Closing the AI Governance Accountability Gap
A Q4 2026 board framework for establishing traceability as the standard-of-record for enterprise AI governance. Free PDF report, 12 pages, by Harish Kumar, Quantamix Solutions.
Most organizations have AI policies, risk registers, model inventories and approval workflows. What they often lack is a single chain of evidence that connects one AI system's business purpose, accountable owner, training data, model version, risk decision, deployment approval, monitoring results and incident record. The report calls the space between a policy requirement and the evidence that it was carried out for a specific system governance white space.
What is inside
- A seven-artifact evidence standard for every material AI system.
- A formula that turns the accountability gap into a board metric: 100% minus complete linked artifacts divided by required artifacts.
- A dated plan from October 2026 to June 2027, with a first accountability-gap scorecard by 15 December 2026.
- The risks that undermine traceability programs, and how to test evidence for completeness, currency, linkage and retrievability.
The key idea
A policy is not a control unless the enterprise can produce system-specific evidence that the policy was applied, approved, monitored and remediated.
This is a practitioner framework, not legal advice and not a peer-reviewed paper. The targets and worked examples in it are the author's proposals, not measured results. Quantamix Solutions builds AI governance software and has a commercial interest in this subject.
Cite as: Kumar, H. (2026). Closing the AI Governance Accountability Gap. Quantamix Solutions B.V. https://doi.org/10.5281/zenodo.23134576. Licensed CC BY 4.0.
By Harish Kumar, Founder, Quantamix Solutions B.V.