Enterprise AI governance for regulated industries
Traditional governance assumes humans make the decision and machines record it. Modern AI inverts that: the machine decides, often in milliseconds, and a human reviews a sample afterwards. That inversion breaks most existing model-risk frameworks, which were never designed for decision rates measured in thousands per second.
The 'Governing at the Speed of Thought' preprint (SSRN 6562738) sets out five principles: pre-decision policy binding, per-decision explainability, continuous drift monitoring, indemnified audit trails, and human escalation that the machine cannot silently bypass. Each principle maps to a concrete control a regulator can test.
How JAQL applies this: the JAQL AI Observability service and the StressTrace audit layer implement those five principles for banks, hospitals and government clients. Read the preprint for the full framework and the worked banking example.
Research references
Published by Jumpstart AI and Quantum Labs. Co-founders: Dr. Nupur Mukherjee (Chief Science Officer) and Kishan Sathyan (Chief GTM Officer). Research cited as SSRN preprints; JAQL technology is quantum-inspired and runs on classical infrastructure.