Certified: The ISACA AAISM Audio Course

This episode teaches the practical hierarchy from policy to standards to procedures, and how the AAISM exam expects you to translate high-level intent into repeatable actions that teams can execute and auditors can verify. You will learn how standards create measurable requirements, how guidelines provide flexible implementation options, and how procedures define who does what, when, and with what...

This episode explains how to create AI security policies that are clear, enforceable, and usable by real teams, which AAISM questions often probe through “what should policy include” and “why did policy fail” scenarios. You will learn how to define scope, roles, mandatory behaviors, and prohibited actions in plain language while still being specific enough to test. We use examples like data handli...

This episode focuses on evidence as the bridge between “we say we comply” and “we can prove we comply,” a distinction the AAISM exam tests repeatedly through documentation and auditability scenarios. You will learn to design evidence trails that link requirements to controls, controls to tests, and tests to outcomes, with clear ownership and version history. We cover examples such as approval reco...

This episode teaches how to execute an AI impact assessment so it produces decisions, controls, and evidence that stand up to audit rather than a vague narrative report. You will learn how to set scope boundaries, identify stakeholders, select evaluation criteria, and gather evidence across data sources, model behavior, deployment pathways, and user interaction patterns. We walk through what “acti...

This episode explains why AI impact assessments must be planned early in the life cycle and how AAISM scenarios test your ability to embed assessment timing into governance and delivery workflows. You will define an impact assessment as a structured evaluation of likely harms, affected stakeholders, and control needs, then learn how to trigger it based on use case sensitivity, data types, deployme...

This episode shows how to convert regulatory and legal expectations for AI into requirements you can test, monitor, and enforce, which is exactly how AAISM questions frame compliance: not as memorization, but as operational control design. You will learn to separate broad principles from concrete obligations, then express those obligations as “shall” statements tied to scope, owners, evidence, and...

This episode teaches how ethical principles become practical security requirements when AI decisions can cause harm, legal exposure, or reputational damage, which is a recurring theme in AAISM scenarios. You will define ethical risk in operational terms, such as unfair outcomes, unsafe recommendations, privacy violations, and deceptive behavior, and learn how to turn those concerns into controls l...

This episode explains how to use industry frameworks as organizing structures for AI governance and security requirements, with an exam focus on mapping principles into testable controls and evidence. You will learn the difference between adopting a framework as guidance versus treating it as a compliance checklist, and how to select scope-appropriate controls for your model, data, and deployment...

This episode focuses on governance routines as repeatable control mechanisms: meeting cadences, intake reviews, approval gates, metrics reviews, and exception handling that keep AI security decisions consistent across teams and time. You will learn what “good” looks like for agendas, minutes, decision logs, and follow-ups so evidence is defensible for internal audit, regulators, and contracts. We...

This episode teaches how the AAISM exam expects you to assign AI security responsibilities across business, security, engineering, data, and risk functions so that approvals and accountability cannot be disputed after an incident. You will learn how to distinguish roles that build and operate systems from roles that set policy, accept risk, and verify control performance, and how to document those...

This episode breaks down what makes an AI governance charter exam-ready: clear purpose, scope boundaries, authority, membership, and decision mechanisms that connect directly to business goals and risk tolerance. You will learn how to write charter language that is testable, including how to define which AI systems are in scope, what decisions require approval, and how exceptions are handled witho...

This episode introduces Domain 1 as the exam’s foundation for proving that AI security work is owned, repeatable, and aligned to business objectives rather than ad hoc technical fixes. You will define governance in practical terms, including decision rights, escalation paths, and the minimum artifacts that make accountability auditable. We explain how program management shows up on the exam throug...

This episode builds fast recognition of the acronyms and shorthand you will see in AAISM-style scenarios, focusing on what each term implies for governance, risk, and control decisions rather than memorizing expansions alone. You will learn to tie common terms to expected evidence, such as how an “assessment” implies scope, criteria, stakeholders, and documentation, while “monitoring” implies tele...

This episode teaches the AI system life cycle the way the AAISM exam expects you to reason about it: as a chain of decisions, artifacts, and controls from idea intake through retirement. You will define key phases such as data acquisition, training, evaluation, deployment, monitoring, and decommissioning, then link each phase to the security questions an auditor or security lead must ask. We use p...

This episode connects typical AAISM question patterns to real AI security responsibilities, so you can recognize what the exam is truly asking you to do: govern, assess risk, or implement and operate controls. You will practice translating a scenario into a task statement, identifying the decision-maker, the evidence needed, and the control intent, which is the quickest way to choose the defensibl...

This episode establishes how the AAISM exam is organized around tasks, what “best answer” logic looks like, and how to build a realistic 30-day audio-first study plan that maps to every tested objective without wasting time on low-yield detail. You will learn how to schedule daily domain rotation, when to switch from understanding to recall, and how to self-check comprehension using short verbal p...