By Alpha Indigo
1. Introduction
1.1. Welcome Course overview and learning objectives Introduction to responsible AI development The importance of governance, compliance, and security in AI systems
2. AI & Machine Learning Foundations for Technical Experts
2.1. AI Concepts Advanced machine learning algorithms and their implications Deep learning architectures and responsible implementation Foundation models and their governance challenges 2.2. AI System Lifecycle Development phases from ideation to deployment Key decision points impacting governance and compliance Documentation requirements throughout the lifecycle 2.3. AI Security Risks Common vulnerabilities in AI/ML systems Attack vectors specific to machine learning models Risk assessment methodologies for AI systems 2.4. AI System Architecture Design principles for secure AI architectures Infrastructure considerations for compliant AI systems Integration points and associated compliance challenges Knowledge Check: Assessment of foundational AI concepts and security awareness
3. AI Governance & Compliance for Engineers
3.1. Regulatory Frameworks Overview of AI regulations (EU AI Act, NIST AI RMF, etc.) Industry-specific compliance requirements Global landscape of AI governance requirements 3.2. Governance Best Practices Documentation standards for AI development Internal review processes and stakeholder engagement Technical approaches to governance implementation 3.3. Bias Auditing Tools Technical tools for bias detection in training data Methods for measuring algorithmic fairness Implementing bias monitoring in development pipelines 3.4. AI Governance Knowledge Assessment Evaluation of regulatory understanding Application of governance principles to case studies 3.5. Bias Mitigation Development Hands-on implementation of bias mitigation techniques Building fairness into model architecture Testing and validating bias reduction approaches
4. AI Risk Categorization, Due Diligence & Policy Implementation
4.1. AI Risk Classification Frameworks for categorizing AI system risks Impact assessment methodologies Technical criteria for risk evaluation 4.2. Conducting Due Diligence Technical review processes for AI components Vendor assessment for AI technologies Documentation requirements for due diligence 4.3. Implementing AI Policies Translating organizational policies into technical requirements Creating technical standards aligned with governance frameworks Automating policy compliance checks in development workflows 4.4. AI Risk and Governance Quiz Assessment of risk classification knowledge Evaluation of due diligence understanding
5. AI Policy, Incident Handling & Compliance Process
5.1. Corporate AI Policies Key components of effective AI policies Technical implementation of policy requirements Monitoring and enforcement mechanisms 5.2. Incident Response Framework AI-specific incident types and detection methods Technical response procedures for AI incidents Root cause analysis for AI system failures 5.3. Compliance Reporting Training Creating technical compliance documentation Automated compliance reporting tools and dashboards Evidence collection and preservation for audits 5.4. Incident Handling and Compliance Quiz Assessment of incident response knowledge Evaluation of compliance reporting understanding 5.5. Simulate AI Incident Response Practical exercise responding to AI system failures Implementing containment, eradication, and recovery procedures Post-incident analysis and reporting
6. AI Security & Privacy-By-Design
6.1. AI Security Threats Advanced persistent threats to AI systems Model extraction and poisoning attacks Adversarial examples and defenses 6.2. Security Best Practices Secure model development and deployment Access control and authentication for AI systems Protecting model integrity through technical safeguards 6.3. Privacy-Enhancing Techniques Differential privacy implementation in ML pipelines Federated learning for privacy preservation Advanced anonymization techniques for training data 6.4. AI Security Best Practices Quiz Assessment of security threat knowledge Evaluation of privacy-enhancing technique understanding
7. AI Model Lifecycle Management & Compliance-Driven MLOps
7.1. Model Lifecycle Compliance Compliance requirements across the model lifecycle Version control and reproducibility for compliance Audit trails and model provenance tracking 7.2. Post-Deployment Monitoring Continuous monitoring for drift and performance degradation Compliance violation detection in production Automated alerting and reporting systems 7.3. Responsible Scaling Technical considerations for scaling compliant AI systems Resource optimization while maintaining governance requirements Managing compliance across distributed AI deployments 7.4. MLOps Compliance Assessment Evaluation of MLOps knowledge Application of compliance principles to MLOps practices 7.5. Capstone Project Design and implementation of a compliance-driven MLOps pipeline Integration of governance, security, and privacy requirements Demonstration of responsible AI development practices
8. Final Certification & Assessment
8.1. Final Exam: AI Compliance & Security Case Study Comprehensive assessment of course knowledge Practical application of governance, compliance, and security principles Problem-solving in complex AI governance scenarios
9. Summary
9.1. Summary Review of key concepts and best practices Resources for continued learning and implementation Creating a roadmap for responsible AI development in your organization
