| _version_ | 1866902099082608640 |
|---|---|
| author | Annamalai Sekar, Mythili Saha, Sumit Carimireddy, Shiva |
| author_facet | Annamalai Sekar, Mythili Saha, Sumit Carimireddy, Shiva |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20390972 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reflexive Synthetic Data Governance for Enterprise AI Agents Annamalai Sekar, Mythili Saha, Sumit Carimireddy, Shiva AI Agents <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> |
| title | Reflexive Synthetic Data Governance for Enterprise AI Agents |
| topic | AI Agents |
| url | https://doi.org/10.5281/zenodo.20390972 |