Abstract
A severity scoring system and method for classifying artificial intelligence (AI) security incidents. The system computes a severity score using a dominant-harm anchor: the highest-weighted harm dimension present in an incident is taken as the base score, with all other present harm dimensions contributing only half their summed weight, capped at a fixed maximum, preventing minor cumulative harms from arithmetically equalling a single severe harm. Situational context factors (attack-vector-specific) are added on top of the harm-anchored base score using a diminishing-contribution model in which each successive context, ordered by descending base value, contributes half the weight of the previous context, subject to both a per-factor cap and a hard aggregate cap. The resulting base score is scaled by an impact factor derived from the maximum (not product) of a population-affected multiplier and a blast-radius multiplier, then adjusted by a temporal/confidence factor that can only reduce — never inflate — the score, based on attribution confidence, investigation status, and evidence quality. The final presentation score maps to a five-level severity matrix and feeds a regulatory-trigger function that replaces binary pass/fail thresholds with a confidence-banded determination (HIGH/MEDIUM/LOW), tied to calibration status, that mandates human review when confidence is low. This disclosure is published to establish prior art and prevent patent claims on these methods by any party.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singh, Mukesh Kumar, "Dominant-Harm-Anchored Severity Scoring Engine for AI Incident Classification with Diminishing-Contribution Context Modeling and Calibration-Aware Regulatory Trigger Confidence Bands", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10504