Abstract
Systems and methods for enterprise software compliance verification may address certain limitations associated with manual and subjective evidence gathering. A technique can involve capturing and cryptographically sealing data that represents a proposed system change, such as a code promotion or configuration update. This sealed data payload can then be subjected to a dual-path analysis. For example, one path can utilize an artificial intelligence (AI) subsystem to generate a set of compliance facts. In parallel, a second, non-AI deterministic subsystem can use rule-based parsers to extract a separate set of reference technical facts from the same payload. The system may compare the AI-generated facts against the deterministically-derived reference facts to check for discrepancies. This process can be configured to create a proactive, mathematically verifiable, and tamper-evident audit trail, which may improve the integrity and reliability of enterprise software governance by reducing reliance on manual evidence.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Saripalli, Kiran, "The Dual-Path Compliance Verification Using AI Analysis with Deterministic Cross-Validation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12375