Modern IT and OT security requires clear control over complex, data-driven AI systems that are directly integrated into business processes. Security architects in the DACH region face the challenge of embedding resilience from the ground up as LLMs and AI services penetrate enterprises via APIs, plugins, and SaaS platforms, often bypassing traditional asset inventories and security audit pipelines. 

This masterclass provides a practical framework for operationalizing AI governance through automated trust checks. Starting with the problem of uncontrolled AI sprawl, the session takes an architectural look inside AI agents to uncover systemic vulnerabilities. In the intensive hands-on segment, participants will interact directly with an engineering harness to manually enforce external security boundaries around AI systems. In live scenarios, critical risk vectors, such as alignment bypass, runtime anomalies, and IP exfiltration are simulated and mitigated.

By directly comparing these technical safeguards with regulatory requirements, this session demonstrates how abstract compliance can be translated into a concrete, resilient AI governance program that empirically demonstrates the value of each individual control.

During the session patripants will configure and test an external protection shield (harness) to secure LLMs and agent networks in real time. They will simulate attacks directly on the system and learn how automated controls stop them.

Participants leave with:

• A ready-to-use framework for detecting shadow AI and systematically prioritizing vulnerabilities in your infrastructure
  
• A clear methodology for mapping the technical controls from the masterclass directly to regulatory requirements
• Practical techniques for measuring control effectiveness using data-driven evidence
  

Ideal for:

• Security Architects
• Detection & Response Engineers
• Cloud / Platform Security Engineers
• AI / ML Engineers with Security Responsibility
• Senior Security Engineers supporting CISOs