Merge pull request #3895 from lgayhardt/release-preview-eval-redteaming

Metric doc update, links in other docs and preview

Author: AnnaMHuff

articles/ai-foundry/concepts/ai-red-teaming-agent.md

@@ -11,7 +11,7 @@ ms.author: lagayhar
 author: lgayhardt
 ---
 
-# AI Red Teaming Agent
+# AI Red Teaming Agent (preview)
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 

articles/ai-foundry/concepts/evaluation-approach-gen-ai.md

@@ -56,10 +56,10 @@ Pre-production evaluation involves:
 The pre-production stage acts as a final quality check, reducing the risk of deploying an AI application that doesn't meet the desired performance or safety standards.
 
 - Bring your own data: You can evaluate your AI applications in pre-production using your own evaluation data with Azure AI Foundry or [Azure AI Evaluation SDK’s](../how-to/develop/evaluate-sdk.md) supported evaluators, including [generation quality, safety,](./evaluation-metrics-built-in.md) or [custom evaluators](../how-to/develop/evaluate-sdk.md#custom-evaluators), and [view results via the Azure AI Foundry portal](../how-to/evaluate-results.md).
-- Simulators and AI red teaming agent: If you don’t have evaluation data (test data), Azure AI [Evaluation SDK’s simulators](..//how-to/develop/simulator-interaction-data.md) can help by generating topic-related or adversarial queries. These simulators test the model’s response to situation-appropriate or attack-like queries (edge cases).
+- Simulators and AI red teaming agent (preview): If you don’t have evaluation data (test data), Azure AI [Evaluation SDK’s simulators](..//how-to/develop/simulator-interaction-data.md) can help by generating topic-related or adversarial queries. These simulators test the model’s response to situation-appropriate or attack-like queries (edge cases).
     - The [adversarial simulator](../how-to/develop/simulator-interaction-data.md#generate-adversarial-simulations-for-safety-evaluation) injects queries that mimic potential safety risks or security attacks such as or attempt jailbreaks, helping identify limitations and preparing the model for unexpected conditions.  
     - [Context-appropriate simulators](../how-to/develop/simulator-interaction-data.md#generate-synthetic-data-and-simulate-non-adversarial-tasks) generate typical, relevant conversations you’d expect from users to test quality of responses. With context-appropriate simulators you can assess metrics such as groundedness, relevance, coherence, and fluency of generated responses.
-    - AI red teaming agent simulates adversarial attacks against your proactively stress-test models and applications against broad range of safety and security attacks using Microsoft’s open framework for Python Risk Identification Tool or [PyRIT](https://github.com/Azure/PyRIT). Automated scans using the AI red teaming agent enhances pre-production risk assessment by systematically testing AI applications for vulnerabilities. This process involves simulated attack scenarios to identify weaknesses in model responses before real-world deployment. By running AI red teaming scans, you can detect and mitigate potential security risks before deployment. This tool is recommended to be used in conjunction with human-in-the-loop processes such as conventional AI red teaming probing to help accelerate risk identification and aid in the assessment by a human expert.
+    - [AI red teaming agent](../how-to/develop/run-scans-ai-red-teaming-agent.md) (preview) simulates adversarial attacks against your proactively stress-test models and applications against broad range of safety and security attacks using Microsoft’s open framework for Python Risk Identification Tool or [PyRIT](https://github.com/Azure/PyRIT). Automated scans using the AI red teaming agent enhances pre-production risk assessment by systematically testing AI applications for vulnerabilities. This process involves simulated attack scenarios to identify weaknesses in model responses before real-world deployment. By running AI red teaming scans, you can detect and mitigate potential security risks before deployment. This tool is recommended to be used in conjunction with human-in-the-loop processes such as conventional AI red teaming probing to help accelerate risk identification and aid in the assessment by a human expert.
 
 Alternatively, you can also use [Azure AI Foundry’s evaluation widget](../how-to/evaluate-generative-ai-app.md) for testing your generative AI applications.  
 
@@ -91,6 +91,7 @@ Cheat sheet:
 ## Related content
 
 - [Evaluate your generative AI apps via the playground](../how-to/evaluate-prompts-playground.md)
+- [Run automated scans with the AI red teaming agent to assess safety and security risks](../how-to/develop/run-scans-ai-red-teaming-agent.md)
 - [Evaluate your generative AI apps with the Azure AI Foundry SDK or portal](../how-to/evaluate-generative-ai-app.md)
 - [Evaluation and monitoring metrics for generative AI](evaluation-metrics-built-in.md)
 - [Transparency Note for Azure AI Foundry safety evaluations](safety-evaluations-transparency-note.md)