Abstract
A method for deduplicating AI security incident records using a deterministic, environment-salted cryptographic hash (HMAC-SHA256) computed over incident-identifying fields, stored as a mandatory, schema-validated field on every incident record within a layered incident-classification architecture. The architecture further defines a distinct classification layer that separates the recording of acute (immediate-impact) harm from chronic (long-term, systemically-accumulating) harm as parallel fields on the same incident record — including a longitudinal harm-accumulation indicator and a trust-erosion tracking field — rather than treating chronic harm as a special case requiring a separate deduplication or merge decision. This architectural separation allows acute and chronic AI harms, which differ fundamentally in detection latency and evidentiary character, to be tracked, validated, and reported independently while remaining part of a single deduplicated incident identity. Published to establish prior art.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singh, Mukesh Kumar, "Cryptographic Hash-Based Incident Deduplication with Architecturally-Separated Acute and Chronic Harm Classification for AI Security Incident Records", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10505