AAISM - Complete Course | Govern AI Security and Risk

This lecture introduces the purpose of the AI Foundations module and why mastering key terminology is essential for AI governance, risk, and compliance. It emphasizes the importance of a shared vocabulary for consistent understanding across industries and frameworks.

Learn the fundamental concepts of AI, machine learning, algorithms, and models. This lecture also explains the difference between narrow AI and AGI and why these distinctions matter for governance.

Explore the principles of AI governance, including accountability, transparency, and oversight. Understand how these concepts build trust and ensure compliance in AI systems.

This lecture covers key technical attributes that define trustworthy AI systems. You’ll learn how accuracy, reliability, and robustness impact risk and confidence in AI outcomes.

Discover the main AI learning methods: supervised, unsupervised, semi-supervised, reinforcement learning, and RLHF. Understand their differences and governance implications.

Learn about advanced techniques like transfer learning, fine-tuning, and active learning. This lecture also introduces foundation models and large language models, highlighting their benefits and risks.

Understand common AI vulnerabilities such as adversarial attacks, data poisoning, and hallucinations. Learn how these weaknesses can be exploited and strategies to mitigate them.

Explore different types of bias—computational, cognitive, and societal—and their impact on fairness. This lecture also covers real-world consequences and mitigation strategies.

Learn how assurance frameworks, audits, and impact assessments promote trustworthy AI. This lecture explains their role in compliance and risk management.

Understand the difference between explainability and interpretability in AI systems. Learn about human-in-the-loop oversight and principles of human-centric AI.

This lecture explains why data quality and provenance are critical for AI reliability. It also covers risks like data drift and leakage across the data lifecycle.

Learn about different dataset types and their roles in AI development. Understand how synthetic data can be used responsibly and the risks of improper data handling.

A summary of all key terms and concepts introduced in the AI Foundations module. This lecture reinforces terminology for use across frameworks like AAISM, ISO/IEC 42001, and NIST AI RMF.

An overview of Domain 1, which focuses on aligning AI initiatives with organizational objectives through governance, policy, and security programs. This lecture explains the domain’s weight in the AAISM exam and its link to enterprise strategy.

Learn how to identify internal and external stakeholders and define their roles in AI governance. This lecture emphasizes strategies for effective engagement and communication to ensure accountability and oversight.

This lecture introduces leading AI governance frameworks such as NIST AI RMF, ISO/IEC 42001, and OECD Principles. Learners will explore how these frameworks are applied in practice and compare risk-based versus compliance-based approaches.

Understand the major AI-related regulations, including the EU AI Act, GDPR, US AI Executive Order, and Saudi NCA ECC. This lecture explains their implications for enterprises and the risks of non-compliance.

Learn how to create AI strategies that align with business objectives and governance principles. This lecture also covers assessing AI maturity and bridging gaps in strategic planning.

This lecture explains how to develop enforceable AI policies and operational procedures. Learners will understand the role of acceptable use guidelines and how to evaluate policy effectiveness.

Discover methods for identifying and classifying AI assets such as datasets, models, and pipelines. This lecture also covers inventory management and safeguards to protect AI assets throughout their lifecycle.

Learn how to secure AI data across its entire lifecycle, from acquisition to disposal. This lecture emphasizes controls for accuracy, integrity, fairness, and risk mitigation against threats like data poisoning.

This lecture explains how to design AI security programs that integrate with the enterprise ISMS. Learners will understand how to ensure resilience, assign roles, and assess maturity levels for AI security initiatives.

Learn how to define KPIs, KRIs, and other metrics to measure the effectiveness of AI security programs. This lecture also covers monitoring techniques and how to communicate results to stakeholders for continuous improvement.

This lecture explores how to incorporate AI into business continuity and disaster recovery planning. Learners will simulate AI-related disruption scenarios and evaluate strategies for maintaining resilience.

Understand the incident response lifecycle for AI-specific threats such as adversarial attacks, data leakage, and unsafe outputs. This lecture also covers reporting requirements and lessons-learned analysis for future improvements.

A comprehensive review of Domain 1 topics, reinforcing governance, policy, and continuity concepts. Learners will apply knowledge through case-based scenarios and prepare for the transition to Domain 2.

This lecture introduces Domain 2, focusing on AI-specific risk management and its integration into enterprise frameworks. Learners will understand why AI risks differ from traditional IT risks and how to address them effectively.

Learn the principles and frameworks of AI risk management and how they differ from enterprise risk management. This lecture emphasizes continuous risk monitoring and governance models for AI systems.

