Certified: The IAPP AIGP Audio Course
Certified: The IAPP AIGP Audio Course is designed for professionals seeking a practical path into AI governance without interrupting their careers. It covers foundations of AI governance, risk management, accountability, and decision-making for real organizations. The course is suitable for privacy professionals, compliance teams, risk managers, security leaders, legal and policy staff, product managers, and consultants. It provides a clear learning path for understanding responsible AI programs and preparing for the AIGP certification.
Episodes
Episode 1 — Decode the AIGP Exam Blueprint, Question Styles, Policies, and Spoken Study Plan
This episode introduces the structure of the AIGP exam so you can study with intention instead of collecting disconnected facts. You will learn how exam domains signal what the certifying body expects you to know, how objective language can hint at the depth of understanding being tested, and why terms such as identify, evaluate, compare, and apply often point to different question styles
Episode 2 — Grasp AI Definitions, Types, and Core Use Cases That Matter
This episode builds the vocabulary needed to understand later governance topics by separating broad AI concepts from narrower technical categories that often appear on the exam. You will review what artificial intelligence generally means in practice, how machine learning differs from rules-based automation, and why generative systems, predictive systems, recommendation systems, classific
Episode 3 — Understand AI Risks, Harms, and Why Governance Cannot Be Optional
This episode explains why AI governance exists by focusing on the gap between technical performance and real-world harm. You will learn the difference between risks to the organization and harms to people, groups, markets, or institutions, and why both matter on the exam and in practice. The discussion covers familiar problems such as bias, privacy intrusion, security weakness, opacity, o
Episode 4 — Apply Responsible AI Principles Across Fairness, Safety, Privacy, Transparency, and Accountability
This episode turns high-level responsible AI principles into practical decision lenses you can use on the exam. You will examine fairness as more than equal treatment, safety as more than cybersecurity, privacy as more than notice language, transparency as more than publishing a policy, and accountability as more than naming an owner. The goal is to understand how these principles interac
Episode 5 — Define AI Governance Roles and Clarify Who Owns Which Decisions
This episode focuses on one of the most common governance failures in both exam scenarios and real organizations: unclear ownership. You will learn how AI governance depends on defined roles for business leaders, legal teams, privacy professionals, security teams, data stewards, model developers, product owners, procurement staff, audit functions, and senior decision-makers. The key point
Episode 6 — Build Cross-Functional AI Governance Collaboration That Actually Works Across the Organization
This episode explains how effective AI governance depends on collaboration between groups that often speak different professional languages and pursue different goals. You will explore how legal, compliance, privacy, security, data science, engineering, procurement, HR, and business units must coordinate without creating endless approval loops that slow useful work. The exam may test this
Episode 7 — Create AI Terminology, Strategy, and Governance Training for Every Stakeholder
This episode shows why AI training must be tailored to role and responsibility rather than delivered as a generic awareness session to everyone. You will learn how frontline users, executives, developers, procurement teams, privacy staff, security professionals, and governance committees need different levels of depth, different examples, and different action triggers. The exam may frame
Episode 8 — Tailor AI Governance to Company Size, Maturity, Industry, and Risk Tolerance
This episode teaches an important exam concept: governance should be proportionate to context. You will examine why a small company testing a narrow internal AI tool does not need the same structure as a global enterprise deploying high-impact systems across regulated markets, even though both still need accountability, controls, and oversight. The episode breaks down how company size aff
Episode 9 — Differentiate Developers, Providers, Deployers, and Users in the AI Governance Model
This episode clarifies role categories that matter because legal duties and operational responsibilities often depend on where an organization sits in the AI value chain. You will learn how developers build or significantly shape systems, providers place systems into the market or make them available under their name, deployers use those systems in their own operations, and users interact
Episode 10 — Establish Life Cycle Policies That Drive Oversight and Accountability End to End
This episode introduces lifecycle governance as the discipline of controlling AI from idea through retirement instead of reacting only at deployment. You will review why policies must cover intake, use-case approval, design, data selection, testing, validation, release, monitoring, incident handling, change management, and decommissioning if an organization wants end-to-end accountability
Episode 11 — Update Privacy, Security, Data Governance, and IP Policies for AI
This episode explains why existing enterprise policies often need revision before an organization can govern AI responsibly. You will learn how privacy policies must address new data uses, how security policies must account for model abuse, prompt injection, data leakage, and access control, how data governance policies must define quality, retention, lineage, and approved sources, and ho
