Practitioner library
Deep, decision-focused material for the assurance problems that do not fit a generic checklist.
20 resources in AI Assurance
Foundation · Published
AI changes the assurance problem because correct behavior can no longer be reduced to “the same input always produces the same output.” A professional CSA strategy therefore validates the complete sociotechnical system: intended use, data, model, application logic, human decisions, operating controls, and evidence over time.
Foundation · Published
An AI assurance strategy fails early when the team treats “the model” as the system. This chapter provides a practical taxonomy and boundary method for predictive models, computer vision, generative AI, retrieval-augmented generation, embedded vendor features, and agents.
Foundation · Published
The intended-use statement is the anchor for AI requirements, risk, data, evaluation, human oversight, monitoring, and change control. This chapter shows how to write one that is precise enough to validate.
Foundation · Published
Professional AI assurance starts by identifying the governing process and record—not by declaring that every AI tool is GxP or that every assistant is merely “productivity software.” This chapter provides a feature-level scoping method.
Practitioner · Published
AI governance becomes operational only when every production use has an owner, an identifiable configuration, a risk decision, a monitoring obligation, and a controlled path to change or retirement. This chapter turns policy into lifecycle gates.
Practitioner · Published
AI risk analysis must move beyond a generic statement that “the model may be wrong.” The practitioner must identify how a specific error arises, how it propagates through the process, whether it can be detected, and which controls actually interrupt the chain.
Practitioner · Published
In AI-enabled GxP systems, data is not merely an input. It shapes model behavior, defines the evaluated population, supports the validation conclusion, and may become part of the regulated evidence. This chapter applies data-integrity thinking across the AI lifecycle.
Practitioner · Published
An AI evaluation can look rigorous while materially overstating performance if the data split leaks related records, the challenge set resembles development data, or prevalence differs from intended use. This chapter shows how to design defensible datasets.
Practitioner · Published
Commercial AI creates a layered supply chain: SaaS application, foundation-model provider, cloud host, retrieval service, monitoring tools, and data subprocessors. Supplier leverage is essential, but it cannot replace customer understanding of intended use and configured risk.