LLMs produce fluent answers, not defensible ones. Introducing Investigation Provenance, Answer Debt, and Kill Threshold Governance: the three primitives enterprises need before AI conclusions can be trusted in regulated decisions.
Every enterprise using AI to answer hard questions (due diligence, clinical pathway evaluation, fraud signal validation, exposure estimation) faces a structural problem that no current governance framework names or resolves. The problem is not that AI gives wrong answers. The problem is that AI gives answers with no accountable record of how they were reached.
A wrong answer with a traceable evidence record is a recoverable error. A wrong answer with no record is a liability. The distinction matters in every regulated industry, in every board-level AI review, and in every context where a human signature follows an AI conclusion.
This article introduces three original constructs that close this gap. Investigation Provenance is the traceable chain from question to evidence to claim that makes an AI conclusion auditable. Answer Debt is the organizational liability that accumulates when AI conclusions circulate without that chain. Kill Threshold Governance is the discipline of committing stopping criteria before an investigation begins, so results cannot be silently cherry-picked after the fact.
These constructs are grounded in an emerging open-source implementation called Zorp (aviskaar/zorp, MIT, pre-alpha), which applies pre-registration discipline borrowed from clinical trial methodology to AI-driven investigation. The patterns it demonstrates are not product-specific. They describe an architectural layer that enterprise AI is currently missing.
enterprise AI governance frameworks [1] addresses risk categorization, output monitoring, and human oversight at the organizational level. applicable AI governance standards [2] addresses AI management system requirements. Both are governance frameworks for AI systems. Neither addresses the investigative accountability layer: the question of whether an AI conclusion is traceable to the evidence that produced it, and whether that evidence was gathered under conditions that prevent post-hoc selection.
This is not a minor omission. It is the precise gap that creates enterprise liability in regulated decisions. Consider three common scenarios:
In each case, the answer is plausible. In each case, the answer is not defensible. The distinction becomes material when a regulator asks, a court asks, or a risk committee asks: show me what this conclusion is accountable to.
Answer Debt is the organizational liability that accumulates when AI conclusions circulate without a traceable evidence record. Like technical debt, it is invisible until it is called in. Unlike technical debt, it may be called in by a regulator, a court, or a board rather than an engineering team. Answer Debt compounds when AI conclusions are used as inputs to subsequent decisions, because the provenance gap propagates forward through every downstream use of the original conclusion.