Generative AI Enabled Actionable Decision Support in Cyber Security Operations for Enterprise Security

In the evolving cyber threat landscape, enterprises employ multiple security solutions such as Endpoint Detection and Response (EDR), Security Information and Event Management (SIEM), and Security Orchestration, Automation, and Response (SOAR). Security analysts are inundated with millions of security event logs from such security tools that makes it increasingly complex to manage and analyze these huge data effectively. Further, there is unavailability of dedicated as well as skilled manpower who can understand and analyse such security events. This paper proposes a novel approach based on generative AI using the state-of-the-art Mistral-7B language model to generate clear and actionable security response messages from these event logs. We demonstrate that this cutting-edge language model can translate complex logs into human-understandable security insights which can enhance analysts’ ability to prioritize and respond to threats.

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Publication Details

Published
2024-10-21
DOI
https://doi.org/10.23919/ituk62727.2024.10772892
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
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article

Generative AI Enabled Actionable Decision Support in Cyber Security Operations for Enterprise Security

Sharma Sandeep, Dr Munish Kumar, Basu Saurabh, Singh Utkrisht et al.
Network Security and Intrusion Detection
article

Generative AI Enabled Actionable Decision Support in Cyber Security Operations for Enterprise Security

Sharma Sandeep, Dr Munish Kumar, Basu Saurabh, Singh Utkrisht, Upadhyay Rajkumar
article en

Abstract

In the evolving cyber threat landscape, enterprises employ multiple security solutions such as Endpoint Detection and Response (EDR), Security Information and Event Management (SIEM), and Security Orchestration, Automation, and Response (SOAR). Security analysts are inundated with millions of security event logs from such security tools that makes it increasingly complex to manage and analyze these huge data effectively. Further, there is unavailability of dedicated as well as skilled manpower who can understand and analyse such security events. This paper proposes a novel approach based on generative AI using the state-of-the-art Mistral-7B language model to generate clear and actionable security response messages from these event logs. We demonstrate that this cutting-edge language model can translate complex logs into human-understandable security insights which can enhance analysts’ ability to prioritize and respond to threats.

Centre for Development of Telematics (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 74%
Network Security and Intrusion Detection
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