Episode 12 — Manage Third-Party AI Risk Through Assessments, Contracts, Procurement, and Acceptable Use
This episode focuses on third-party AI risk, which becomes critical when organizations buy, license, or embed tools they did not build themselves. You will examine how procurement reviews, vendor assessments, contract terms, and acceptable use rules help control risks involving data handling, model transparency, security testing, retraining practices, subprocessors, and responsibility for
Episode 13 — Navigate Transparency, Choice, Lawful Basis, and Purpose Limits in AI
This episode addresses core privacy and governance concepts that often become more complicated when AI systems process large volumes of data or make consequential inferences. You will review what transparency means in practice, when individuals may need meaningful notice, how user choice can apply depending on context, why lawful basis matters for certain data processing regimes, and how
Episode 14 — Embed Data Minimization and Privacy by Design into AI Systems
This episode explains how privacy by design becomes operational when teams make deliberate choices about what data an AI system truly needs, when it needs it, and how long it should be kept. You will learn why data minimization is not just a legal slogan but a practical way to reduce exposure, improve governance, and narrow the blast radius when something goes wrong. The episode examines
Episode 15 — Master Controller Obligations for AI Impact Assessments, Rights, Transfers, and Records
This episode examines the obligations that often fall on controllers or comparable responsible entities when AI systems process personal data. You will review why impact assessments matter for higher-risk processing, how individual rights can be affected by automated systems, what cross-border transfers may require in regulated environments, and why recordkeeping is central to proving acc
Episode 16 — Protect Sensitive and Special Category Data When AI Uses Biometrics
This episode focuses on one of the most sensitive areas in AI governance: the use of biometric data and other sensitive or special category data in systems that identify, infer, classify, or monitor people. You will explore why these data types demand heightened controls, including stronger purpose definition, restricted access, clear legal justification where required, careful retention
Episode 17 — Understand How Intellectual Property Law Shapes AI Training and Use
This episode explains how intellectual property concerns affect AI long before a tool reaches production. You will learn why training data rights matter, how copyrighted or proprietary material can raise licensing and infringement questions, and why generated outputs may create separate concerns involving ownership, attribution, trade secrets, and unauthorized reuse. For the AIGP exam, th
Episode 18 — Apply Nondiscrimination Law to AI in Employment, Credit, Housing, and Insurance
This episode connects AI governance to nondiscrimination obligations in some of the highest-stakes domains organizations face. You will examine how AI systems used in employment, credit, housing, and insurance can create legal and ethical exposure when they rely on biased data, flawed proxies, unequal error rates, or decision processes that disadvantage protected groups. The AIGP exam may
Episode 19 — Interpret Consumer Protection and Product Liability Risks in AI Systems
This episode explains how AI can create consumer protection and product liability risk even when a system is marketed as helpful, innovative, or low friction. You will learn why misleading claims about accuracy, safety, neutrality, or suitability can become governance problems, and how harm may arise when users reasonably rely on outputs that are incomplete, wrong, or poorly explained. Th
Episode 20 — Map AI Risk Classifications from Prohibited Uses to Minimal Risk
This episode introduces risk classification as a way to organize governance effort according to the seriousness of potential harm and the nature of the use case. You will review the basic idea behind categories that range from prohibited uses through high-risk and limited-risk uses down to minimal-risk activity, while also learning that labels only help when they are tied to real obligati
Episode 21 — Operationalize AI Law Requirements for Risk Management, Documentation, and Record Keeping
This episode explains how legal requirements become real controls only when an organization turns them into repeatable operational practices. You will learn how risk management requirements connect to intake reviews, impact assessments, testing thresholds, issue escalation, and approval decisions, while documentation and record keeping requirements support traceability, accountability, an
Episode 22 — Govern Human Oversight, Transparency, Notification, and Quality Management Requirements
This episode focuses on governance requirements that exist to keep AI systems understandable, reviewable, and controllable in real use. You will examine what meaningful human oversight looks like, when transparency must extend beyond internal teams to affected individuals or customers, why notification requirements matter when people interact with or are evaluated by AI, and how quality m
Episode 23 — Understand the Distinct Requirements That Apply to General-Purpose AI Models
This episode explains why general-purpose AI models can create governance challenges that differ from narrow, single-use systems. You will learn how models designed for many downstream uses can raise broader concerns involving transparency, documentation, capability limits, downstream integration, misuse risk, and the difficulty of predicting every context in which the model may be deploy
