AI-native asset intelligence framework converts heterogeneous security signals into normalized asset importance scores by separating intrinsic exposure from contextual factors using modeling and deterministic aggregation.
Cortex: Collaborative llm agents for high-stakes alert triage.CoRR, abs/2510.00311
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4roles
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support 1representative citing papers
Security practitioners use LLMs independently for low-risk productivity tasks while showing interest in enterprise platforms, but reliability, verification needs, and security risks limit broader autonomy.
CyberAId is a proposed on-premise multi-agent system that coordinates LLM subagents with classical security tools to improve threat response and regulatory alignment in financial services.
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.
citing papers explorer
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AI Native Asset Intelligence
AI-native asset intelligence framework converts heterogeneous security signals into normalized asset importance scores by separating intrinsic exposure from contextual factors using modeling and deterministic aggregation.
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Security practitioners use LLMs independently for low-risk productivity tasks while showing interest in enterprise platforms, but reliability, verification needs, and security risks limit broader autonomy.
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CyberAId: AI-Driven Cybersecurity for Financial Service Providers
CyberAId is a proposed on-premise multi-agent system that coordinates LLM subagents with classical security tools to improve threat response and regulatory alignment in financial services.
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AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.