Should tenant policies live in code or versioned configuration?
Hard-coded policy rules can be quick to implement, but they make per-tenant differences harder to review and reverse as the number of tenants grows. Versioned configuration supports auditable updates and replaying a past decision against the policy version that was actually active.
Configuration does not eliminate code: the gateway still needs to validate the policy, interpret its actions, and fail safely when a policy is missing or malformed. Define a schema and test policy changes before they take effect. Keep the model’s standardized signals separate from tenant-specific thresholds and actions so a model update does not silently rewrite customer policy.
When you change a policy, record who or what initiated the update through your own administrative controls, publish a new version, and make the effective version available to the moderation request. Do not edit a past version in place if you need reliable audit history.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should happen when moderation fails or the model changes?
Treat an unavailable or unusable moderation result as an explicit error state, not as an implicit allow. Choose fail-open or fail-closed behavior by tenant risk tier and document it in policy; the appropriate choice depends on the product’s risk tolerance and what happens if a check cannot complete.
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Keep moderation and generation errors distinct. If moderation fails before generation, do not record it as a classifier block. If the generation service returns moderation_blocked, record the block and its known stage; if the stage cannot be established, use an unknown value rather than guessing. Give users a short explanation and a practical next step, such as revising the prompt, without exposing internal system details.
Recalibrate score-dependent rules when the underlying moderation model changes. OpenAI warns that custom policies relying on category scores may need recalibration over time. Test the new behavior against representative traffic and adversarial cases before treating existing thresholds as stable.
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How do you test a tenant-aware moderation system?
Test both the classifier signal and the policy decision. A prompt can produce an expected category yet still be handled incorrectly if the gateway loads the wrong tenant policy, applies the wrong version, or sends a review case to an automatic block path.
- Test representative prompts for each tenant’s use case and each enabled category.
- Include adversarial inputs such as obfuscated wording, multilingual text, jailbreak attempts, and image-plus-text combinations.
- Verify that two tenants with different policies can receive different actions for the same moderation signals.
- Check that the logged policy version, model, categories, scores, input types, decision, and request identifier match the request that was evaluated.
- Exercise borderline results and confirm the right review queue, end-user message, and reviewer-outcome record.
- Simulate moderation timeouts or invalid results and confirm that the configured fail-open or fail-closed behavior is applied.
- For image generation, test both input checks and generated-image checks, including cases where the prompt passes but the output is blocked.
OpenAI recommends adversarial testing with representative and attack-oriented inputs. Re-run the relevant tests when you change a tenant policy or update the moderation model; these changes can affect different parts of the decision chain.
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Start with the simplest approach that matches your policy needs, then add customization only where a real gap exists.
| Decision | Choose this when |
|---|---|
| Standalone classifier or policy-prompted model | Prefer a standalone classifier if standard categories map to your rules. Consider a policy-prompted model if a tenant’s taxonomy cannot be represented cleanly by fixed categories. |
| Input-only or input plus output moderation | Input-only uses fewer steps, but input plus output checks address the risk that safe prompts can produce unsafe images. |
| Automatic block or review queue | Block clear policy violations; route uncertain high-risk results to review when the tenant’s rules require human judgment. |
| Rules in code or versioned configuration | Code can suit a small fixed policy surface; versioned configuration is more suitable when tenant rules need auditable, reversible updates. |
For most multi-tenant image-generation services, the durable foundation is the same regardless of classifier: authenticate the tenant, load a versioned policy, evaluate structured moderation signals, retain an auditable decision, and check generated images before delivery when your application supports it.
Frequently Asked Questions
Can I use one moderation model for several tenants with different rules?
Yes. The moderation model can provide common category signals while your gateway applies a different versioned policy for each authenticated tenant. Keep those policies separate from the classifier’s output so changing a tenant rule does not require changing the model.
What should I show a user when their prompt is blocked?
Give a concise, actionable explanation that helps them revise the request, without exposing internal scores or security logic. If the request is under human review, say that plainly rather than presenting it as a permanent rejection.
Can moderation detect what an image prompt is trying to produce?
Moderation evaluates the submitted text or image inputs and returns category signals; it does not guarantee what an image generator will create. Generated output should be checked separately before delivery when the application needs to enforce image-safety rules.
How do I avoid storing sensitive user information in moderation logs?
Store only identifiers and content needed for the product’s audit and safety purpose, and prefer a stable privacy-preserving identifier such as a hash over a direct user identifier. Set retention and access controls according to your application’s privacy requirements.
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