সংরক্ষণ করুন:
| প্রধান লেখক: | , , |
|---|---|
| বিন্যাস: | Recurso digital |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
Zenodo
2026
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.5281/zenodo.20390972 |
| ট্যাগগুলো: |
ট্যাগ যুক্ত করুন
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সূচিপত্রের সারণি:
- <p>Enterprise AI agents need representative records for retrieval testing, policy evaluation, incident rehearsal, risk analysis, and documentation maintenance, but using raw operational data in these workflows can expose sensitive fields and silently reinforce stale policy assumptions. This paper proposes RSDG, a reflexive synthetic data governance architecture for enterprise AI agents. RSDG treats synthetic-data generation, anonymization, retrieval, validation, documentation, release, and downstream agent feedback as one governed lifecycle. A release candidate is accepted only when it satisfies privacy accounting, hybrid evidence support, model-context contract validity, risk constraints, and phrase-level policy-drift checks. The system also observes how released synthetic records affect agent answers and decisions, then renews, constrains, or withdraws datasets whose utility, privacy risk, or narrative influence has changed. In a simulated benchmark spanning compliance evidence, policy question answering, and financial risk review, RSDG improves evidence-supported synthetic-record acceptance from 0.72 to 0.92, reduces simulated disclosure-risk alerts from 8.4% to 1.1%, and cuts unsupported policy-influence events by 63.5% relative to an unbounded synthetic-generation baseline.</p>