Skip to content
Artwork for Certified: The IAPP AIGP Audio Course
TechnologyEducationCourses

Certified: The IAPP AIGP Audio Course

Jason Edwards

Certified: The IAPP AIGP Audio Course is built for professionals who need a practical path into AI governance without having to stop their day job to get there. It is a strong fit for privacy professionals, compliance teams, risk managers, security leaders, legal and policy staff, product managers, consultants, and anyone else who now has AI oversight in their role. The course assumes you are motivated and capable, but not necessarily deep in technical machine learning work. It starts from clear foundations and then moves into the governance, risk, accountability, and decision-making issues that matter in real organizations. If you are trying to understand how responsible AI programs are structured, how governance connects to business use, and how to prepare for the AIGP certification in a way that feels manageable, this course gives you a steady and usable learning path.

You will learn the language, concepts, and operating mindset behind modern AI governance in a format designed for listening first. The lessons explain how organizations think about AI risk, accountability, transparency, oversight, policy design, lifecycle controls, third-party considerations, documentation, and cross-functional decision-making. Instead of sounding like a policy manual read into a microphone, the teaching is built to be clear in your headphones, in your car, on a walk, or between meetings. Each episode is shaped to help you absorb complex ideas through straightforward explanation, practical framing, and repeated connection to real workplace decisions. That matters because AI governance can feel abstract when it is presented as a wall of terms. In audio form, the material becomes easier to follow, easier to revisit, and easier to connect to the kinds of judgment calls professionals face every day.

What sets this course apart is that it treats the certification as important, but not as the only goal. You are not just memorizing terms for a test. You are building a working understanding of how AI governance fits into real organizations, how roles and responsibilities should be defined, where risk and compliance pressures show up, and how to think clearly when rules, innovation, and business pressure collide. The teaching stays grounded, avoids unnecessary jargon, and respects the fact that most learners want both exam readiness and practical value. Success here means more than finishing episodes. It means you can hear a new AI initiative, understand the governance questions behind it, speak more confidently across teams, and walk into the IAPP AIGP exam with a stronger sense of structure, purpose, and control.

Play
  • 20 episodes
  • Avg 17 min
  • English
  • April 19 · 59 sec

    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 grounded in real governance work and exam-focused thinking, helping you understand not just what to study, but how to frame governance decisions, apply laws and frameworks, define accountability, spot common traps, and choose the best next step. Whether you’re commuting, exercising, or fitting in study time after work, this series is designed to keep you consistent, focused, and moving forward.

  • #58
    April 4 · 19 min

    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 planning all support a continuous chain of accountability. For the AIGP exam, this final synthesis matters because strong answers usually reflect integration. The best governance response is rarely a single policy, committee, or test result. It is a framework that connects purpose, risk, roles, documentation, oversight, and corrective action across the full lifecycle of the system. In real organizations, defensible governance depends on continuity between what was promised during development and what is actually controlled after deployment. When those pieces stay aligned, the organization is better prepared to explain its decisions, manage changing risk, and demonstrate that AI was governed with discipline from beginning to end. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #57
    April 4 · 18 min

    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 should be prepared before a crisis instead of improvised under pressure. The episode also explores deactivation and localization controls, which help organizations disable risky functionality, restrict use to certain jurisdictions or business contexts, and contain harm when a system cannot be trusted in all environments. For the AIGP exam, the important insight is that responsible governance includes contingency planning, not just successful launch planning. In real practice, organizations that cannot explain what happened, who is affected, or how the system can be limited during a problem are often less resilient than they appeared during deployment. Good governance prepares both the message and the control lever before they are urgently needed. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #56
    April 4 · 18 min

    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 harms that only become visible after real users, real workflows, and real incentives shape system behavior. For the AIGP exam, the key lesson is that governance must address secondary use and downstream harm, not just the primary deployment scenario. A tool introduced for one purpose can later be repurposed, integrated elsewhere, or relied on more heavily than intended, which can create new risks that were never reviewed. In practice, organizations reduce those risks by defining permitted uses, watching for misuse, documenting adverse events, and updating controls when monitoring reveals new patterns of harm or exposure. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #55
    April 4 · 18 min

