Ten AI Stepping Stones for Cybersecurity

TL;DR

This paper explores ten critical issues at the intersection of AI and cybersecurity, addressing opportunities, challenges, and adversarial dynamics to guide practitioners in effectively leveraging AI for security.

Contribution

It provides a comprehensive discussion of ten key cybersecurity issues related to AI, including privacy, explainability, adversarial threats, and attacker strategies, based on current state-of-the-art knowledge.

Findings

AI can enhance cybersecurity operations but raises privacy concerns.

Explainability of AI decisions is crucial for trust and effectiveness.

Adversaries can exploit AI systems, creating an AI battlefield.

Abstract

With the turmoil in cybersecurity and the mind-blowing advances in AI, it is only natural that cybersecurity practitioners consider further employing learning techniques to help secure their organizations and improve the efficiency of their security operation centers. But with great fears come great opportunities for both the good and the evil, and a myriad of bad deals. This paper discusses ten issues in cybersecurity that hopefully will make it easier for practitioners to ask detailed questions about what they want from an AI system in their cybersecurity operations. We draw on the state of the art to provide factual arguments for a discussion on well-established AI in cybersecurity issues, including the current scope of AI and its application to cybersecurity, the impact of privacy concerns on the cybersecurity data that can be collected and shared externally to the organization, how an AI decision can be explained to the person running the operations center, and the implications of the adversarial nature of cybersecurity in the learning techniques. We then discuss the use of AI by attackers on a level playing field including several issues in an AI battlefield, and an AI perspective on the old cat-and-mouse game including how the adversary may assess your AI power.