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Stablecoins have turned out to be the "killer" use case of the growing digital asset space. However, risk management frameworks, including regulatory ones, have been largely absent. In this paper, we address the critical question of…
Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management (IRM)…
Enterprise software engineering is shifting away from deterministic CRUD/REST architectures toward AI-native systems where large language models act as cognitive orchestrators. This transition introduces a critical security tension:…
Authentication in financial systems remains a uniquely high-stakes security challenge, where even marginal increases in false acceptance can result in catastrophic monetary loss. Existing deployments of adaptive authentication, which…
Federated Recommendation Systems (FRS) enable privacy-preserving model training by keeping user data on edge devices. However, the practical deployment of FRS in Edge-Cloud environments faces significant challenges due to system and…
Frontier artificial intelligence (AI) systems present both benefits and risks to society. Safety cases - structured arguments supported by evidence - are one way to help ensure the safe development and deployment of these systems. Yet the…
GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services…
This paper introduces the Agentic AI Governance Assurance & Trust Engine (AAGATE), a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in…
Federated learning (FL) offers an innovative paradigm for collaborative model training across decentralized devices, such as smartphones, balancing enhanced predictive performance with the protection of user privacy in sensitive areas like…
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulations, cloud-based FMs cannot directly access private edge data,…
For one to guarantee higher-quality software development processes, risk management is essential. Furthermore, risks are those that could negatively impact an organization's operations or a project's progress. The appropriate prioritisation…
Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence…
The increasing use of autonomous and semi-autonomous agents in society has made it crucial to validate their safety. However, the complex scenarios in which they are used may make formal verification impossible. To address this challenge,…
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient control design in presence of adversarial cyber-events, and…
Recent advances in artificial intelligence and machine learning may soon yield paradigm-shifting benefits for aerospace systems. However, complexity and possible continued on-line learning makes neural network control systems (NNCS)…
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under-estimated by finite-sampling approximations of…
The last years have seen an increase in Man-at-the-End (MATE) attacks against software applications, both in number and severity. However, software protection, which aims at mitigating MATE attacks, is dominated by fuzzy concepts and…
In this work, we propose a novel safe and scalable decentralized solution for multi-agent control in the presence of stochastic disturbances. Safety is mathematically encoded using stochastic control barrier functions and safe controls are…
Intensified netload uncertainty and variability led to the concept of a new market product, flexible ramping product (FRP). The main goal of FRP is to enhance the generation dispatch flexibility inside real-time (RT) markets to mitigate…
We study the multi-agent safe control problem where agents should avoid collisions to static obstacles and collisions with each other while reaching their goals. Our core idea is to learn the multi-agent control policy jointly with learning…