AI Risk 101: How to test your AI systems before users do

​You’ve built an AI system. It works in demos. But can you really trust it in the wild?

​AI breaks in ways regular software doesn’t. It can hallucinate, say risky things, leak data, or perform inconsistently with unexpected inputs. These aren’t just engineering bugs — they’re product risks: brand damage, compliance violations, lost trust.

​To build AI apps that are reliable, safe, and production-ready, you need a systematic, repeatable AI testing process that aligns with your use case, industry, and internal policies. 

​Join this hands-on webinar to learn how to identify AI risks, define test strategies, and introduce structured testing into your AI product workflow.

We’ll cover:
⚠️ Common AI failure modes: hallucinations, unsafe outputs, jailbreaks, PII leaks, brand risks
✅ Testing techniques — stress tests, red-teaming, regression testing for LLMs
🔡 How to create and use synthetic data for AI testing
💻 Practical example: testing AI app with Evidently

​Who is it for?

​This session is for AI product teams, AI risk and governance experts, ML engineers, data scientists, and tech leads who are:

• ​Building or deploying LLM-powered applications

• ​Concerned about real-world risks like unsafe outputs and compliance issues

• ​Looking to move beyond "vibes-based" testing and towards structured, policy-aligned evaluation workflows

​​Speaker:

​​Elena Samuylova — CEO & Co-founder at Evidently AI, the company behind Evidently, an open-source framework for AI evaluation with 25+ million downloads.