Certified: The IAPP AIGP Audio 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...

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 al...

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 pre...

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 tha...

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 point...

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 adju...

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 co...

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 tim...

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, an...

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...

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...

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 con...

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 AIG...

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 align...

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 t...

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 chan...

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 th...

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, th...

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 inte...

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 st...

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-makers to ev...

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 performance,...

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 needed to...

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 representational gaps...

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, known limita...