    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 weak points in the design, and how security testing provides evidence about resilience under realistic conditions. For the AIGP exam, this topic matters because governance is not complete unless the organization checks whether deployed controls actually work. A system may appear stable in normal use while still being vulnerable to manipulation, integration flaws, or control breakdowns. In real environments, verification activities help organizations discover hidden risk before adversaries, regulators, or affected users do. Strong governance uses these methods not as one-time events, but as recurring mechanisms for learning, correction, and sustained accountability after deployment. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #54
    April 4 · 17 min

    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 technical adjustment. For the AIGP exam, the main lesson is that deployment is not the end of governance. An AI system can become riskier over time due to data drift, new user behaviors, changing business conditions, or evolving legal expectations, so the organization must be prepared to intervene. The episode also explores practical measures such as change logs, monitoring dashboards, retraining thresholds, exception review, and rollback plans. In real practice, organizations that treat post-deployment care as routine operational work are better able to spot weak signals early and prevent small quality issues from becoming larger compliance, safety, or reputational problems. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #53
    April 4 · 17 min

    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 issue controls must support reporting and corrective action, and why user training must explain not just how to use the AI, but when to question it, override it, or stop using it. For the AIGP exam, the strongest answer is often the one that links deployment readiness to practical controls instead of abstract policy language. In real environments, systems fail when users are undertrained, issues are handled informally, or data flows exceed what was reviewed and approved. Strong governance makes deployment safer by turning expectations into routines that teams can follow consistently and defend under scrutiny. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #52
    April 4 · 18 min

    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 same time offering opportunities such as specialized capability, controlled deployment environments, and support aligned to specific business needs. For the AIGP exam, the key point is that governance must account for both the benefits and the constraints of proprietary deployment. A closed system may simplify some operational choices, but it can also make it harder to assess training data, explain model behavior, validate claims, or monitor hidden changes. In real organizations, the governance challenge is to avoid assuming that a proprietary product is safer simply because it is commercial and polished. Good oversight requires careful review of documentation, obligations, controls, and the organization’s ability to supervise what it does not fully own or see. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #51
    April 4 · 18 min

    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 levels, and termination options, while licensing terms can restrict how outputs are used, whether fine-tuning is allowed, and who bears responsibility for downstream misuse. For the AIGP exam, the important lesson is that governance does not stop at technical evaluation or privacy review. A well-chosen tool can still become a bad deployment decision if contractual terms undermine oversight, shift risk unfairly, or permit uses that conflict with the organization’s legal and ethical obligations. In real practice, strong governance means reviewing not only what the AI can do, but also what the vendor is allowed to do with your data, how problems are handled, and whether the agreement supports defensible deployment. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #50
    April 4 · 16 min

    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 controls and human oversight are adequate, and whether the organization is prepared to monitor and respond once the system goes live. For the AIGP exam, the important insight is that pre-deployment review should be specific to the selected implementation, data flows, user groups, and decision impacts rather than relying on generic vendor claims or earlier high-level assessments. In real practice, focused reviews often catch issues involving integration, rights impacts, role confusion, or weak escalation paths that were not obvious during procurement or design. Good governance pauses before deployment to confirm that the actual system in the actual environment is ready. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #49
    April 4 · 18 min

    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 changes local processing and update challenges, and how approaches such as fine-tuning, retrieval-augmented generation, and agentic architectures introduce different risks and oversight needs. For the AIGP exam, the goal is to recognize that architecture choices are not neutral. They influence privacy posture, security testing, monitoring complexity, and the degree to which an organization can explain and manage system behavior. The episode also covers practical tradeoffs, such as how a RAG approach may reduce some hallucination risk through grounding while creating new governance concerns around source quality, retrieval scope, and prompt paths. Good governance compares deployment models in operational terms, not just technical excitement. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #48
    April 4 · 18 min

    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 cost of control. For the AIGP exam, this means understanding that model choice is a governance decision as well as a technical one. A narrow predictive model, a rules-based system, a recommender, and a generative model can all appear useful, but they create different documentation, testing, monitoring, and oversight demands. The episode also explores practical examples where a simpler model may be more defensible because it is easier to explain, validate, and bound, especially in higher-stakes settings. In real practice, strong governance compares options deliberately and selects the one that best supports safe, lawful, and sustainable deployment. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #47
    April 4 · 17 min

