RAG hallucination is mostly a retrieval problem, not a model problem. Diagnose the failing layer, then fix retrieval, freshness, prompts, no-answer behavior.
Enterprise AI & Engineering Insights — Page 8
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A GDPR subject request about AI starts a 30-day clock. When every prompt and retrieval sits on one queryable record, answering is a lookup, not a scramble.
Under GDPR Article 22 a person can demand to know what your AI decided about them. A complete, signed record turns that request from a scramble into a query.
A signed decision receipt is a portable record of what an AI decided and why — verifiable offline against a published key, with no access to your systems.
Tax and audit work can't rest on an AI's unverified word. An assistant that shows its sources and keeps an audit trail fits how these firms already work.
Insurance AI faces two skeptics: underwriting needs it accurate and explainable, compliance needs it controlled and auditable. Governed AI satisfies both.
Legal AI that professionals trust is grounded in the actual documents and cites a verifiable source for every point — traceable, defensible, and on the record.
Keep enterprise data out of public models with a private RAG stack: self-hosted LLMs and vector databases, in-boundary ingestion for VPC, on-prem, air-gapped.
Enterprise AI pilots fail from ungrounded models, not weak ones. Grounded means answers from your facts, verified, permission-aware, and recorded.
An AI answer without provenance is an opinion. Provenance means every answer carries its citations, retrieval scope, and redactions — traceable to its inputs.
How enterprise RAG changes under HIPAA, SEC/FINRA, and legal privilege — permission-aware retrieval, tamper-evident audit trails, and human-in-the-loop review.
Each EU AI Act high-risk AI obligation mapped to a runtime control you can operate and demonstrate — not a policy document you file and hope no one tests.