All Episodes

Welcome to the ISACA AAIA Audio Course

Episode 112 — Exam-Day Tactics: Calm, fast, defensible answers for AAIA scenarios (Exam-Day Tactics)

Episode 111 — Spaced Retrieval Mega-Review: All 23 tasks in one connected storyline (Review: Tasks 1–23)

Episode 110 — Spaced Retrieval Review: Domain 3 audit tools and techniques, simplified (Review: Domain 3)

Episode 109 — Utilize AI to enhance audit reporting without hallucinated conclusions (Task 23)

Episode 108 — Utilize AI to enhance audit execution while preserving evidence quality (Task 23)

Episode 107 — Utilize AI to enhance audit planning without outsourcing judgment (Task 23)

Episode 106 — Prevent AI-in-audit blind spots: bias, leakage, and overreliance risks (Task 22)

Episode 105 — Evaluate impacts and risk when integrating AI into the audit process (Task 22)

Episode 104 — Follow up AI audits so fixes stick and risk stays reduced (Domain 3E)

Episode 103 — Write AI findings that tie cause, risk, evidence, and remediation together (Domain 3E)

Episode 102 — Deliver AI audit reports executives understand and teams can act on (Domain 3E)

Episode 101 — Use analytics to detect drift, anomalies, and control breakdown trends (Domain 3D)

Episode 100 — Audit data quality before trusting any AI output or model score (Domain 3D)

Episode 99 — Validate evidence integrity when models and data change over time (Domain 3C)

Episode 98 — Collect AI audit evidence: logs, lineage, artifacts, and change records (Domain 3C)

Episode 97 — Test AI controls with evidence, not opinions or vendor demos (Domain 3B)

Episode 96 — Design sampling for AI decisions that reveals bias and failure modes (Domain 3B)

Episode 95 — Use audit techniques tailored to AI systems, not generic checklists (Domain 3B)

Episode 94 — Choose audit criteria for AI using policy, risk, and outcomes (Domain 3A)

Episode 93 — Build AI audit objectives that connect directly to business risk (Domain 3A)

Episode 92 — Plan an AI audit: scope, criteria, stakeholders, and timing choices (Domain 3A)

Episode 91 — Spaced Retrieval Review: Domain 2 operations and controls, simplified (Review: Domain 2)

Episode 90 — Run AI incident response: detect, triage, contain, recover, and learn (Domain 2G)

Episode 89 — Evaluate AI problem and incident management programs for fast containment (Task 20)

Episode 88 — Audit AI vendor claims, contracts, and control evidence without getting sold (Task 10)

Episode 87 — Evaluate AI vendors and supply chain controls where your visibility ends (Task 10)

Episode 86 — Audit least privilege for pipelines, service accounts, and model endpoints (Task 16)

Episode 85 — Evaluate identity and access management for AI models, data, and keys (Task 16)

Episode 84 — Build threat monitoring that catches abuse of models and prompts early (Task 19)

Episode 83 — Evaluate AI threat and vulnerability management programs for real coverage (Task 19)

Episode 82 — Understand data poisoning, evasion, and model theft in plain language (Domain 2F)

Episode 81 — Evaluate AI threats and vulnerabilities that do not exist in normal IT (Domain 2F)

Episode 80 — Prove AI controls work over time, not only on launch day (Task 12)

Episode 79 — Evaluate the design and effectiveness of AI-specific controls (Task 12)

Episode 78 — Choose AI testing methods that match the risk of the use case (Domain 2E)

Episode 77 — Test AI solutions for accuracy, robustness, bias, and safety (Domain 2E)

Episode 76 — Validate supervision of AI impacts on fairness, safety, and quality (Domain 2D)

Episode 75 — Build human oversight triggers for AI decisions that need escalation (Domain 2D)

Episode 74 — Supervise AI outputs: detect harmful decisions before customers do (Domain 2D)

Episode 73 — Audit access to model artifacts, pipelines, and configuration repositories (Task 14)

Episode 72 — Prove reproducibility: model versions, parameters, and training snapshots (Task 14)

Episode 71 — Evaluate configuration management for AI across code, data, and models (Task 14)

Episode 70 — Audit emergency changes for AI when risk forces fast decisions (Task 13)

Episode 69 — Audit model update approvals, testing evidence, and release readiness (Task 13)

Episode 68 — Evaluate change management for AI where “updates” can change outcomes (Task 13)

Episode 67 — Evaluate model performance claims using audit-grade skepticism (Task 9)

Episode 66 — Evaluate model explainability expectations without overpromising certainty (Task 9)

Episode 65 — Test model alignment to policy: what it should do versus what it does (Task 9)

Episode 64 — Evaluate algorithms and models for alignment to business objectives (Task 9)

Episode 63 — Audit AI decommissioning: retirement criteria and data cleanup duties (Task 8)

Episode 62 — Audit AI monitoring controls: drift, performance, and incident triggers (Task 8)

Episode 61 — Audit AI deployment controls: approvals, gates, and rollback readiness (Task 8)

Episode 60 — Embed vendor AI security requirements before procurement begins (Task 9)

Episode 59 — Retest and document fixes so AI vulnerabilities stay closed (Task 7)

