OPTIMIZING DATA STRATEGY FOR AUTOMATED MITIGATION RESPONSE SECURITY - RANSOMWARE CASE STUDY

Abstract

As organizations increasingly leverage data strategy for enhancing cyber-security through Automated Mitigation Response (AMR) systems, particularly in the context of fighting sophisticated threats like ransomware, the challenge of false positives emerges as a critical concern. This research paper provides a thorough examination of this phenomenon, exploring its implications on operations, resource allocation, and trust in automation. We present an in-depth analysis of the factors contributing to false positives within the context of digital twins implementing data strategies and discuss their potential impact on security postures. Furthermore, the paper offers insights into the complexities associated with managing false positives, highlighting the need for effective validation mechanisms. To address this challenge, we propose mitigation strategies, including the refinement of detection algorithms, continuous fine-tuning, and the integration of adaptive response measures. Our findings contribute to a deeper understanding of the dynamics between Automated Mitigation Response systems and data strategy, offering practical recommendations for organizations seeking to optimize their cyber-security frameworks while minimizing the risks associated with false positives. This research will be evaluated in the frame of ransomware attack detection and mitigation.