Omni Quant Bio-Sentinel: Real-Time Human Presence Detection and Facial Liveness Verification for Secure AI-Assisted Financial Systems and Multi-Market Intelligence Ecosystem

AI-assisted financial systems increasingly use language models and automated analysis to process market information and support trading decisions.These systems introduce security concerns beyond conventional login authentication, particularly when an application remains active while the authorized user is no longer present.Omni Quant Bio-Sentinel addresses this problem through a facial liveness verification layer integrated into a multi-market financial intelligence ecosystem.The initial Bio-Sentinel implementation explored continuous eye tracking to monitor user activity.However, spontaneous failures in real-time eye tracking led to a change in the verification approach.The updated mechanism uses an interactive challenge-response process in which the system requests a left or right head turn when a prolonged stationary state triggers verification.The observed response is evaluated to determine whether it matches the requested movement.This reduces dependence on continuous eye tracking, although it does not independently establish the user's identity or guarantee resistance to sophisticated presentation attacks.The wider Omni Quant ecosystem combines a web-based interface, local language-model processing through the documented Ollama and Llama 3.2 stack, a multi-agent financial reasoning workflow, backend validation, database records, and an integrated chatbot.The architecture separates AI-generated financial analysis from deterministic application checks and allows the liveness mechanism to contribute to session-level security policies.This paper presents the system's motivation, related work, architecture, verification methodology, implementation considerations, and evaluation framework.Since controlled measurements for the updated liveness mechanism and the complete system are not established in the available project records, quantitative performance claims are not made.The work provides a basis for evaluating the practical use of interactive facial liveness verification in AI-assisted financial applications.