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Integrating Security Information and Event Management (SIEM) with Data Lakes and AI: Enhancing Threat Detection and Response

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  • Rahul Marri
  • Sriram Varanasi
  • Satwik Varma Kalidindi Chaitanya

Abstract

The evolving threat landscape in cybersecurity necessitates more advanced and efficient solutions for threat detection and response. Traditional Security Information and Event Management (SIEM) systems have limitations in handling large volumes of data and identifying sophisticated threats. This research explores the integration of SIEM solutions with data lakes, offering a scalable and flexible approach to managing security data. By leveraging artificial intelligence (AI) and machine learning (ML) algorithms, SIEM platforms can enhance their capabilities in real-time threat detection, automated response, and advanced analytics. This integration enables organizations to process vast amounts of structured and unstructured data from various sources, improving both the speed and accuracy of identifying security threats. The article examines the architecture, benefits, and challenges of combining SIEM with data lakes and AI, providing insights into how these technologies can collectively strengthen organizational security postures.

Suggested Citation

  • Rahul Marri & Sriram Varanasi & Satwik Varma Kalidindi Chaitanya, 2024. "Integrating Security Information and Event Management (SIEM) with Data Lakes and AI: Enhancing Threat Detection and Response," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 151-165.
    • Handle: RePEc:das:njaigs:v:6:y:2024:i:1:p:151-165:id:239
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    References listed on IDEAS

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    1. Ankit Sharma, 2024. "Bridging Paradigms: The Integration of Symbolic and Connectionist AI in LLM-Driven Autonomous Agents," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 138-150.
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