Introduction to MLSecOps

Learn how to build security into your machine learning and AI lifecycles with MLSecOps.

Introduction

• The power of MLSecOps

1. Introduction to MLSecOps

• What is MLSecOps?
• The benefits of AI risk awareness in organizations
• Key MLSecOps categories of assurance explained
• Understanding the MLSecOps framework

2. Applying MLSecOps to Secure the AI Lifecycle

• Map, measure, manage, and govern
• AI attack vectors and vulnerabilities
• Introduction to threat modeling for AI systems
• Customized threat models
• Strategic threat analysis
• Ensuring adversarial robustness
• Secure model deployment and monitoring

3. The MLSecOps Dream Team

• Building the team: Ownership and roles
• Introduction to the Violet teaming integrative framework
• Facilitating cross-collaboration for MLSecOps implementation
• Empowering MLSecOps stakeholders with team training

4. MLSecOps Implementation and Strategy: Risk Assessment and Incident Response

• Step-by-step: Infusing MLSecOps into existing processes
• Foundations for AI/ML risk assessments and assurance
• AI incident response plans
• Audit, inventory, and supply chain

Conclusion

• Mastering MLSecOps: Safeguarding AI in the modern era