Content filtering our LLM responses but it's not clear why

Hello Malek Ascha!

Intermittent and unexplained content filtering by Azure OpenAI can definitely be confusing, especially when logs show no violations against standard categories (like violence or hate). Here are some insights and advice based on how Azure OpenAI content filtering usually works:

1. Filters Can Trigger on Complex, Subtle Patterns: Sometimes content gets flagged by machine learning models for nuanced reasons, such as ambiguous phrasing or context that can be interpreted as risky—even if it doesn’t strictly fall under violence, hate, etc.

Policy Updates and Hidden Criteria: The Azure OpenAI content filters evolve regularly, sometimes adding or adjusting sensitivities (for privacy, legal, or compliance reasons) that aren’t published in detail. This can lead to inconsistent behavior.

Logs Generally Lack Full Reasoning: Unfortunately, current AzureDiagnostics or ContentFilter logs rarely give detailed reasons for a rejection. Typically, you just see which broad category (if any) triggered, and sometimes it’s not reported at all if the trigger was very low confidence.

Protected Modes Don’t Override All Filters: Settings to not filter protected code/text, or similar policy options, typically only tweak the most basic rule-based filters—not the newer neural network-driven content policies.

What You Can Try:

If possible, review the exact student present-level statements being filtered, looking for any unintentionally sensitive terms (privacy, medical, legal language, abbreviations that could have double meanings, etc.).

Test by gradually changing or segmenting your prompts—if filtering suddenly stops for certain wordings, you may be able to narrow down the “risk phrase.”

For persistent problems, escalate to Azure Support with the full prompt, details, and logs—Microsoft can sometimes share more about the filter category or internal scoring that wasn’t exposed in user logs.

Join the Azure OpenAI dev support forums or user feedback groups, as other organizations sometimes share patterns that triggered unexplained filtering.

Ultimately, even very “safe” educational or business content can get caught if the filter model has overly broad or evolving safety nets. Keeping a running log of affected samples and reporting them is best for improving filter transparency over time.

Best regards

Jerald Felix

Hello Malek Ascha!

Agree with content ambiguity in user prompts mentioned by Jerald. Some time it might mis-flag some user prompt.

On playground, you might not get clear classification reason. Please SDK to find the reason of rejection.

Please also check in Microsoft purview resource which might block sensitive information in operation.

Please share few prompts which might be getting blocked to replicate the issue.

Reference

https://techcommunity.microsoft.com/blog/azuredevcommunityblog/content-filtering-with-azure-ai-studio/4247355

https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/openai/azure-openai/samples ( SDK)

Thank you

Hello Malek Ascha!

Did you get any chance to check the above response

Thank you