%% You should probably cite draft-sharif-agent-audit-trail-06 instead of this revision.
@techreport{sharif-agent-audit-trail-01,
    number =    {draft-sharif-agent-audit-trail-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-sharif-agent-audit-trail/01/},
    author =    {Raza Sharif},
    title =     {{Agent Audit Trail: A Standard Logging Format for Autonomous AI Systems}},
    pagetotal = 38,
    year =      ,
    month =     ,
    day =       ,
    abstract =  {This document specifies a standard logging format for autonomous AI agent systems. The Agent Audit Trail (AAT) defines a JSON-based record structure with mandatory fields for agent identity, action classification, outcome tracking, and trust level reporting. Records are linked via tamper-evident hash chaining using SHA-256 per RFC 8785, with optional ECDSA signatures for non-repudiation. The format addresses requirements from the EU AI Act (Regulation 2024/1689), which mandates automatic recording of events for high-risk AI systems effective August 2026. It also maps to SOC 2 Trust Services Criteria, ISO/IEC 42001, ISO/IEC 24970, prEN 18229-1, and PCI DSS v4.0.1 logging requirements. The design is transport-agnostic and supports export to JSONL, Syslog (RFC 5424), and CSV while preserving chain integrity. Privacy is addressed through input/output hashing, content fingerprinting, and tombstone-based deletion compatible with GDPR Article 17. This revision (-01) adds pre-execution recording requirements, recording independence, deny reason codes, replay protection, external timestamp anchoring, and content fingerprinting based on feedback from independent implementers.},
}