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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singh, Mukesh Kumar, "Inference-layer decision trace logging and coordinated multi-component rollback for AI Incident Response", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10509