From Obligations to Control Placement
Explore how to transform AI system obligations into enforceable controls by assigning primary enforcement points, evidence artifacts, and clear ownership. Understand the process of creating obligation-to-control maps to ensure governance, operational oversight, and audit readiness in AI architectures.
We'll cover the following...
- The obligation-to-control map as a contract
- A repeatable placement method
- Example 1: Transparency and provenance at the retrieval boundary
- Example 2: Human oversight in the containment envelope
- Extending to a full obligation set
- Ownership, review cadence, and re-review triggers
- Acceptance criteria and residual risk handling
Framework mapping produces a list of relevant obligation categories. It does not identify where those obligations are enforced in the architecture. An obligation without an enforcement point remains a documented requirement rather than an implemented control. An obligation becomes actionable when it is mapped to a specific enforcement point. Every in-scope obligation should have a primary enforcement point, a measurable evidence artifact or telemetry signal, and a named owner. Otherwise, the obligation remains documented but unenforced, with no reliable way to demonstrate that the control operated as intended during an audit or to reconstruct system behavior during an incident.
The artifact produced here is an obligation-to-control map that serves as an architecture contract for the end-to-end system. The system already includes an AI gateway policy, permission-aware retrieval, delegated identity, an agent containment envelope, an evaluation release gate, and observability instrumentation. Risk classification helps determine which obligations require stronger controls within the system’s defined scope, while framework mapping identifies the relevant obligation category. This lesson maps each obligation to an enforcement point and the ...