Rademics Research Institute

Abstract

Cyber deception strategies, powered by artificial intelligence (AI), have emerged as a critical tool in defending against increasingly sophisticated cyber threats. This chapter explores the integration of AI-driven honeypots and generative models for enhancing cybersecurity defenses, with a particular focus on mitigating cloud supply chain attacks. Cloud environments, characterized by their complexity and interconnected nature, have become prime targets for cybercriminals seeking to exploit vulnerabilities in trusted vendor relationships. AI-powered honeypots simulate realistic decoy systems within cloud infrastructures, luring attackers into engaging with fake environments, thus providing valuable intelligence on attacker tactics, techniques, and procedures (TTPs). The use of generative models, such as Generative Adversarial Networks (GANs), further strengthens deception by generating dynamic and convincing cloud service interactions, complicating attackers’ efforts to identify legitimate systems. By continuously adapting to attacker behavior, AI-driven deception frameworks can offer real-time detection, analysis, and mitigation of supply chain compromises. The chapter also highlights the role of AI in enhancing the capabilities of traditional cybersecurity tools, such as intrusion detection systems (IDS) and Security Information and Event Management (SIEM) platforms, by providing more accurate and timely threat intelligence. As cloud-based services continue to grow, the application of AI-driven honeypots and generative models is poised to become a fundamental aspect of proactive, scalable, and adaptive defense mechanisms against cloud supply chain attacks.

Introduction

The rapid adoption of cloud computing has transformed the way businesses operate, providing enhanced flexibility, scalability, and cost-efficiency. Cloud environments, however, are inherently complex, comprising interconnected systems, third-party vendors, and distributed resources. While this interconnectedness provides significant operational benefits, it also presents a substantial attack surface for cybercriminals. Cloud supply chain attacks, in particular, have emerged as a critical concern for organizations, as they exploit vulnerabilities in the relationships between service providers, software vendors, and cloud-based infrastructure. These attacks often bypass traditional security defenses by targeting trusted partners and leveraging indirect paths to infiltrate systems. Detecting and mitigating such attacks is a complex task, as they often go unnoticed for extended periods due to the nature of the cloud’s shared infrastructure. As organizations continue to integrate cloud technologies into their operations, the need for advanced, proactive security measures becomes more pressing.

AI-driven deception strategies, which employ advanced techniques such as honeypots and generative models, offer a promising solution to this growing threat landscape. Honeypots, as decoy systems, lure attackers into interacting with fake environments, providing an opportunity to monitor and analyze their behavior. AI-powered honeypots take this concept a step further by leveraging machine learning and adaptive algorithms to create dynamic, intelligent decoys that evolve in real-time based on attacker interactions. By simulating a wide range of cloud service environments, these honeypots can mimic legitimate systems, making it more difficult for attackers to distinguish between real and fake assets. This approach not only helps identify potential vulnerabilities but also aids in understanding the tactics, techniques, and procedures (TTPs) employed by attackers during supply chain attacks.