I am an Information Technology executive, academic researcher, and research practitioner with over a decade of international experience spanning cloud computing, advanced cybersecurity, and enterprise digital transformation. Having successfully defended my doctoral dissertation, I hold a Doctor of Science (D.Sc.) in Civil Security, Leadership, Management, and Policy from New Jersey City University (NJCU). My work bridges the gap between empirical cybersecurity scholarship and live corporate infrastructure, focusing on the future of secure human-AI collaboration.

Professional Leadership & Applied Infrastructure Security

Currently, I serve as the Director of Cloud Solutions Engineering at S&P Global Inc., where I lead enterprise cloud engineering, governance architectures, and the Shared Services Product Engineering function. In this role, I direct the secure design, hardening, and deployment of core enterprise cloud images and binaries across the global organization. This executive oversight directly advances our "shift-left" security practices—actively hardening infrastructure, enabling secure modern development pipelines, and safeguarding critical global data assets and intellectual property.

Doctoral Research & AI Security Expertise

My scholarly agenda focuses on the critical intersection of Artificial Intelligence (AI), AI Security, Social Engineering, and Human Factors. My doctoral research established a comparative empirical benchmark for utilizing out-of-the-box, open-access Large Language Models (LLMs) as localized, end-user security decision-support tools for phishing detection.

Grounded in Signal Detection Theory and human-computer interaction frameworks, my research evaluated leading models (Anthropic Claude, Google Gemini, and OpenAI GPT) to uncover critical insights for the security industry:

• The Sensitivity-Precision Trade-off: I exposed a major operational asymmetry between hypervigilant threat mitigation and day-to-day consumer utility. While certain models excel at catching malicious intent, they often do so at the cost of elevated false-positive rates that disrupt standard communication.

• The Trust-Verify Paradox: Through thematic analysis of user interaction, I identified that end-user trust and technology acceptance are strictly gated by model explainability. Trained digital citizens actively reject "black-box" autonomous security recommendations, requiring transparent, evidence-based reasoning before shifting their final decision-making boundaries.

Emerging Research & Professional Agenda

As an active research practitioner, I am translating these empirical findings into next-generation defensive systems. My post-doctoral focus includes engineering hybrid, secure-by-design AI architectures that leverage Retrieval-Augmented Generation (RAG) and specialized fine-tuning to mitigate model hallucinations and lower false-alarm overhead. Furthermore, I investigate adversarial AI vulnerabilities to ensure long-term model robustness against adaptive, automated zero-day social engineering vectors.

Personal Resilience

To balance the cognitive demands of enterprise engineering leadership and advanced scientific inquiry, I maintain a committed personal well-being practice. As an avid long-distance runner and dedicated yoga practitioner, I rely on these physical disciplines to sustain the mental clarity, endurance, and situational awareness necessary to innovate within the rapidly evolving landscapes of artificial intelligence and cybersecurity.