AI Risk Management for Security Managers

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Overview

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This course provides comprehensive preparation for AI Risk Management for Security Managers. It covers the ISACA Advanced in AI Security Management (AAISM) certification objectives and equips learners with the governance, risk, and technical control skills demanded by enterprise AI security programs. Spanning 10 modules and approximately 11 hours of expert video instruction from Dr. Michael Solomon, the course covers all three AAISM exam domains: AI Governance and Program Management, AI Risk and Opportunity Management, and AI Technologies and Controls. Learners benefit by gaining practical skills directly applicable to roles such as AI security manager, IT risk officer, internal auditor, and compliance manager. The course covers AI governance frameworks, risk appetite and treatment, AI asset and data lifecycle management, incident response and business continuity, penetration testing and red-teaming, vendor and supply-chain risk, AI ethics and IP accountability, and the privacy, ethics, and monitoring controls that keep production AI systems secure. By the end of this course, learners will be able to evaluate, govern, and audit AI systems from a risk and control perspective.

Syllabus

About AI Governance Foundations and the AAISM Exam
- Learners are introduced to the AI governance, the ISACA AAISM certification and the career value of the credential. It builds the foundation of AI governance: governance models (centralized, federated, hybrid), board-level accountability, steering committees and charters, stakeholder identification, risk appetite and tolerance, and framework selection across the EU AI Act, ISO 42001, OECD, and NIST AI RMF. By the end of this module, learners can describe how an AI governance program is structured and staffed.
AI Risk Appetite, Frameworks & Strategic Alignment
- Learners complete the governance-framework arc, examining how organizations select and apply the EU AI Act, ISO 42001, OECD principles, and the NIST AI RMF, how AI business use cases are governed through intake and privacy review workflows, and the strategic differences between consumer and enterprise AI adoption, including shadow AI and data residency risk. By the end of this module, learners can evaluate and recommend a governance framework for a given organizational context.
AI Risk Management and Data Security
- Learners work through the policy and procedural backbone of an AI program: buy-vs-build decision governance, AI policy and responsible-use development, procedures and manuals, AI asset inventories, model cards and documentation standards, data classification and discovery, data augmentation controls, and secure data storage. By the end of this module, learners can build the documentation and data-security controls an AI program depends on.
AI Lifecycle Security and Program Management
- Learners build the AI security program itself: data destruction and retention, developing an AI security program plan, aligning AI security with enterprise InfoSec, structuring an AI security team, AI-enabled security tools, security metrics/KPIs/KRIs, executive management reporting, and AI incident detection and notification. By the end of this module, learners can stand up and report on an AI security program end to end.
Incident Classification & AI Resiliency Planning
- Learners learn to classify AI incidents by severity, design AI-specific business continuity plans, establish red-button and break-glass emergency controls, and apply RTO/RPO thinking to AI disaster recovery. By the end of this module, learners can build a resiliency plan that accounts for AI-specific failure modes.
AI Risk Assessment & Adversarial Testing
- Learners move into hands-on risk practice: AI risk assessment and impact analysis, risk documentation and treatment decisions, penetration testing methodology, and red-teaming and adversarial-threat techniques used against real AI systems. By the end of this module, learners can plan and interpret an adversarial test against an AI system.
Threat Intelligence & Adversarial AI
- Learners study AI-specific attack chains through a threat-intelligence lens and learn to recognize deepfakes, insider threats, and the emerging risks posed by autonomous AI agents. By the end of this module, learners can map an AI attack chain and identify the threat actor techniques behind it.
Vendor Risk & Third-Party Accountability
- Learners cover vendor due diligence and contracts, provider-versus-deployer accountability, third-, fourth-, and fifth-party supply-chain risk, IP ownership and liability, and ongoing vendor monitoring. By the end of this module, learners can run a full AI vendor risk review from intake through ongoing monitoring.
AI Security Architecture, Testing & Data Management
- Learners work through AI security architecture and change management, model testing, regression, and TEVV, and the data management practices that guard against data poisoning. By the end of this module, learners can evaluate an AI system's architecture and testing regime for security gaps.
Privacy, Ethics & Continuous Monitoring
- Learners close out the course by applying privacy, ethical, and trust controls, learning control selection and lifecycle management, and building the continuous security-monitoring practices that keep AI systems secure after deployment. By the end of this module, learners can design a monitoring program that keeps a production AI system accountable over time.
Course Assessment