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A medical topic is not automatically prohibited in Microsoft Foundry. A request can still be blocked if a harm-category filter flags it at or above the deployment’s configured severity threshold, or if Prompt Shields detects a prompt attack and is set to block. To find the cause, inspect the response annotations and the guardrail configuration attached to the specific deployment; the user-facing error alone may not identify which control intervened.
Why a medical question can be blocked
Foundry content filters classify content into categories and severity levels; they are not a blanket ban on health or medical discussion. Microsoft’s Guardrails and controls overview says safe-level content may include language touching on violence, self-harm, sexual content, or hate in general, journalistic, scientific, medical, or similar professional contexts. Safe content is annotated, not filtered. A block therefore points to a classification at a blocking severity or to another control, but the topic alone cannot establish which one.
The four harm categories described in Foundry documentation are hate, sexual, self-harm, and violence. A medical question may be classified under one of these categories because of its content or context; that classification does not by itself establish that the question is unsafe or that the filter made a correct decision.
Identify which control acted and where
Start with the preserved API response and determine whether the intervention occurred on the user’s prompt or on the model’s generated output. Foundry supports filtering at both stages, and the settings associated with the deployed model matter. A message shown in an application may not expose all the annotations needed to diagnose the event.
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- Capture the event: Record the exact request, response, model deployment, time, and any returned filter annotations. Keep the API response rather than relying only on the interface’s error message.
- Check for Prompt Shields: Prompt Shields detect attacks in user prompts and in documents. Their response distinguishes whether an attack was
detectedfrom whether it wasfiltered. If the annotation identifies a Prompt Shields detection, review its subtype. - Locate the intervention stage: Determine whether the user input or generated output was filtered. Check the configuration actually associated with that model deployment, not just a project-level or presumed default setting.
- Check the harm-category result: If the response identifies a harm category, compare its reported severity with the threshold configured for that category.
How thresholds affect harm-category filtering
A configured threshold determines which severity level and higher are flagged for a category. The overview describes safe-level content as annotated rather than filtered; safe is not a configurable blocking severity in that overview. Thresholds and available configuration options depend on the relevant service and deployment, so do not assume that a particular adjustment is available or appropriate without checking the attached configuration.
Microsoft’s Azure AI Content Safety FAQ says threshold choice depends on the dataset, community guidelines, and risk tolerance, and recommends starting at severity level 4 for new projects. That is general Content Safety guidance, not a universal recommended setting for every Foundry model deployment. Use it as context, not as a substitute for the deployment’s documented options and your application’s risk requirements.
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When Prompt Shields may be responsible
Prompt Shields handle user-prompt attacks and attacks embedded in documents; they are separate from harm-category severity filtering. Depending on configuration, detections can be annotated or blocked. A benign medical request that includes quoted text, pasted material, or an attached document may therefore be affected by document-attack scanning even if the medical subject itself is not the cause.
For a suspected false positive, Microsoft’s Prompt Shields troubleshooting guidance recommends switching from block to annotate mode to log detections without filtering, then reviewing the triggering subtype. Where appropriate, it also suggests considering an exemption for trusted sources from document-attack scanning. Treat annotate mode as a controlled diagnostic: it gathers evidence but does not resolve the underlying model or policy concern.
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Test a change before using it in production
Do not lower a threshold or disable a control solely because one legitimate request was blocked. First confirm which control acted, then test representative benign medical questions alongside unsafe cases against the relevant input and output paths. If Prompt Shields is implicated, test user-prompt and document-attack scanning separately where those settings are available. Keep the application’s safety requirements in view when weighing fewer false positives against the risk of allowing harmful content.
Language is another factor. Microsoft says the harm-category models described in its content-filter category documentation were trained and tested on English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese. Microsoft also cautions that detection accuracy and false-positive rates may vary and advises thorough testing. This does not establish a false-positive rate for medical questions in any language.
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Changes to or disabling of filters may have approval constraints depending on the service and configuration. Confirm the applicable requirements before making deployment changes.
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- Work at the speed of your ideas – Built with the latest Qualcomm Snapdragon X2 Elite (12 Core) processors, Surface Laptop delivers fast, AI‑accelerated performance—making it the most powerful Surface laptop for everything from multitasking to demanding workloads.
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