AI Safety and Reliability - The industry emphasizes the importance of ensuring the safety and reliability of autonomous AI agents [1] - Azure AI Evaluation SDK's Red Teaming Agent is designed to uncover vulnerabilities in AI agents proactively [1] - The tool simulates adversarial scenarios and stress-tests agentic decision-making to ensure applications are robust, ethical, and safe [1] Risk Mitigation and Trust - Adversarial testing mitigates risks and strengthens trust in AI solutions [1] - Integrating safety checks into the development lifecycle is crucial [1] Azure AI Evaluation SDK - The SDK enables red teaming for GenAI applications [1]

Core Concept - Trustworthy AI is defined as the combination of AI Security and AI Safety, crucial for AI systems [1] Key Strategies - Building trustworthy AI requires product and engineering teams to collaborate on AI that is aligned, explainable, and secure [1] - MLSecOps, AI Red Teaming, and AI Runtime Security are three focus areas that contribute to achieving both AI Security and AI Safety [1] Resources for Implementation - Modelscan (https://github.com/protectai/modelscan) is a resource for MLSecOps [1] - PyRIT (https://azure.github.io/PyRIT/) and Microsoft's AI Red Teaming Lessons eBook (https://ashy-coast-00aeb501e.6.azurestaticapps.net/MS_AIRT_Lessons_eBook.pdf) are resources for AI Red Teaming [1] - Pillar Security (https://www.pillar.security/solutionsai-detection) and Noma Security (https://noma.security/) offer resources for AI Runtime Security [1] Demonstrating Trust - Vanta (https://www.vanta.com/collection/trust/what-is-a-trust-center) provides resources for showcasing Trustworthy AI to customers and prospects [1]