Large Language Models (LLMs) are rapidly transforming the IAM landscape, but how do they actually work, and how can they be applied effectively?
This interactive workshop is designed for IAM leaders, security practitioners, and governance professionals who want to gain a deeper, more pragmatic understanding of LLM-powered systems through hands-on experimentation.
Participants will engage in guided exercises to explore how LLMs process and generate responses, how Retrieval-Augmented Generation (RAG) enhances AI-driven insights, and how to construct their own LLM-powered tools. No prior AI experience is required—attendees can choose their level of participation, from full hands-on engagement to observation.
By the end of the workshop, participants will leave with practical experience, new insights into AI’s role in identity and security, and a roadmap for continued learning. Bring a laptop and an open mind—let’s demystify AI together!
the speaker
Jonathan Sander
President
42 Notions
Compare and contrast LLM outputs from different models to understand their strengths, biases, and limitations.
Experiment with AI-driven chatbots to induce and analyze model errors, including hallucinations.
Build a simple RAG system by embedding specialized knowledge, designing a system prompt, and testing real-world queries.
Explore the future of AI in IAM, including its impact on security, governance, and non-human identity.
Cybersecurity Professionals
CISOs, security architects, and analysts focused on identity management, fraud prevention, and AI security.
Identity and Access Management (IAM) Specialists
Experts managing authentication, user identity verification, and access control systems.
Compliance and Privacy Officers
Professionals ensuring AI-driven identity systems meet regulatory and ethical standards.
Fraud Prevention and Risk Management Teams
Those responsible for detecting and mitigating identity fraud and cyber threats.
Business and IT Leaders
Decision-makers evaluating AI’s role in identity security and digital transformation initiatives.
Traditional identity systems are under pressure from outdated architectures that over-collect and retain data, leading to inaccurate decisions and increased risk.
Understanding and addressing the emerging risks of Non-Human Identities (NHIs) is essential in an era of increasing cyber threats and evolving technologies.
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