The Challenges of Leveraging Threat Intelligence to Stop Data Breaches

Abstract

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Despite the significant increase in cybersecurity solutions investment, organizations are still plagued by security breaches, especially data breaches. As more organizations experience crippling security breaches, the wave of compromised data is growing significantly. The financial consequences of a data breach are set on the rise, but the cost goes beyond potential fines. Data breaches could have a catastrophic impact not only in loss of company's reputation and stock price, but also in economic terms. Threat Intelligence has been recently introduced to enable greater visibility of cyber threats, in order to better protect organizations' digital assets and prevent data breaches. Threat intelligence is the practice of integrating and analyzing disjointed cyber data to extract evidence-based insights regarding an organization's unique threat landscape. This helps explain who the adversary is, how and why they are comprising the organization's digital assets, what consequences could happen following the attack, what assets actually could be compromised, and how to detect or respond to the threat. Every organization is different and threat intelligence frameworks are custom-tailored to the business process itself and the organization's risks, as there is no “one-size-fits-all” in cyber. In this paper, we review the problem of data breaches and discuss the challenges of implementing threat intelligence that scales in today's complex threat landscape and digital infrastructure. This is followed by an illustration of how the future of effective threat intelligence is closely linked to efficiently applying Artificial Intelligence and Machine Learning approaches, and we conclude by outlining future research directions in this area.

Keywords

• data breaches
• threat intelligence
• data intelligence
• machine learning
• cybersecurity
• artificial intelligence