Private by Design and Secure by Default AI Products

In charge of deploying AI but not sure how to protect and secure these systems? Want to learn how to architect around potential security vulnerabilities and privacy gotchas? Do you need to design AI systems that meet trust, privacy and security guidelines?

This intensive, one-day masterclass goes beyond theoretical concepts, empowering experienced engineers and architects to proactively build privacy and security into AI products by design.

Move beyond reactive measures by learning:

• Real-World Threat Modeling: Identify vulnerabilities in your AI systems.
• Hands-On Red Teaming: Execute and evaluate attacks on models.
• Meta Prompt Engineering & Guardrails: Learn how to create useful and more privacy-aware meta prompts. Use guardrails to identify insecure prompts or questionable AI output.
• Data Flow Analysis, Risk Assessment, Privacy Controls: Map and mitigate privacy and confidentiality risks in your data workflows. Choose appropriate protections for identification, sanitization and pseudonymization.
• Practical Model Evaluation Strategies: Build evaluation datasets and integrate security & privacy testing into your deployment workflow.

Designed for those just starting out or already familiar with AI concepts, this masterclass provides actionable insights, practical tools, and a clear framework for building more trustworthy AI solutions. You’ll leave equipped to design and deploy better privacy and security within your organization.

Note: No deep math or stats background required (although it’s great if you have one!).