Episode 24 — Compare Enforcement, Penalties, and Duties for Providers, Deployers, Importers, and Distributors
This episode examines how governance obligations differ across entities that create, introduce, distribute, or use AI systems, and why those differences matter when legal accountability is assigned. You will review how providers often carry duties tied to design, documentation, and conformity, while deployers must govern implementation, context of use, monitoring, and user impacts. Import
Episode 25 — Apply OECD Trustworthy AI Principles, Frameworks, Policies, and Recommended Practices
This episode introduces the practical value of broad AI principles and recommended practices by showing how they guide governance choices even when they are not written as strict technical rules. You will review common themes such as human-centered design, fairness, robustness, transparency, accountability, and responsible stewardship, then connect those themes to policy development, role
Episode 26 — Use the NIST AI RMF and Playbook to Structure Governance
This episode explains how the NIST AI Risk Management Framework and its supporting playbook can help organizations turn broad governance goals into a structured operating model. You will learn how the framework supports governance, mapping, measurement, and management activities, and why that matters for identifying risks early, assigning responsibility, documenting decisions, and improvi
Episode 27 — Understand ISO 22989, ISO 42001, and ISO 42005 in AI Governance
This episode introduces three ISO standards that matter because they help organizations describe AI consistently, build management systems, and guide governance practices in a more formal and auditable way. You will learn that standards can serve different purposes, with some focused on shared terminology and concepts, some focused on management system requirements, and others focused on
Episode 28 — Review the Governance Foundations and Legal Duties Most Likely to Matter
This episode pulls together the major governance foundations and legal duties that repeatedly appear across AI oversight programs and exam scenarios. You will review why accountability, documented risk assessment, role clarity, lawful data use, transparency, security, human oversight, testing, monitoring, and incident response keep showing up regardless of industry or tool type. The AIGP
Episode 29 — Define Business Context and Use Cases Before Building Any AI System
This episode explains why good AI governance begins before model selection, procurement, or experimentation by forcing clarity about the business context and intended use case. You will learn how a well-defined use case identifies the problem to be solved, the users involved, the decision being supported or automated, the data needed, the stakeholders affected, and the consequences of err
Episode 30 — Perform Impact Assessments Early to Shape Safer AI Design Decisions
This episode focuses on impact assessments as early governance tools that shape design choices before risk becomes harder and more expensive to control. You will examine how an effective assessment looks beyond technical ambition and asks who may be affected, what harms could occur, what data is involved, how the system will be used, what safeguards are needed, and whether the use case sh
Episode 31 — Design AI Systems with Clear Purpose, Requirements, Architecture, and Model Choice
This episode explains how sound AI governance starts with disciplined design choices instead of jumping straight to tools or model hype. You will learn how to define the system’s purpose in business terms, translate that purpose into clear functional and nonfunctional requirements, and choose an architecture and model approach that fit the use case, data environment, risk level, and opera
Episode 32 — Build Human Oversight, Metrics, Thresholds, Feedback, and Controls into Design
This episode focuses on designing governance into the system from the beginning by defining how people will supervise the AI, what measurements will show whether it is behaving acceptably, and what thresholds will trigger review, intervention, or shutdown. You will learn why human oversight must be specific to the use case, why metrics should reflect real business and risk outcomes rather
Episode 33 — Identify and Mitigate Design Risks with Harms Matrices, Risk Hierarchies, and Stakeholder Mapping
This episode explains how structured risk tools can improve design quality by forcing teams to think beyond technical accuracy and consider who could be affected, how harm could occur, and which risks deserve the most attention first. You will learn how harms matrices help teams catalog possible negative outcomes, how risk hierarchies help prioritize those outcomes based on severity and l
Episode 34 — Strengthen AI Designs Through Use-Case Evaluation, Benchmarking, Pilots, and Testing
This episode shows how design quality improves when organizations challenge assumptions before full deployment. You will examine how use-case evaluation helps confirm that the proposed system actually fits the business need, how benchmarking can compare candidate models or methods against defined performance and risk criteria, how pilots reveal workflow problems in limited settings, and h
Episode 35 — Document Design and Build Decisions to Prove Compliance and Manage Risk
This episode explains why documentation is not a bureaucratic afterthought but a core governance control that shows what was built, why it was built that way, and how risks were considered along the way. You will learn how design and build records support accountability by capturing requirements, architecture choices, data decisions, testing assumptions, control selections, approvals, kno
