TR-2026-06: Hash-Chained Decision Provenance, Purpose-Bound Data Passports, and Reversibility-Gated Autonomous Cyber Response

Conventional AI observability pipelines monitor output distributions while remaining blind to silent reasoning drift, unauthorized downstream data repurposing, and irreversible automated remediation errors. Synthesizing the eight-paper foundational research series of Durga (2025, DOI: 10.5281/zenodo.23723284; IJSRMT Vol. 4, Issues 5-12, DOIs: 10.38124/ijsrmt.v4i5.1043 through 10.38124/ijsrmt.v4i12.1050) and the empirical control-plane benchmarks of Durga (2026, DOIs: 10.5281/zenodo.23735274, 10.5281/zenodo.23735497), this report specifies three coupled governance mechanisms: (1) Merkle-aggregated decision-provenance capsules that detect policy-reasoning drift via rejected-alternative distribution monitoring; (2) cryptographic purpose-bound data passports that enforce residency, retention, and classification invariants across RAG and feature-engineering pipelines; and (3) a reversibility-gated cyber-defense execution engine that conditions autonomous containment on verified inverse actions.