Certified: The ISACA AAIA Audio Course

This episode focuses on AI decommissioning, because Task 8 scenarios sometimes test whether you can manage the end of the lifecycle with the same discipline as development and deployment. You’ll learn how to define retirement criteria, such as models that no longer meet requirements, models that create unacceptable harm, systems that cannot be supported operationally, or use cases that no longer h...

This episode teaches you how to audit AI monitoring controls for drift, performance, and incident triggers, because Task 8 expects monitoring to be designed and proven, not improvised after problems surface. You’ll learn how to define what must be monitored based on decision impact, including performance trends, stability of input data, fairness and segment outcomes where relevant, and operational...

This episode focuses on deployment controls for AI, because Task 8 scenarios often test whether you treat deployment as a controlled release with approvals, gates, and rollback readiness rather than a simple “go live.” You’ll learn how approval gates should confirm that requirements were met, validation evidence is complete, privacy and security constraints are enforced, and operational owners are...

This episode introduces Task 9 by showing how to embed vendor AI security requirements before procurement begins, because AAISM expects you to shape vendor risk outcomes early through clear requirements, evidence expectations, and contractual controls rather than trying to “fix” weak vendor posture after adoption. You’ll define what vendor requirements should cover for AI services: data handling a...

This episode completes the Task 7 vulnerability thread by teaching how to retest and document fixes so vulnerabilities stay closed, because AAISM often tests whether you can prove remediation with evidence and prevent reintroduction through drift, model updates, or configuration changes. You’ll learn how to design retesting that actually validates risk reduction, such as confirming access permissi...

This episode focuses on Task 7 by explaining how to build AI vulnerability management from discovery to remediation, because AAISM treats vulnerability management as an end-to-end control process that includes identification, prioritization, ownership, fixes, and evidence—not just scanning and ticket creation. You’ll learn how “vulnerabilities” show up in AI environments, including misconfigured a...

This episode introduces Task 7 by teaching how to design AI security testing that matches your model, data, and use case, because AAISM expects you to test what can realistically fail in your specific deployment instead of applying generic security tests that miss AI-specific failure modes. You’ll define what “AI security testing” means here: validating access controls and data protections, probin...

This episode covers Task 6 by teaching you to build a reassessment cadence that prevents stale AI risk decisions, because AAISM often rewards answers that institutionalize review routines rather than relying on ad hoc “we’ll revisit later” promises. You’ll define how cadence works in practice: scheduled reviews for high-impact systems, event-driven reviews for major changes, and lightweight check-...

This episode focuses on Task 6 by showing how external changes should drive AI risk reassessment, because AAISM expects you to manage risk in a live environment where laws, vendor terms, threat activity, and AI capabilities can shift without your internal teams making any code change. You’ll learn how to track external triggers such as new regulatory requirements, updated contract language, vendor...

This episode teaches Task 6 by explaining how to monitor internal changes that should trigger AI risk reassessment, because AAISM commonly tests whether you can recognize when a prior approval is no longer valid due to scope, data, or control changes. You’ll define internal change triggers such as expanding the user population, adding new data sources, enabling plugins or connectors, changing prom...

This episode addresses Task 5 by showing how to keep threat understanding current as attackers and tools evolve, because AI systems and their integrations change quickly and AAISM expects you to maintain an updated threat view that feeds governance, monitoring, and reassessment decisions. You’ll learn what “current” means operationally: regularly reviewing new abuse patterns, vendor capability cha...

This episode focuses on Task 5 by teaching you to assess AI threats using likelihood and impact rather than hype, because AAISM questions often include distractors that overreact to novel terminology while ignoring practical risk drivers. You’ll learn how to evaluate whether a threat is credible in your environment by examining access paths, data sensitivity, control strength, user behavior, vendo...

This episode covers Task 5 by building a practical view of the AI threat landscape using realistic abuse cases, because AAISM expects you to recognize how AI systems can be attacked or misused without relying on vague “AI is risky” statements. You’ll define what a threat landscape means in exam terms: the set of credible threat actors, their objectives, and the tactics that can affect AI confident...

