Search for a command to run...
This release presents Representative Work 1 from the broader AI Auditability Architect project: AI Audit Control Matrix 1.0 (v0.1). It is a bounded AI auditability architecture for low-observability environments. The central problem addressed by this release is that many organizations do not begin with rich telemetry, complete logging, mature AI governance infrastructure, or fully observable model and vendor behavior. In practice, AI use often emerges under constrained visibility, fragmented tooling, partial evidence, and uneven operational ownership. This release provides a structured way to think about AI auditability under those conditions. The release is organized around four layers: Auditability thesisA compact account of what makes an AI system auditable in this project’s terms, including distinctions among controls, evidence, test procedures, approval logic, monitoring, residual risk, and assurance boundaries. Control architectureA structured control-matrix layer that translates the thesis into reviewable domains, control objectives, evidence expectations, test procedures, and bounded assurance logic. Implementation profilesA profile-based treatment of observability conditions, including: Profile A for higher-observability settings with richer telemetry Profile B for lower-observability, startup-constrained environments Worked exampleA concrete Profile B worked example based on an internal employee-facing generative AI assistant, with explicit controls, evidence, approval logic, retest logic, monitoring, traceability, and stated assurance limits. What this release contains a bounded auditability thesis an explicit evidence hierarchy an assurance-boundaries note a control-matrix architecture implementation profile logic a Profile B worked example a v0.1 release note and readiness framing selected structured artifacts supporting inspection and reuse What this release does not claim This release does not claim to be: a complete general AI audit standard a full legal or regulatory crosswalk a built-out high-observability implementation package a basis for stronger assurance than the evidence supports proof that Profile B yields Profile A-level assurance a substitute for organization-specific legal, regulatory, security, or compliance review It also does not claim exhaustive observation of AI use, complete third-party transparency, or full model-level auditability in constrained environments. Intended use This release is intended for: practitioners designing early-stage AI governance and auditability structures governance, audit, risk, and control professionals working under incomplete observability researchers interested in AI auditability architecture, bounded assurance, and implementation profiles collaborators evaluating how auditability can be structured before mature telemetry and enterprise-scale instrumentation are available This is a v0.1 release. Its contribution is not a final universal framework, but a disciplined and publicly inspectable architecture for structuring AI auditability where observability is limited and practical governance must begin before ideal conditions exist.