Generative AI Security — A Practical Guide to Securing Your AI Application
The pace of innovation in generative AI offers immense opportunities while also introducing new security challenges. As organizations seek to leverage generative AI for innovation, security leaders must take concrete steps to enable rapid experimentation without compromising security.
We will then discuss key strategies for securing generative AI applications, including threat modeling, guardrails, observability, and evaluation of effectiveness of security measures. Through case studies and practical examples, we will show how to apply these strategies in real-world scenarios. Attendees will learn how to identify and mitigate potential risks, and how to evaluate the effectiveness of their security measures.
Target Audience: Security leaders, developers, practitioners, decision makers, software architects, CTOs
Prerequisites: Interest to learn about new technology
Level: Advanced
Manuel Heinkel is a Solutions Architect at AWS, working with software companies in Germany to build innovative and secure applications in the cloud. He supports customers in solving business challenges and achieving success with AWS. Manuel has a track record of diving deep into security and SaaS topics. Outside of work, he enjoys spending time with his family and exploring the mountains.
Puria Izady is a Solutions Architect at AWS, helping software companies in Germany build innovative SaaS solutions. With a background in machine learning and generative AI, he brings technical expertise and business acumen to design AI/ML driven applications at scale. Puria collaborates closely with customers, understanding their needs and tailoring solutions to meet their specific goals. Before joining AWS, Puria worked in a global bank where he modernized a monolithic stack to a scalable and agile architecture while ensuring security and compliance. Outside of work, Puria enjoys exploring new countries and fine-tuning his secret pizza recipe.
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