Governed Knowledge Base Integrity Workshop

Enterprise knowledge bases encode more than facts. They also encode policy preferences, operational assumptions, persuasive phrases, reviewer norms, and routing defaults that shape downstream retrieval-augmented generation and recommendation behavior. This paper proposes Ideological Drift Detection for Governed Knowledge Bases (IDD-GKB), a synthetic architecture for detecting when recurring policy narratives, compliance phrases, documentation claims, and retrieved evidence gradually change meaning or influence while remaining superficially consistent. IDD-GKB combines phrase-lineage analysis, hybrid semantic-relational retrieval, critic-guided documentation maintenance, distributed RAG, anonymized evidence views, and latency-aware sequence models. It extends Retrieval-Grounded Documentation Agents for Enterprise Compliance Evidence by adding drift scores over claim lineages and extends Cross-Cloud LLMOps Scheduler for Privacy-Budgeted RAG and Inference by feeding those drift scores into privacy-budgeted route and context selection. We define the architecture, a lineage-and-evidence drift model, and a simulated benchmark over governed policy, support, incident, and compliance corpora. In simulation, IDD-GKB improves drift-event F1 from 0.61 to 0.84, reduces unsupported narrative propagation by 57.8%, and preserves zero unauthorized sensitive-field disclosures under the benchmark privacy policy.