Research
Compliance
LLMs for Zero-Shot Threat Detection via Structured Risk Indicators
Key Insights
- We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from heterogeneous security logs.
- The framework models use.
Cite this synthesis
DOI: 10.48550/arXiv.2608.16508 ↗
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@misc{ acaciacompliance-research-llms-for-zero-shot-threat-detection-via-structured-risk-indi,
title = { LLMs for Zero-Shot Threat Detection via Structured Risk Indicators },
author = { Leszek },
year = { 2026 },
doi = { 10.48550/arXiv.2608.16508 },
url = { http://arxiv.org/abs/2608.16508v1 },
note = {Summarized and classified by AcaciaFund}
}
TY - GEN TI - LLMs for Zero-Shot Threat Detection via Structured Risk Indicators AU - Leszek PY - 2026 DO - 10.48550/arXiv.2608.16508 UR - http://arxiv.org/abs/2608.16508v1 ER -
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Overview
We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from heterogeneous security logs. The framework models use
Key Topics
- Risk Management: A theme covered by this item within the AML and compliance context.
Why It Matters
This item adds to the AML and compliance knowledge base. Practitioners can use it to stay current on LLMs for Zero-Shot Threat Detection via Structured Risk Indicators, but should validate its claims against primary sources and more recent work before relying on it in production decisions.
Key Takeaways
- Understand how LLMs for Zero-Shot Threat Detection via Structured Risk Indicators relates to AML and compliance workflows and controls.
- Assess evidence quality and freshness before acting on the findings.
- Use the tagged topics to connect this item to related content in the library.
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