[Live Session] Secure AI Agents: Understanding automated Red Teaming and AI Evals

The rapid adoption of LLMs and AI agents brings opportunities, but also exposes your solutions to critical security, privacy, and quality risks. How do you proactively secure your AI applications before malicious actors or benign users trigger unexpected failures in production?

In this live session, we will explore how to systematically uncover vulnerabilities in your AI agents using automated continuous red teaming. We will also explain how to generate realistic and domain-specific test cases using knowledge bases and persona simulations.

Agenda:

• AI Red Teaming 101: Understand the fundamentals of LLM vulnerabilities: from prompt injections and data leakage to hallucinations.
• Automating Red Teaming: launch vulnerability scans to execute specialized attacks against your AI agent, and learn how each attack maps directly to OWASP categories.
• Generate domain-specific tests: Learn how to ground your AI evaluation in your company’s unique context by leveraging your Knowledge Bases (KBs) to generate highly specific and business-oriented test cases.
• Simulate realistic user interactions: We will demonstrate how to stress-test your AI by simulating complex back-and-forth scenarios, such as an angry customer or a manipulative user.
• Conclusion and Q&A

Who Should Attend: AI Product Managers, Heads of AI, AI/ML engineers, AI security professionals, Data scientists, and anyone building or deploying GenAI applications who wants to ensure their models are safe, reliable, and production-ready.