Security, Compliance, and Governance for AI
Description
Comprehensive understanding of security frameworks, compliance requirements, and governance structures for AI systems including data protection, access control, audit processes, and regulatory adherence.
Learning Objectives
Learners will master AI security best practices, compliance frameworks for AI systems, governance structures for AI initiatives, risk management strategies, data protection mechanisms, access control systems, audit processes, and regulatory requirements for AI deployment in enterprise environments.
Topics (9)
Detailed study of data encryption, anonymization techniques, privacy-preserving ML, and compliance with privacy regulations.
Comprehensive coverage of IAM systems, role-based access control, multi-factor authentication, and API security for AI services.
Study of regulatory requirements, compliance assessment, documentation requirements, and audit preparation for AI systems.
Comprehensive study of AI governance models, committee structures, policy development, and organizational frameworks for AI oversight.
Study of audit frameworks, compliance monitoring tools, assessment procedures, and reporting mechanisms for AI systems.
Comprehensive coverage of adversarial ML, model security techniques, threat detection, and protection mechanisms for AI models.
Study of incident response frameworks, recovery procedures, business continuity planning, and post-incident analysis for AI systems.
Comprehensive overview of AI security landscape, threat identification, vulnerability assessment, and security design principles for AI systems.
Detailed coverage of AI risk taxonomy, assessment techniques, mitigation planning, and continuous risk monitoring for AI systems.