Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres

Author:

Baruwal Chhetri Mohan1ORCID,Tariq Shahroz2ORCID,Singh Ronal3ORCID,Jalalvand Fatemeh3ORCID,Paris Cecile2ORCID,Nepal Surya2ORCID

Affiliation:

1. CSIRO's Data61, Melbourne, Australia

2. CSIRO's Data61, Sydney Australia

3. CSIRO's Data61, Melbourne Australia

Abstract

Security Operations Centres (SOCs) play a pivotal role in defending organisations against evolving cyber threats. They function as central hubs for detecting, analysing, and responding promptly to cyber incidents with the primary objective of ensuring the confidentiality, integrity, and availability of digital assets. However, they struggle against the growing problem of alert fatigue, where the sheer volume of alerts overwhelms SOC analysts and raises the risk of overlooking critical threats. In recent times, there has been a growing call for human-AI teaming, wherein humans and AI collaborate with each other, leveraging their complementary strengths and compensating for their weaknesses. The rapid advances in AI and the growing integration of AI-enabled tools and technologies within SOCs give rise to a compelling argument for the implementation of human-AI teaming within the SOC environment. Therefore, in this article, we present our vision for human-AI teaming to address the problem of alert fatigue in the SOC. We propose the 𝒜 2 𝒞 Framework, which enables flexible and dynamic decision making by allowing seamless transitions between automated, augmented, and collaborative modes of operation. Our framework allows AI-powered automation for routine alerts, AI-driven augmentation for expedited expert decision making, and collaborative exploration for tackling complex, novel threats. By implementing and operationalising 𝒜 2 𝒞, SOCs can significantly reduce alert fatigue while empowering analysts to efficiently and effectively respond to security incidents.

Funder

CSIRO’s Collaborative Intelligence (CINTEL) Future Science Platform

Publisher

Association for Computing Machinery (ACM)

Reference68 articles.

1. A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence

2. Bushra A. Alahmadi, Louise Axon, and Ivan Martinovic. 2022. 99% false positives: A qualitative study of SOC analysts’ perspectives on security alarms. In Proceedings of the 31st USENIX Security Symposium (USENIX Security’22). 2783–2800.

3. The role of shared mental models in human-AI teams: a theoretical review

4. Endpoint Detection & Response: A Malware Identification Solution

5. A survey of inverse reinforcement learning: Challenges, methods and progress

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3