AI-Native IP Disclosure and Release Governance: A Dependency-Aware Control Plane for Irreversible Intellectual-Property Decisions

An approval is not a release. A release is not its legal effect. And a disclosure rarely exists in isolation. These distinctions expose a fundamental limitation of conventional intellectual-property management and technical approval workflows: they can record individual decisions without adequately representing how technical acts, accumulated disclosures, changing rights positions, and historical dependencies interact over time. AI-Native IP Disclosure and Release Governance develops a distinct systems-engineering method for addressing this challenge. Building on the strategic framework established in Governing the Moat, the paper advances an act-centered, stateful, and dependency-aware IP governance approach. Its proposed contribution is not an isolated algorithm or another compliance checklist, but a coherent architectural composition of semantic relationships, temporal context, cumulative disclosure analysis, provenance-preserving dependencies, authority separation, and verifiable design properties. The paper develops illustrative formal models and compares alternative architectural patterns rather than prescribing a single reference implementation. It identifies observable properties against which different realizations of the method may be examined, including the integrity of decision context, evidence, authorization boundaries, and reassessment following material changes. Artificial intelligence is positioned as an analytical and orchestration capability operating within explicit semantic and authority constraints—not as an autonomous source of legal or commercial decision authority. The underlying proposition is that consequential technical acts must be governed through their changing meaning, dependencies, and effects—not merely through the approval status of the artifacts involved. For systems architects, technology investors, strategic partners, and IP-intensive organizations, this offers a different perspective on the relationship between intellectual property, AI, software architecture, and enterprise governance. The publication presents implementation-independent architectural methods and deliberately non-prescriptive examples. Proprietary decision logic, operational parameters, actual implementation details, and confidential know-how remain undisclosed. It does not claim empirical validation of a specific implementation. The result is a technical companion to Governing the Moat: an explicit method for reasoning about IP governance as a coherent systems-engineering problem.