This lecture provides practical methods for assessing AI risks across models, datasets, and outputs. Learners will explore structured methodologies and how to document findings for compliance and oversight.

Understand how organizations define acceptable AI risk levels and align them with strategic goals. This lecture also covers scenarios where thresholds are exceeded and governance adjustments are required.

Explore the four main AI risk treatment strategies: avoidance, mitigation, transfer, and acceptance. Learners will apply these strategies to real-world scenarios and integrate them into enterprise risk management.

This lecture introduces the evolving AI threat landscape, including adversarial attacks, model poisoning, data leakage, and prompt injection. Learners will analyze real-world examples and understand how these threats impact AI systems.

Learn how to identify, test, and remediate vulnerabilities in AI systems and data pipelines. This lecture explains how traditional vulnerability management practices adapt to AI environments through techniques like red teaming and penetration testing.

This lecture explains the role of AI impact assessments in evaluating compliance, ethics, and enterprise risks. Learners will explore conformity assessment practices and how to report findings to stakeholders and regulators.

Understand the risks of adopting AI solutions from third-party vendors and how to manage them effectively. This lecture covers drafting security requirements into contracts, monitoring compliance, and applying vendor management frameworks.

This lecture examines supply chain risks in AI systems, including datasets, APIs, and model provenance. Learners will learn to apply controls that secure AI supply chains and monitor third-party providers for compliance.

A review of Domain 2 concepts, including risk assessments, thresholds, treatment strategies, vendor oversight, and supply chain security. Learners will consolidate knowledge through applied examples and prepare for Domain

This lecture introduces Domain 3, the most heavily weighted section of the AAISM exam, focusing on technical controls and security architectures. Learners will understand how to embed security throughout the AI lifecycle to ensure privacy, ethics, and resilience.

This lecture introduces the principles of secure AI architecture and how design choices influence risk mitigation. Learners will compare centralized and distributed architectures and identify considerations for secure AI integration.

Explore secure design principles such as defense-in-depth, zero trust, and secure-by-design for AI environments. Learners will apply these strategies to AI pipelines and real-world system designs.

This lecture explains how AI systems fit into broader enterprise IT and IS architectures. Learners will understand integration challenges and how to align AI with governance, compliance, and organizational strategies.

Learn how to translate governance strategies into technical implementation policies for AI systems. This lecture covers aligning technical measures with global regulations and applying risk management during deployment.

This lecture examines risks to AI training and inference data, including poisoning and leakage. Learners will explore strategies to ensure data integrity, confidentiality, and availability throughout the AI lifecycle.

Understand the importance of transparent and auditable AI systems. This lecture covers implementing traceability controls, ensuring data lineage, and applying explainability frameworks for compliance.

This lecture explores privacy risks in AI adoption and compliance with regulations like GDPR and CPRA. Learners will apply privacy-by-design principles to AI development and evaluate compliance risks.

Learn the ethical principles of fairness, accountability, and transparency in AI governance. This lecture includes frameworks for trustworthy AI and real-world case studies of ethical challenges.

This lecture focuses on designing safety controls to prevent harmful or unsafe AI outputs. Learners will study human-in-the-loop oversight and trust frameworks to ensure responsible AI behavior.

This lecture explains how organizations can monitor AI systems for bias and ensure robustness under various conditions. Learners will explore fairness testing, bias mitigation techniques, and stress testing for adversarial resilience.

Learn about AI-specific security controls, including authentication, authorization, encryption, and monitoring. This lecture demonstrates how to integrate these controls into enterprise ISMS frameworks for comprehensive protection.

This lecture covers continuous monitoring practices for AI systems, including integration with SOC and SIEM tools. Learners will define monitoring metrics and evaluate tools that support real-time oversight of AI environments.

Understand how to measure and report the effectiveness of AI security programs. This lecture focuses on defining KPIs and KRIs, conducting regular testing, and communicating results to executives and regulators.

A comprehensive review of Domain 3, consolidating knowledge of architecture, strategies, data controls, privacy, ethics, and monitoring. Learners will apply these concepts to real-world case studies and prepare for the final course recap.

This lecture ties together all three domains, showing how governance, risk, and controls work together to create trustworthy AI. Learners will review integrated concepts and prepare for exam readiness.

Explore emerging trends in AI governance, regulation, and security threats such as adversarial AI and deepfakes. This lecture also discusses the evolving role of governance in innovation and future career opportunities.

The closing lecture thanks learners for completing the course and provides guidance on next steps in their professional journey. It highlights continuing education opportunities and applying course knowledge in real-world environments.