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PII-TRACE: A Benchmark for Context-Aware PII Detection in Multi-Turn LLM Conversations
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduce PII-TRACE, a new benchmark to evaluate context-aware PII detection across multi-turn LLM conversations.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 22 September 2026
- Collected by OneBench
- 23 Sept 2026, 03:01 UK
Stored source excerpt
arXiv:2609.22200v1 Announce Type: new Abstract: LLM assistants and agentic systems log long multi-turn conversations. AI providers often scan these conversations for Personally Identifiable Information (PII)…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Standard static PII masking fails to catch sensitive data exposed iteratively across multi-turn enterprise conversational AI logs.
Do what
Review data-masking controls for conversational AI platforms with the team responsible for enterprise data governance.