AI & ML interests
Our organization, Convergent Intelligence, is dedicated to advancing the application of artificial intelligence and novel mathematical frameworks to address complex financial threats. We bridge the gap between theoretical research and practical, high-impact security controls, with a specific focus on the fintech sector. Our primary interests and research pillars include: * Discrepancy Calculus & Anomaly Detection: A significant portion of our work revolves around a proprietary mathematical framework called Discrepancy Calculus. This involves using Gap-Metric Risk (\Delta_g) to quantify the deviation between observed and expected signal distributions and forecasting anomaly energy (\Delta\epsilon_f) to indicate the magnitude of potential risk events. We are interested in models that can identify subtle, multi-step abuse chains that traditional tools often miss. * Adversarial Behavior & Path Modeling: We focus on modeling adversary behavior rather than just code flaws. Our research in Resonance Path Modeling (\psi) aims to identify the "lowest-energy routes" or most likely attack paths through a combination of human and digital systems. This informs our interest in AI that can understand and predict complex, multi-stage attack scenarios. * Adaptive Systems & Probing: We develop and apply Phase-Locked Probes (T), which are precisely-timed tests used to validate or falsify security assumptions without introducing production risk. This leads to an interest in adaptive systems and models, such as Burst-Aware Thresholds, which dynamically adjust alerting sensitivity based on real-time risk trajectories. * Secure & Ethical AI Implementation: We are deeply committed to the responsible application of AI. Our data use policies strictly prohibit the use of client data for training general-purpose or non-client models without explicit written consent. Any authorized model fine-tuning is performed in a logically and access-wise segregated environment to ensure data privacy and security. Our work also explores defenses against AI/automation risks like prompt/agent abuse and data leakage. The models, tools, and research we may share here will reflect these interests, translating our findings into reference implementations, research notes, and open-source tooling where appropriate.