Practitioner’s Playbook for RSAIF 

Target group

AI Security Professionals looking to enhance their practical skills in securing AI systems and managing risks across the AI lifecycle.

Data Scientists and Engineers who want to integrate security into AI model development and deployment pipelines.

AI Governance and Compliance Officers seeking to gain a deeper understanding of security measures and regulatory requirements for AI systems.

Tech Leads and Managers who oversee AI projects and need to ensure secure and ethical AI practices within their teams.

Cybersecurity Experts aiming to specialize in AI-specific threats and enhance their threat modeling and risk mitigation strategies.

Course structure

Module 1: AI Security Foundations – Responsible Development & Secure Design

1. 1.1 Overview of AI Security Challenges
2. 1.2 Secure Design Principles
3. 1.3 Best Practices for Secure AI
4. 1.4 Hands-On: Threat Modeling Workshop

Module 2: AI Threat Models

1. 2.1 Introduction to Threat Modeling
2. 2.2 Creating an AI Threat Model
3. 2.3 Tools for Threat Modeling
4. 2.4 Case Study: AI in Autonomous Vehicles

Module 3: Secure AI SDLC (Software Development Lifecycle)

1. 3.1 SDLC Overview
2. 3.2 AI-Specific Security Measures
3. 3.3 Continuous Monitoring & Feedback Loops
4. 3.4 Hands-On: Integrating Security in AI Development
5. 3.5 Use Case: AI Fraud Detection System

Module 4: Enforcement & Model Integrity

1. 4.1 Securing AI Systems Post-Deployment
2. 4.2 Model Integrity and Auditing
3. 4.3 Hands-On: Implementing RBAC

Module 5: Audit Readiness & Red-Teaming

1. 5.1 Preparing AI Systems for Audits
2. 5.2 Red-Teaming for AI Systems
3. 5.3 Hands-On: Red-Teaming Simulation

Module 6: Toolkits & Automation

1. 6.1 Introduction to AI Security Tools
2. 6.2 Automating AI Security and Compliance
3. 6.3 Hands-On: Tool Integration