Episode 36 — Govern Training Data Rights, Quality, Quantity, Integrity, and Fitness for Purpose
This episode focuses on the governance questions surrounding training data, which often determine whether an AI system is lawful, reliable, and appropriate for its intended use. You will learn why teams must examine data rights before using information for model development, why data quality affects downstream performance and fairness, why quantity matters but does not solve representatio
Episode 37 — Establish Data Lineage and Provenance You Can Defend Under Scrutiny
This episode explains why organizations need to know where their data came from, how it moved, what changed along the way, and who handled it if they want defensible AI governance. You will learn that data lineage tracks the flow of information through collection, transformation, storage, training, testing, and deployment, while provenance focuses on origin, authenticity, and the context
Episode 38 — Plan Training and Testing Across Unit, Integration, Validation, Performance, Security, and Bias
This episode introduces a fuller view of AI assurance by showing how training and testing should span multiple layers rather than focusing on a single accuracy score. You will learn how unit testing checks specific components, how integration testing evaluates how the system behaves within a broader workflow, how validation confirms that the system meets defined requirements, and how perf
Episode 39 — Improve Interpretability and Reduce Model Risk During AI Testing
This episode focuses on interpretability as a practical governance tool that helps organizations understand how a model behaves, where it is fragile, and how much trust its outputs should receive. You will learn why interpretability does not always mean full transparency into every internal mechanism, but it does mean producing enough understanding for testers, reviewers, and decision-mak
Episode 40 — Manage Training and Testing Issues While Documenting Results for Compliance
This episode explains how organizations should handle problems discovered during training and testing without losing traceability or governance discipline. You will learn why issue management matters when models show bias, instability, weak performance, security flaws, data defects, or unexplained behavior, and why it is not enough to fix a problem informally and move on. For the AIGP exa
Episode 41 — Assess Release Readiness with Model Cards and Conformity Requirements
This episode explains how organizations determine whether an AI system is ready to move from testing into real use without treating release as a guess or a deadline-driven compromise. You will learn how model cards can summarize intended use, performance limits, known risks, testing outcomes, and appropriate cautions, while conformity requirements help confirm that the system meets applic
Episode 42 — Build Continuous Monitoring, Maintenance, Updates, and Retraining Rhythms for Released AI
This episode focuses on what happens after launch, when an AI system must be monitored and maintained as a living system rather than treated as a finished product. You will learn why continuous monitoring matters for performance, fairness, security, drift, and user impact, and how maintenance, updates, and retraining should follow defined rhythms rather than ad hoc reactions. For the AIGP
Episode 43 — Assess Production AI After Release with Audits, Red Teaming, Threat Modeling, and Security Testing
This episode explains how organizations should examine AI systems in production using methods that go beyond routine monitoring and basic performance checks. You will learn how audits provide structured reviews of whether controls and documentation remain aligned with policy and legal obligations, how red teaming can expose misuse paths and unsafe behavior, how threat modeling helps teams
Episode 44 — Investigate AI Incidents with Cross-Functional Teams Tracing Drift, Data Gaps, and Brittleness
This episode focuses on incident investigation when an AI system behaves unexpectedly, causes harm, or fails under real-world conditions. You will learn why AI incidents often require cross-functional analysis involving technical teams, legal, privacy, security, product, and business stakeholders, because the root cause may involve more than a coding defect. The episode explains how drift
Episode 45 — Meet Transparency Duties with Technical Documentation, Instructions, and Monitoring Plans
This episode explains how transparency becomes operational through documentation, user-facing instructions, and monitoring plans that make an AI system understandable enough to govern and use responsibly. You will learn why technical documentation matters for internal review, why instructions for deployers or users must communicate intended use and known limits, and why monitoring plans s
Episode 46 — Review AI Development Governance from Impact Assessments to Public Disclosures
This episode pulls together the development lifecycle by showing how governance starts with early impact assessments and continues through design reviews, testing evidence, approval decisions, and, when required, public-facing disclosures. You will learn that development governance is not a single committee meeting or control checkpoint, but a chain of documented decisions that should rem
Episode 47 — Evaluate Deployment Context, Business Goals, Ethics, Data, and Workforce Readiness
This episode explains why a technically capable AI system can still be a poor deployment decision if the surrounding business and operational context are not ready for it. You will learn how to evaluate the deployment setting by examining business goals, ethical implications, available data, workforce readiness, and the practical conditions under which the system will actually be used. Fo
