Adaptive Asymmetric Cyber Defense
Traditional static defense mechanisms are no longer sufficient to protect critical infrastructure, sensitive data, and digital assets from sophisticated adversaries.
On AI security threats
Traditional static defense mechanisms are no longer sufficient to protect critical infrastructure, sensitive data, and digital assets from sophisticated adversaries.
Recent incidents have highlighted the extensive impact of supply chain attacks, affecting not only traditional software systems but also AI-specific contexts.
While cybersecurity professionals are familiar with traditional threats like data breaches and DDoS attacks, federated learning presents unique dangers, such as model hijacking and neural network trojans.
This essay explores the unique security considerations surrounding generative AI and why I believe this will be the next significant frontier in cybersecurity.
Adversarial Attacks on AI Systems
Threats and Countermeasures in Artificial Intelligence Systems
Navigating the complexities of AI-driven cyber conflict requires a multifaceted approach that encompasses the development of international norms, the promotion of transparency and human oversight, and investments in AI safety research.
This article explores the intersection of AI, cyber wargaming, and cyber ranges, detailing their synergies, benefits, challenges, and future trends.
Concept of Modern Asymmetric Cyber Defense. This innovative approach leverages advanced deception techniques to level the playing field and proactively defend against cutting-edge threats.
These sophisticated, self-directed attacks can adapt and evolve in real-time, circumventing traditional detection and defense mechanisms (Kaloudi & Li, 2020). This article examines these emerging threats, their potential impacts, and the strategies being developed to counter them.