    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. For the AIGP exam, the key lesson is that deployment decisions must account for context, not just model performance. A system may look strong in testing but still fail if staff are not trained, escalation paths are unclear, data feeds are unreliable, or the organization has not defined what responsible use should look like in practice. The episode also explores real-world examples where AI adoption creates confusion because teams lack the authority, skills, or governance structure to supervise it well. Good deployment evaluation asks whether the organization is ready, not just whether the tool is available. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #46
    April 4 · 17 min

    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 remain aligned from planning through release. For the AIGP exam, this matters because questions often test whether you can see the connection between early risk identification, later design choices, and the disclosure obligations that may arise once a system is offered to users, customers, or the public. The episode also highlights real-world mistakes such as incomplete assessments, undocumented exceptions, unsupported claims about system capability, or disclosures that are too vague to be useful. Strong governance creates continuity so the story told externally can be supported by the evidence captured internally throughout development. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #45
    April 4 · 19 min

    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 show how the organization will keep watch after release instead of assuming the system will remain stable. For the AIGP exam, this topic often appears in scenarios where a system may perform acceptably, but the governance weakness lies in poor communication, incomplete records, or the absence of a clear plan for oversight. The episode also covers practical benefits such as easier audits, better incident response, clearer user expectations, and stronger accountability when something goes wrong. In real organizations, transparency duties are easier to satisfy when documentation is built into the lifecycle rather than rushed at the end as a defensive paperwork exercise. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #44
    April 4 · 17 min

    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 can change performance over time, how data gaps can create blind spots or unstable outputs, and how brittleness appears when a system fails outside the narrow conditions it handled well in testing. For the AIGP exam, the main lesson is that incident response must include investigation, documentation, remediation, and governance review rather than only a quick technical patch. In practice, strong organizations trace what changed, who was affected, what controls failed, and whether the use case or system should be limited, retrained, redesigned, or removed from service. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #43
    April 4 · 18 min

    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 think through attacker goals and weak points, and how security testing validates whether the system can withstand realistic abuse. For the AIGP exam, this topic matters because post-release assurance is a core part of governance, especially when systems operate in higher-risk settings or handle sensitive data. The episode also highlights real-world issues such as prompt manipulation, unauthorized model access, data leakage, insecure integrations, and hidden process failures. Good governance requires organizations to test production reality, not just development assumptions, and to use those findings to improve controls, documentation, and operational resilience. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #42
    April 4 · 16 min

    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 exam, the important point is that governance does not end at deployment. Released systems can degrade, face new threats, encounter changing data conditions, or produce new harms as their environment evolves. The episode also explores practical considerations such as threshold-based alerts, update approval processes, retraining triggers, change documentation, and rollback planning. In real organizations, disciplined post-release care reduces surprises because teams know what to watch, when to intervene, and how to preserve traceability as the system changes over time. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #41
    April 4 · 17 min

    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 applicable internal controls, legal expectations, and governance standards before launch. For the AIGP exam, the key lesson is that release readiness depends on evidence, not optimism. Teams must be able to show that documentation is complete, controls are in place, limitations are understood, and approvals reflect the actual risk of the use case. In practice, release decisions become more defensible when organizations use structured artifacts and checklists to prove that the system is not only functional, but governed well enough for deployment. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
  • #40
    April 4 · 17 min

    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 exam, the strongest answer often includes documenting what was found, how serious it was, what corrective action was taken, who approved the response, and whether retesting confirmed that the issue was resolved or remained as a known limitation. The episode also covers practical examples such as threshold failures, unexpected drift during validation, or red-team findings that require design changes before release. In real organizations, disciplined issue handling supports compliance because it shows that concerns were identified, escalated, tracked, and addressed in a repeatable way. Good governance turns testing problems into accountable decisions instead of hidden technical debt. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with. And dont forget Cyberauthor.me for the companion study guide and flash cards!

    • Transcript
Showing 1–20 of 20 episodes