Inference-Layer Decision Trace Logging and Coordinated Multi-Component Rollback for AI Incident Response

Defensive Publication — Companion record to ODA3 IP Filing Doc #12

Independent record notice. This page is published on oda3.org as an independent, separately dated record of the disclosure below. The same disclosure text has also been submitted to Technical Disclosure Commons (tdcommons.org) as a companion defensive publication, currently pending review. This page will be updated with the TDCommons URL and publication date once posted. Maintaining both records is intended to provide two independent, publicly verifiable timestamps for the same prior art.

Abstract

This disclosure describes two related technical methods for artificial intelligence (AI) incident response. The first method captures a defined set of data fields at each model inference event, including retrieval-augmented generation (RAG) context where applicable, and writes the resulting record to append-only storage with a cryptographic hash chain linking each record to its predecessor, producing a tamper-evident forensic log. The second method defines model rollback as a single atomic operation across five independently versioned components — model weights, inference configuration, downstream service dependency versions, safety policy configuration, and a training data snapshot reference — selected by a single composite identifier and validated by a pre-rollback compatibility check before restoration proceeds. Both methods address forensic and recovery gaps that arise because conventional security logging and conventional software rollback were not designed for the inference-time and multi-artefact characteristics of deployed machine learning systems. This disclosure is published to establish prior art and prevent patent claims on these methods by any party.

Technical Field

This disclosure relates to systems and methods for AI security incident response, specifically (a) cryptographic-hash-based, tamper-evident audit logging for machine learning inference pipelines, including retrieval-augmented generation architectures, and (b) coordinated, multi-component recovery mechanisms for deployed machine learning models and their associated configuration and policy state.

Background

Conventional security information and event management (SIEM) infrastructure captures network traffic, authentication events, and operating-system-level activity. It does not, by default, capture model inference events, and most machine learning deployment frameworks do not generate inference-layer logs without additional instrumentation. This creates a forensic gap: when a deployed AI system produces a harmful, anomalous, or non-compliant output, an incident responder is frequently unable to reconstruct what input, retrieved context, model version, or intermediate decision led to that output.

A related gap exists in recovery. Conventional software rollback mechanisms (for example, container image rollback or blue-green deployment reversion) restore application code or configuration to a prior version. Restoring a deployed AI system to a safe operating state is structurally different, because it depends on the joint state of multiple independently versioned artefacts: the model weights, the inference configuration, the configuration of any downstream services the model depends on or routes output to, the safety policy or content-filtering configuration in effect at the time, and, for re-validation purposes, the training or fine-tuning data snapshot associated with the model weights. Restoring fewer than all of these components together can fail to resolve the original incident or can introduce a new failure mode.

Existing machine learning observability and monitoring tools typically capture aggregate performance metrics rather than per-inference forensic records sufficient for incident reconstruction, and do not apply cryptographic integrity verification to such records. Existing model registries support versioning of model weights but do not, as a defined method, couple that versioning to downstream dependency and safety policy versioning under a single composite identifier with a pre-rollback compatibility check.

Detailed Description

1. Inference-Layer Decision Trace Logging — Minimum Field Set

At each model inference event, a defined set of fields is captured as a single logged record: a timestamp captured at the point of model invocation; a unique inference identifier; a model version identifier that deterministically identifies the exact deployed model artefact; a deployment hash, being a cryptographic hash of the full deployed inference configuration; a prompt hash, covering the system prompt, user input, and, in RAG architectures, all retrieved context passed into the model’s context window; an output hash; a record of any content filtering or guardrail decision applied pre- or post-inference; and, where applicable, a record of any downstream routing decision triggered by the output. Where architecturally accessible, a reference to intermediate reasoning output is additionally captured. Where the deployment uses retrieval-augmented generation, a reference sufficient to retrieve the exact retrieved content used in that specific inference call is captured, distinct from a mere document identifier, since the underlying document store may itself be versioned or mutable.

2. Tamper-Evident Storage via Cryptographic Hash Chaining

Logged records are written to storage that rejects in-place modification or deletion of existing records (write-once-read-many storage, an append-only ledger structure, or an equivalent control). Each record is cryptographically chained to the immediately preceding record using a construction of the general form hash[n] equals a collision-resistant hash function applied to the concatenation of the previous record’s chained hash and the serialized current record, with the first record chaining to a fixed published genesis value. This allows a verifier to detect insertion, deletion, or modification of any record, since altering any record breaks the chain for every subsequent record. A single authoritative append-only log with periodic externally-anchored checkpoint publication is sufficient; a distributed ledger is not required.

3. Data Minimization for Personal Data in Logged Fields

Where logged fields contain personal data, the method incorporates pseudonymization of direct user identifiers at the point of logging, with any re-identification key held under separate access control, and differential retention periods for personal-data-bearing fields relative to the integrity-chained technical fields, such that personal data may be purged on a shorter cycle without breaking the hash chain, by substituting a published tombstone value without altering the chained hash contribution of that record.

4. Coordinated Multi-Component Model Rollback — Component Set

A rollback operation is defined as the joint, atomic restoration of five independently versioned components: model weights; inference configuration; downstream service dependency versions, pinned to versions compatible with the restored model weights; safety policy configuration; and a training data snapshot reference, retained for re-validation purposes. All five components are versioned under a single composite identifier at deployment time, such that a rollback target is selected by specifying one composite identifier rather than five independently resolved component versions.

5. Atomicity and Pre-Rollback Compatibility Check

The rollback operation executes as an atomic transition: either all five components reach the target version, or the system remains at, or returns to, its pre-rollback state. A partial restoration is treated as a failure state requiring automated halt and operator alert, not an accepted degraded outcome. Before executing the transition, the method performs a compatibility check across the five target component versions, verifying that the specific combination was previously deployed together, or, where it has not, flagging the operation for manual approval. Because the five components are pre-identified and pinned to compatible versions at deployment time rather than resolved at incident time, the method supports execution within a defined recovery time objective without requiring incident-time compatibility analysis.

6. Illustrative Example

A deployed RAG-based system produces incorrect output traced to a stale retrieval-index document. The responder retrieves the logged record for the relevant inference identifier; the retrieved-context reference resolves to the exact content used at inference time, confirming the stale source. Because the log chain is verifiably intact, the record’s integrity since the original event is confirmed. The responder then initiates rollback to the most recent composite deployment state pre-dating the index update, restoring all five components atomically following confirmation by the pre-rollback compatibility check.

Prior Art Coverage

This disclosure covers: (1) inference-layer decision trace logging using the field set described above, including mandatory RAG-context capture, written to append-only storage with cryptographic hash chaining for tamper evidence; (2) data-minimization handling of personal data within a hash-chained log via tombstone substitution that preserves chain integrity; (3) coordinated multi-component model rollback in which model weights, inference configuration, downstream dependency versions, safety policy configuration, and a training data snapshot reference are versioned under a single composite identifier and restored as a single atomic operation; (4) a pre-rollback compatibility check that validates a target component combination against previously deployed combinations before restoration proceeds; (5) any AI incident response system implementing any combination of the above.

Patent Non-Assertion Covenant. 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, AI-IRF® 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.

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 — submitted, pending review; URL to be added once posted
Document reference: ODA3 IP Filing Doc #12