@techreport{reilly-sentinel-protocol-01,
    number =    {draft-reilly-sentinel-protocol-01},
    type =      {Internet-Draft},
    institution =   {Internet Engineering Task Force},
    publisher = {Internet Engineering Task Force},
    note =      {Work in Progress},
    url =       {https://datatracker.ietf.org/doc/draft-reilly-sentinel-protocol/01/},
    author =    {Lawrence John Reilly Jr},
    title =     {{Reilly Sentinel Protocol (RSP): Blockchain-Anchored Integrity for AI Datasets, Training, Fine-Tuning, and Inference Provenance}},
    pagetotal = 25,
    year =      2026,
    month =     mar,
    day =       29,
    abstract =  {The Reilly Sentinel Protocol (RSP) specifies an interoperable, multi-layer method for establishing integrity, provenance, and auditability across the artificial intelligence (AI) lifecycle. RSP defines a Sentinel Evidence Package (SEP) that binds payload digests, provenance metadata, signatures, blockchain timestamp proofs, and resolvable identifiers (DOIs). This enables tamper-evident, independently verifiable receipts for datasets, data transformations, training jobs, checkpoints, fine-tuning runs, evaluations, inference outputs, and agentic AI action logs. This revision (-01) expands the protocol with a quantum-resistant triple-hash cross-chain architecture (SHA-256, SHA3-512, BLAKE3), formal cross-chain binding proofs, agentic AI provenance extensions, streaming inference provenance, federated learning provenance, expanded threat model covering post-quantum adversaries, integration with the Reilly EternaMark (REM) Protocol triple-layer permanence system, implementation status updates, and a conformance framework. RSP is transport-agnostic and serializable in JSON and CBOR. It leverages existing IETF and industry building blocks, including COSE/CMS signatures, CBOR (RFC 8949), CDDL (RFC 8610), JSON (RFC 8259), and NTS-secured time (RFC 8915). Anchoring is done via append-only blockchain receipts and identity/lineage is stabilized with a DOI registry. The result is an evidence-grade, quantum-resilient audit trail for regulated and safety-critical AI deployments worldwide.},
}