Episode 58 — Build AI vulnerability management from discovery to remediation (Task 7)

Episode 57 — Design AI security testing that matches your model, data, and use case (Task 7)

Episode 56 — Build a reassessment cadence that prevents stale AI risk decisions (Task 6)

Episode 55 — Monitor external changes like laws, vendors, and new AI capabilities (Task 6)

Episode 54 — Monitor internal changes that require AI risk reassessment (Task 6)

Episode 53 — Keep threat understanding current as attackers and tools evolve (Task 5)

Episode 52 — Assess AI threats by likelihood and impact, not hype and fear (Task 5)

Episode 51 — Identify the AI threat landscape using realistic abuse cases (Task 5)

Episode 50 — Assign AI risk owners and approvals so accountability is never unclear (Task 4)

Episode 49 — Connect AI risks to enterprise risk reporting and decision-making (Task 4)

Episode 48 — Run the AI risk management life cycle from intake to monitoring (Task 4)

Episode 47 — Domain 2 overview: manage AI risk while enabling business opportunity (Task 4)

Episode 46 — Domain 1 recap drill: pick the right task under pressure (Tasks 1–21)

Episode 45 — Plan for vendor outages and safe degraded modes in AI systems (Task 17)

Episode 44 — Set recovery goals for AI services, data pipelines, and vendors (Task 17)

Episode 43 — Add AI systems to business continuity plans without hidden weak points (Task 17)

Episode 42 — Eradicate root causes and recover safely after AI security incidents (Task 16)

Episode 41 — Notify and escalate during AI incidents with the right triggers (Task 16)

Episode 40 — Contain AI incidents quickly by limiting access and stopping risky flows (Task 16)

Episode 39 — Report AI security incidents on time without losing accuracy (Task 15)

Episode 38 — Document AI incidents clearly for regulators, contracts, and executive updates (Task 15)

Episode 37 — Investigate AI security incidents by collecting the right evidence fast (Task 15)

Episode 36 — Domain 1 quick review: governance, policies, assets, metrics, and training (Tasks 1–3)

Episode 35 — Operationalize tools with tuning, ownership, and measurable outcomes (Task 19)

Episode 34 — Implement AI security tools into monitoring, alerting, and response workflows (Task 19)

Episode 33 — Review AI security tools by coverage, gaps, and operational fit (Task 19)

Episode 32 — Use metrics to prioritize work and prove security program value (Task 18)

Episode 31 — Monitor AI metrics to spot misuse, drift, and early incident signals (Task 18)

Episode 30 — Define AI security metrics leaders can understand and act on (Task 18)

Episode 29 — Build an AI security program that fits the enterprise security program (Task 19)

Episode 28 — Manage retention and deletion to reduce long-term AI data exposure (Task 14)

Episode 27 — Preserve data integrity so models stay reliable and trustworthy (Task 14)

Episode 26 — Protect training and test data with access control and secure storage (Task 14)

Episode 25 — Identify data risks across the AI life cycle: leaks and tampering (Task 14)

Episode 24 — Keep the AI inventory accurate with routine governance checks (Task 13)

Episode 23 — Classify AI assets by sensitivity, criticality, and compliance scope (Task 13)

Episode 22 — Inventory AI assets: models, prompts, data, and key dependencies (Task 13)

Episode 21 — Refresh training when threats, tools, and regulations change (Task 21)

Episode 20 — Build AI security awareness training that sticks in daily work (Task 21)

Episode 19 — Create acceptable use guidelines that reduce risky AI behavior (Task 21)

Episode 18 — Essential Terms: Plain-Language Glossary for fast, accurate recall (Tasks 1–22)

Episode 17 — Keep AI security policies current using ownership and change control (Task 2)

Episode 16 — Turn policies into standards, guidelines, and step-by-step procedures (Task 2)

Episode 15 — Write AI security policies people can follow without guessing (Task 2)

Episode 14 — Prove conformity by building defensible evidence for regulators and contracts (Task 8)

Episode 13 — Perform AI impact assessments with scope, evidence, and actionable results (Task 8)

Episode 12 — Plan AI impact assessments early so compliance is not an afterthought (Task 8)

Episode 11 — Translate AI regulations into practical, testable security requirements (Task 3)

Episode 10 — Apply ethical principles when AI outcomes create real business risk (Task 3)

Episode 9 — Use industry frameworks to organize AI governance and security work (Task 3)

Episode 8 — Set governance routines that keep AI security decisions consistent (Task 1)

Episode 7 — Define AI roles and responsibilities so decisions are owned and clear (Task 1)

Episode 6 — Build an AI governance charter that aligns to business objectives (Task 1)

Episode 5 — Domain 1 overview: lead AI governance and program management confidently (Task 1)

Episode 4 — Exam Acronyms: High-Yield Audio Reference for AAISM daily practice (Tasks 1–22)

Episode 3 — Walk through an AI system life cycle in clear, simple language (Task 22)

Episode 2 — Understand how AAISM questions map to real AI security work (Tasks 1–22)

Episode 1 — Exam orientation and a spoken 30-day plan to pass AAISM (Tasks 1–22)