A Verifiable Hallucination Risk Index for Enterprise AI Systems

Large Language Models can produce fluent answers that are not fully supported by facts, source documents, or the operational context in which they are used. Existing research evaluates related phenomena through truthfulness benchmarks, attribution frameworks, citation-quality benchmarks, atomic factuality metrics, black-box consistency checks, and retrieval-augmented generation evaluation. This paper proposes a Verifiable Hallucination Risk Index (HRI) for enterprise AI systems. The index is not presented as a universal benchmark, but as an auditable, claim-level scoring framework that combines factual inconsistency, context deviation, unsupported claims, data freshness, scope mismatch, access or permission gaps, and validated human feedback. Compared with model-level hallucination benchmarks, the proposed HRI treats hallucination risk as a system-level reliability problem emerging from the interaction between model behavior, available evidence, governance constraints, and human validation. The paper contributes an operational claim scoring method, a numerical example, a provisional risk-band model, and a source verification protocol. Empirical validation and calibration of thresholds remain future work.