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Azure OpenAI in Microsoft Foundry Models includes default safety policies that apply to all models, excluding transcription models. These configurations provide a responsible experience by default, including content filtering models, blocklists, prompt transformation, content credentials, and other features.
Guardrails and controls ensure that AI-generated outputs align with ethical guidelines and safety standards. Azure OpenAI provides Guardrail capabilities to help identify and mitigate risks associated with various categories of harmful or inappropriate content. Default safety aims to mitigate risks in different categories such as hate and fairness, sexual, violence, self-harm, protected material content, and user prompt injection attacks. To learn more, see categories and severity levels.
All safety policies are configurable. To learn more about configurability, see configuring Guardrails.
When content is detected that exceeds the severity threshold for a risk category, the API request is blocked and returns an error response indicating which category triggered the filter. This applies to both user prompts (input) and model completions (output).
Prerequisites
- An Azure subscription with access to Azure OpenAI Service
- Deployed Azure OpenAI models (excluding transcription models, which use different safety configurations)
Text models
Text models in Azure OpenAI can take in and generate both text and code. These models leverage Azure's text content filters to detect and prevent harmful content. This system works on both prompts and completions.
| Risk category | Prompt or completion | Severity threshold |
|---|---|---|
| Hate and fairness | Prompts and completions | Medium |
| Violence | Prompts and completions | Medium |
| Sexual | Prompts and completions | Medium |
| Self-harm | Prompts and completions | Medium |
| User prompt injection attack (jailbreak) | Prompts | N/A |
| Protected material – text | Completions | N/A |
| Protected material – code | Completions | N/A |
Vision models
Vision-enabled chat models
| Risk category | Prompt or completion | Severity threshold |
|---|---|---|
| Hate and fairness | Prompts and completions | Medium |
| Violence | Prompts and completions | Medium |
| Sexual | Prompts and completions | Medium |
| Self-harm | Prompts and completions | Medium |
| Identification of individuals and inference of sensitive attributes | Prompts | N/A |
| User prompt injection attack (jailbreak) | Prompts | N/A |
Image generation models
| Risk category | Prompt or completion | Severity threshold |
|---|---|---|
| Hate and fairness | Prompts and completions | Medium |
| Violence | Prompts and completions | Medium |
| Sexual | Prompts and completions | Medium |
| Self-harm | Prompts and completions | Medium |
| Content credentials | Completions | N/A |
| Deceptive generation of political candidates | Prompts | N/A |
| Depictions of public figures | Prompts | N/A |
| User prompt injection attack (jailbreak) | Prompts | N/A |
| Protected material – art and studio characters | Prompts | N/A |
| Profanity | Prompts | N/A |
Audio models
| Risk category | Prompt or completion | Severity threshold |
|---|---|---|
| Hate and fairness | Prompts and completions | Medium |
| Violence | Prompts and completions | Medium |
| Sexual | Prompts and completions | Medium |
| Self-harm | Prompts and completions | Medium |
| User prompt injection attack (jailbreak) | Prompts | N/A |
| Protected material - text | Completions | N/A |
| Protected material - code | Completions | N/A |
Severity levels
Note
The text content filtering models for the hate, sexual, violence, and self-harm categories are specifically trained and tested on the following languages: English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese. However, the service can work in many other languages, but the quality might vary. In all cases, you should do your own testing to ensure that it works for your application.
Text content
Warning
The Severity definitions tab in this document contains examples of harmful content that may be disturbing to some readers.