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In today's globally networked environment, information security incidents can inflict staggering financial losses on organizations. Industry reports indicate that fundamental problems exist with the application of current linear plan-driven…
Artificial intelligence (AI) copilots are increasingly integrated into enterprise cybersecurity platforms to assist analysts in threat detection, triage, and remediation. However, the effectiveness of these systems depends not only on the…
Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is important for human safety and economic benefit. Progress has been…
AI-driven penetration testing now executes thousands of actions per hour but still lacks the strategic intuition humans apply in competitive security. To build cybersecurity superintelligence --Cybersecurity AI exceeding best human…
Over the years, the technological landscape has evolved, reshaping the security posture of organisations and increasing their exposure to cybersecurity threats, many originating from within. Insider threats remain a major challenge,…
Multi-task learning (MTL) has emerged as a successful strategy in industrial-scale recommender systems, offering significant advantages such as capturing diverse users' interests and accurately detecting different behaviors like ``click" or…
Large Language Models (LLMs) presents significant priority in text understanding and generation. However, LLMs suffer from the risk of generating harmful contents especially while being employed to applications. There are several black-box…
Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not…
In the constantly evolving field of cybersecurity, it is imperative for analysts to stay abreast of the latest attack trends and pertinent information that aids in the investigation and attribution of cyber-attacks. In this work, we…
During the design of safety-critical systems, safety and security engineers make use of architecture patterns, such as Watchdog and Firewall, to address identified failures and threats. Often, however, the deployment of safety patterns has…
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the…
Large Language Models (LLMs) have the potential to significantly enhance threat intelligence by automating the collection, preprocessing, and analysis of threat data. However, the usability of these tools is critical to ensure their…
As online service systems continue to grow in terms of complexity and volume, how service incidents are managed will significantly impact company revenue and user trust. Due to the cascading effect, cloud failures often come with an…
We introduce a method for Intrusion Detection based on the classification, understanding and prediction of behavioural deviance and potential threats, issuing recommendations, and acting to address eminent issues. Our work seeks a practical…
Ensuring the reliability and availability of complex networked services demands effective root cause analysis (RCA) across cloud environments, data centers, and on-premises networks. Traditional RCA methods, which involve manual inspection…
Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development…
Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into Artificial Intelligence (AI) remains challenging. Previous approaches, such as rule-based systems, face significant…
Stealing attacks pose a persistent threat to the intellectual property of deployed machine-learning systems. Retrieval-augmented generation (RAG) intensifies this risk by extending the attack surface beyond model weights to knowledge base…
In long-horizon open-world multi-agent systems, existing methods often treat local anomalies as automatic triggers for communication. This default design introduces coordination noise, interrupts local execution, and overuses public…
Effective incident management in large-scale IT systems relies on troubleshooting guides (TSGs), but their manual execution is slow and error-prone. While recent advances in LLMs offer promise for automating incident management tasks,…