Episode 48 — Compare AI Model Types Before Choosing What Your Organization Will Deploy
This episode focuses on comparing model types so organizations choose an approach that fits the use case, risk profile, explainability needs, and operational environment instead of defaulting to whatever is popular. You will learn why different model types create different governance tradeoffs involving accuracy, interpretability, adaptability, data requirements, security exposure, and co
Episode 49 — Choose Deployment Options Across Cloud, On-Premise, Edge, Fine-Tuning, RAG, and Agentic Architectures
This episode explains how deployment architecture shapes governance by affecting data exposure, control boundaries, latency, integration complexity, and responsibility allocation. You will learn how cloud deployment can offer scale but may raise vendor and data handling concerns, how on-premise options can increase control but require stronger internal capability, how edge deployment chan
Episode 50 — Assess Selected AI Systems with Focused Impact Reviews Before Deployment
This episode explains why organizations should conduct focused impact reviews before deployment even after a system has already been selected, because choosing a tool is not the same as proving it is safe and appropriate for the intended use. You will learn how these reviews test whether the chosen system fits the deployment context, whether legal and ethical risks are understood, whether
Episode 51 — Evaluate Vendor Contracts and Licensing Terms Before You Deploy AI
This episode explains why AI governance must include careful review of vendor contracts and licensing terms before deployment, because legal and operational exposure often hides in clauses that technical teams overlook. You will learn how contract language can affect data rights, confidentiality, liability allocation, audit access, security commitments, model improvement rights, service l
Episode 52 — Understand the Unique Risks, Opportunities, and Obligations of Deploying Proprietary AI
This episode focuses on proprietary AI systems, which can offer performance, customization, or competitive advantage while also creating governance demands that differ from open or broadly shared tools. You will learn how proprietary systems may introduce tighter vendor dependency, reduced transparency, limited testing visibility, and stronger reliance on contract assurances, while at the
Episode 53 — Apply Governance Controls to Deployment Through Data, Risk, Issue, and User Training
This episode explains how deployment governance becomes real through operational controls that shape how data is handled, how risks are tracked, how issues are escalated, and how users are prepared to interact with the system responsibly. You will learn why data controls must address access, retention, quality, and permitted use, why risk controls must define thresholds and ownership, why
Episode 54 — Conduct Ongoing Monitoring, Maintenance, Updates, and Retraining After Deployment
This episode focuses on post-deployment stewardship, which is essential because AI systems continue to change in effect even when their code appears stable. You will learn why ongoing monitoring must track performance, fairness, reliability, security, and user impact, and why maintenance, updates, and retraining require formal triggers, documentation, and approval rather than casual techn
Episode 55 — Verify Deployed AI with Audits, Red Teaming, Threat Modeling, and Security Testing
This episode explains how deployed AI systems should be verified through deliberate assurance activities that test more than routine business performance. You will learn how audits confirm whether policies, controls, and records are being followed in practice, how red teaming can surface misuse paths and unexpected system behavior, how threat modeling helps anticipate attacker goals and w
Episode 56 — Document Incidents and Post-Market Monitoring While Reducing Secondary Uses and Downstream Harms
This episode focuses on the governance work that follows deployment when organizations must document incidents, sustain post-market monitoring, and control how AI systems are used beyond their original approved purpose. You will learn why incident records matter for accountability, trend analysis, remediation, and legal defensibility, and why post-market monitoring is necessary to detect
Episode 57 — Establish External Communication Plans and Deactivation or Localization Controls for AI
This episode explains why deployment governance must include plans for what the organization will say externally and what technical or operational controls it can use if the system must be limited, localized, or shut down. You will learn how external communication plans support transparency during incidents, user complaints, major changes, or regulatory inquiries, and why those plans shou
Episode 58 — Synthesize Development and Deployment Governance into One Defensible Decision-Making Framework
This episode brings the full course together by showing how development governance and deployment governance should operate as one connected decision-making framework rather than as separate bodies of work. You will learn how early impact assessments, design reviews, data governance, testing evidence, release approvals, deployment controls, monitoring, incident response, and retirement pl
Welcome to the AIGP Course!
Welcome to The Bare Metal Cyber AIGP Audio Course—your practical companion for preparing for the IAPP Artificial Intelligence Governance Professional (AIGP) certification. Built for busy professionals who need a clear understanding of responsible AI governance, this audio course turns the major AIGP topics into clear, structured lessons you can follow anytime, anywhere. Each episode stays
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