Deterministic Knowledge Gatekeeper

DKG is the deterministic knowledge gatekeeper within GenerativeDrive OS.

It compiles an organization’s approved documentation, policies, datasets, and records into a governed canonical corpus. Every output is evidence-linked, replayable, and auditable, operating under explicit institutional governance rules.

Truth in DKG is relative to the organization’s own sources.

The canon is constructed exclusively from documentation, policies, datasets, and records explicitly provided and governed by the organization. DKG does not assert external, absolute, or inferred truth.

Its role is to ensure internal consistency, traceability, and durability of knowledge within defined institutional boundaries.

DKG enforces internal coherence across the canonical corpus and prevents silent drift.

Every decision path is traceable — sources → claims → checks → outputs — producing outcomes that remain stable across audits, leadership changes, and regulatory review.

DKG governs reasoning, not reality.

The correctness of outcomes depends on the completeness and integrity of the material supplied by the organization. When inconsistencies, gaps, or outdated inputs are detected, they are surfaced explicitly.

Responsibility for truth and updates remains with human governance by design.

Ingestion occurs through governed workflows: explicit approvals, manifests, and controlled updates.

Queries return not only answers but full provenance packages — citations, traces, and reproducibility metadata — suitable for review, audit, and institutional memory.

DKG is deterministic by construction and evidence-first by default.

It operates fail-closed, with no silent overrides, no free-form execution, and no hidden actions. All behavior is inspectable, controlled, and logged within GenerativeDrive OS.