Turn policies and control objectives into verifiable runtime, release, decision, and audit evidence.
Learning outcomes
Curriculum
The chapters are individually addressable documentation pages. You can link directly to a concept from another program, architecture decision, or implementation guide.
Establish precise sovereignty terms, dimensions, control objectives, and evidence principles.
Connect policy intent to owned controls, technical enforcement, verifiable evidence, and decisions.
Evaluate data, model, infrastructure, operational, and evidence control as separate but connected dimensions.
Distinguish sovereignty from privacy, residency, localization, autonomy, portability, and security.
Translate sovereignty goals into testable statements of what must be allowed, prevented, approved, observed, retained, and recovered.
Design evidence alongside controls so important decisions, releases, configurations, and operating events can be verified.
Connect obligations and risks to owned, tested, evidenced controls.
Trace obligations and risk decisions to control objectives, owners, implementations, tests, and evidence.
Identify software, models, data, prompts, policy, and configuration with verifiable origin and lineage.
Preserve user, workload, service, and tool identity across AI boundaries and make policy decisions explicit.
Assemble the system passport, evaluation evidence, and operational evidence used in release decisions.
Create a versioned system record that makes scope, components, evaluations, approvals, and limitations inspectable.
Turn acceptance criteria into repeatable promotion decisions with recorded evidence and rollback conditions.
Design signals that explain service health, AI behavior, policy outcomes, change, and incidents without leaking sensitive content.
Govern exceptions and material change with explicit decisions and reversal conditions.
Run proportionate reviews, document exceptions, trigger reassessment, and preserve independent challenge.
Version complete AI behavior, assess change impact, approve promotion, support rollback, and retire superseded assets.
Record context, options, control consequences, evidence, dependencies, limitations, and reversal triggers for material decisions.
Produce an assurance package and evaluate its decision usefulness.
Assemble a control-to-evidence traceability matrix, evaluate evidence quality and exceptions, and issue a deterministic release recommendation for a synthetic system passport.
Runnable synthetic prototype with recorded validation
Run a synthetic local retrieval system that enforces document permissions, citations, deletion, no-answer behavior, and injection controls.
Runnable synthetic prototype with recorded validation
This public curriculum supports technical assurance practice. It is not legal advice, regulatory certification, an audit opinion, or production approval. Required evidence and independence must be tailored to the organization and applicable obligations.