Cryptographic Hash-Based Incident Deduplication with Architecturally-Separated Acute and Chronic Harm Classification for AI Security Incident Records

Defensive Publication — Companion record to ODA3 IP Filing Doc #10

Independent record notice This page is published on oda3.org as an independent, separately dated record of the disclosure below. The same disclosure text is also filed on Technical Disclosure Commons (tdcommons.org) as a companion defensive publication. Maintaining both records provides two independent, publicly verifiable timestamps for the same prior art.

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.

Technical Field

Systems and methods for AI security incident record management, specifically (a) cryptographic-hash-based deduplication of incident records across reporting sources, and (b) layered classification architectures that structurally separate acute and chronic AI harm tracking within a unified incident schema.

Background

Prior art incident-management systems typically deduplicate records using exact string matching, simple timestamp proximity, or ticket-number correlation, which is brittle across heterogeneous reporting sources and does not address salting/key-management concerns needed to prevent deduplication-hash collision attacks or cross-organization correlation leakage. Separately, prior art AI incident classification schemes generally treat an incident as a single point-in-time event, which is poorly suited to AI-specific harms that accumulate gradually (e.g., behavioral drift, bias amplification, gradual data poisoning) and are only observable over an extended observation period, as distinct from harms with an immediate, acute onset. Existing schemes do not provide an architecturally separated, schema-enforced mechanism for recording both harm types on a single incident identity while keeping the core deduplication mechanism independent of harm-onset pattern.

1. Cryptographic Deduplication Hash

Each incident record carries a mandatory deduplication_hash field, computed as an HMAC-SHA256 digest (a 64-character hexadecimal string) over incident-identifying content, using a salt key that is environment-specific and managed via a hardware security module or cloud key-management service rather than embedded in code or configuration files. The salt key is subject to organizational key-rotation policy. This field is schema-validated (fixed-length hexadecimal pattern) and is one of the small set of fields mandatory across every conformance profile of the incident schema, ensuring deduplication capability is present even in the minimal (Core) profile.

2. Layered Incident Architecture with Independent Harm-Onset Tracking

The incident record schema is organized into ordered layers, one of which (the Acute vs. Chronic Harm layer) is dedicated to recording: an acute-harm indicator and value; a chronic-harm proxy value; a set of realized-harm categories; and a cumulative-bias-index field for tracking gradual harm accumulation (e.g., bias amplification) over time. This layer additionally supports recording of an observation-period start and end, a threshold-exceeded indicator, and a trust-erosion-rate field, enabling longitudinal monitoring of harms that do not present as a single discrete event. This disclosure describes the architectural role and presence of the cumulative-bias-index and trust-erosion-rate fields within the layer; it does not assert or disclose any particular computation, formula, or algorithm for deriving their values, and no such claim should be inferred. Because this layer is structurally separated from — and additive to — the deduplication-hash field rather than a precondition for it, acute and chronic harms are deduplicated using the same cryptographic mechanism while being classified and monitored through entirely independent fields. This avoids the need for a harm-onset-pattern-specific deduplication branch and keeps the deduplication mechanism’s correctness independent of how harm accumulates over time.

3. Conformance-Profile Gating of Harm-Onset Fields

The acute/chronic harm-separation fields are gated to higher conformance profiles (Enterprise and above) and excluded entirely from the minimal Core profile’s schema (not merely marked optional), while the deduplication-hash field remains mandatory across all profiles including Core. This tiered field-exposure structure allows minimal implementations to deduplicate incidents without taking on the reporting burden of longitudinal chronic-harm tracking, while enabling more mature implementations to layer in chronic-harm monitoring without altering the deduplication mechanism itself.

PRIOR ART COVERAGE

This disclosure covers: (1) use of an HMAC-based cryptographic hash, computed with an environment-salted, HSM/KMS-managed key, as a mandatory schema-validated deduplication field for AI security incident records; (2) architectural separation of acute and chronic AI harm tracking into independent fields (including a cumulative bias-accumulation indicator and a trust-erosion-rate field) on a single deduplicated incident record, rather than treating chronic harm as a deduplication-time special case; (3) conformance-profile-tiered exposure of harm-onset classification fields such that a minimal profile retains full deduplication capability without chronic-harm tracking obligations; (4) any AI incident management system implementing any combination of the above.

PUBLICATION STATEMENT

Published by ODA3 Pvt Ltd (ODA³ Institute) on Technical Disclosure Commons (tdcommons.org) for prior art purposes. ODA3 Pvt Ltd asserts no patent rights in the methods described above and covenants not to assert any such rights against any party practicing these methods. This covenant is unconditional and is not contingent on certification, licensing, or conformance with any ODA³ Institute scheme. Separately, and without affecting the foregoing, UAIF® v1.0 and the certification scheme built upon it remain governed by the GAISSF Ecosystem Licence (GEL v1.0), under which ODA³ Institute is sole governing authority (GEL §10.5). ODA3 retains copyright in this disclosure. Bihar, India. June 2026. https://oda3.org

Publication date: June 17, 2026

Publisher: ODA3 Pvt Ltd (ODA³ Institute)

Jurisdiction of publication: India

Companion filing: Technical Disclosure Commons (tdcommons.org), Defensive Publications Series

Document reference: ODA3 IP Filing Doc #10