This episode completes the set by teaching Task 4’s accountability core: assigning AI risk owners and approvals so responsibility is explicit, decisions are traceable, and risk acceptance is intentional rather than accidental. You’ll define what it means to “own” AI risk, including being accountable for controls, monitoring outcomes, exception handling, and lifecycle changes that alter exposure, a...

This episode focuses on Task 4 by showing how to connect AI risks to enterprise risk reporting and decision-making, because AAISM expects AI risk to be expressed in the same language leaders already use for prioritization, funding, and acceptance decisions. You’ll learn how to translate AI-specific concerns—like prompt injection, model drift, unsafe automation, and vendor dependency—into risk stat...

This episode teaches Task 4 by walking through the AI risk management life cycle from intake to monitoring, because AAISM questions often test whether you can apply risk management as a continuous loop rather than a single assessment document. You’ll define the lifecycle stages as intake and scope definition, risk identification, analysis and prioritization, treatment selection, control implementa...

This episode introduces Domain 2 through Task 4 by explaining how AAISM expects you to manage AI risk while enabling business opportunity, which means balancing innovation with disciplined risk management that leadership can defend. You’ll define AI risk in practical terms—uncertainty that impacts confidentiality, integrity, availability, safety, compliance, and reputation—then connect that to a r...

This episode is a Domain 1 recap drill designed to improve speed and accuracy under pressure by training you to identify which task is being tested and what the most defensible next step looks like across Tasks 1–21. You’ll rehearse fast classification of scenarios into governance and program management, policy and procedure operationalization, framework and ethics alignment, impact assessment dis...

This episode covers Task 17 by teaching how to plan for vendor outages and safe degraded modes, because many AI deployments depend on external model services, and AAISM scenarios often hinge on whether you can keep operations safe when a vendor fails or changes behavior unexpectedly. You’ll define “safe degraded mode” as an intentionally designed fallback that reduces functionality while preservin...

This episode focuses on Task 17 by showing how to set recovery goals for AI services, data pipelines, and vendors, because AAISM expects you to define recovery in measurable terms that match business impact and risk tolerance instead of using vague “restore ASAP” language. You’ll learn how to express recovery goals through service priorities, maximum tolerable downtime, data freshness expectations...

This episode addresses Task 17 by teaching how to add AI systems to business continuity plans without hidden weak points, because AAISM tests whether you can treat AI services as real dependencies with failure modes, not optional features that can be ignored during outages. You’ll define what business continuity means for AI-enabled processes by identifying which business functions rely on inferen...

This episode focuses on Task 16 by explaining how to eradicate root causes and recover safely after an AI security incident, because AAISM expects you to move beyond containment into durable fixes that prevent recurrence while maintaining evidence and governance discipline. You’ll learn how to distinguish symptom fixes, like disabling a feature, from root-cause eradication actions, like correcting...

This episode covers Task 16 by teaching how to notify and escalate during AI incidents using the right triggers, because AAISM often tests whether you can recognize when an AI issue crosses the threshold from “operational anomaly” to “security incident” that requires formal governance, legal, privacy, or executive involvement. You’ll define escalation triggers such as confirmed or suspected sensit...

This episode introduces Task 16 by focusing on rapid containment actions for AI incidents, because AAISM questions often test whether you can stop harm first by limiting access and risky data flows while preserving evidence and keeping governance decision rights intact. You’ll define containment for AI contexts, including disabling compromised accounts, revoking or narrowing plugin and connector p...

This episode teaches how to report AI security incidents on time while maintaining accuracy, aligning with Task 15 and reflecting how AAISM balances speed, governance, and evidence when deadlines are driven by regulation, contracts, or internal escalation policies. You’ll learn how to manage reporting with incomplete information by clearly separating confirmed facts from open questions, defining w...