Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article introduces the Principle of Evidentiary Compliance, a novel theoretical framework that re-conceptualizes AI explainability for regulated industries. The prevailing focus on model-centric transparency is fundamentally flawed for complex, non-deterministic systems. Instead, I propose a pivot to data-centric provability, where regulatory adherence is demonstrated through a cryptographically secured, immutable "Diagnostic AI Ledger." This architectural instantiation of the Evidentiary Compliance principle transforms generative AI systems from opaque "black boxes" into forensically verifiable decision-making engines. By creating an unalterable chain of evidence, this framework resolves the intractable conflict between deploying advanced AI and satisfying stringent regulatory mandates. Validation through reverse-engineering audit simulations in financial compliance scenarios confirms the framework's efficacy, establishing a new and essential standard for the responsible adoption of AI in mission-critical environments.