I work at the intersection of cybersecurity, data analytics, and artificial intelligence, focusing on the design of resilient, automation-first security frameworks that address continuously evolving attack surfaces. My approach emphasizes defense-in-depth, continuous monitoring, and evidence-driven decision-making, rather than static, snapshot-based security controls.
My experience includes network and system security architecture, vulnerability assessment, incident response, and risk management, guided by established frameworks such as NIST CSF, NIST SP 800-61, ISO/IEC 27001, and MITRE ATT&CK. I design and evaluate security operations workflows that integrate logging, intrusion detection, vulnerability intelligence, and threat correlation to reduce detection latency and analyst fatigue.
I apply AI and machine learning techniques to cybersecurity problems such as attack-surface drift detection, anomaly identification, and prioritization of security findings. By combining time-series analysis, pattern recognition, and contextual correlation, I focus on transforming raw security telemetry into decision-grade intelligence that supports faster and more defensible responses.
Through hands-on lab environments and applied research, I integrate traditional security tools with AI-assisted analytics, ensuring that automated defenses remain interpretable, auditable, and aligned with operational realities. This work supports the development of adaptive security systems capable of anticipating change, minimizing risk exposure, and strengthening organizational resilience in complex cyber environments.