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Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4

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arxiv 2309.16422 v1 pith:Y5UOHO6N submitted 2023-09-28 cs.CR

classification cs.CR
keywords cybercybersecuritysentinelsystemthreatscommunicationdialoguegpt-4
verification ladder T0 review T1 audit T2 compute T3 formal
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In an era where cyberspace is both a battleground and a backbone of modern society, the urgency of safeguarding digital assets against ever-evolving threats is paramount. This paper introduces Cyber Sentinel, an innovative task-oriented cybersecurity dialogue system that is effectively capable of managing two core functions: explaining potential cyber threats within an organization to the user, and taking proactive/reactive security actions when instructed by the user. Cyber Sentinel embodies the fusion of artificial intelligence, cybersecurity domain expertise, and real-time data analysis to combat the multifaceted challenges posed by cyber adversaries. This article delves into the process of creating such a system and how it can interact with other components typically found in cybersecurity organizations. Our work is a novel approach to task-oriented dialogue systems, leveraging the power of chaining GPT-4 models combined with prompt engineering across all sub-tasks. We also highlight its pivotal role in enhancing cybersecurity communication and interaction, concluding that not only does this framework enhance the system's transparency (Explainable AI) but also streamlines the decision-making process and responding to threats (Actionable AI), therefore marking a significant advancement in the realm of cybersecurity communication.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations

    cs.CR 2025-06 reject novelty 4.0 of 10

    The FAA framework automates credit card fraud investigations with GPT-4o agents and reports 98-99% fraud-detection F1, though the evaluation is weakened by self-referential LLM scoring.

  2. Generative AI for Internet of Things Security: Challenges and Opportunities

    cs.CR 2025-02 conditional novelty 4.0 of 10

    A survey that catalogs 33 GenAI-for-IoT-security works through the MITRE ICS mitigations lens, with three small case studies on adapting LLMs to IoT incident response and security question answering.

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