As AI systems grow in complexity and importance, safety engineering is essential. This masterclass delivers a focused, technical guide to building reliable, interpretable, and aligned AI systems capable of withstanding threats, failures, and regulatory challenges. Participants will learn key safety architectures, red-teaming strategies, and compliance frameworks crucial for successful deployment in high-stakes settings.

What You’ll Learn

• Core principles of AI safety engineering, including robustness, interpretability, and alignment with human intent.
• Techniques for detecting and mitigating adversarial attacks, model drift, and failure modes in real-time systems.
• Implementing safety layers in LLMs and autonomous agents, including red-teaming, sandboxing, and human-in-the-loop oversight.
• Navigating technical requirements of emerging AI safety standards (EU AI Act, NIST AI RMF, ISO/IEC 42001).

Key Takeaways

• A technical toolkit for AI safety, including robustness testing checklists, monitoring frameworks, and fail-safe design patterns.
• Templates for red-teaming exercises, incident response protocols, and post-deployment audit workflows.
• Why This Matters AI safety is no longer optional—it’s a prerequisite for trust, adoption, and regulatory approval. Engineering resilient systems that can explain their behavior, recover from failure, and align with human values is critical to scaling AI responsibly. This session equips technical teams with the tools to operationalize safety across the AI lifecycle.

Who Should Attend

AI engineers, safety researchers, risk officers, compliance leads, and product teams working in:

• Autonomous Systems
• Regulated Industries
• AI Governance & Policy
• Enterprise AI Deployment