Cybersecurity
Meets AI
My MSc research sits at the intersection of two of the most critical fields in modern technology — Cybersecurity and Artificial Intelligence.
My goal is to explore how AI-powered threat detection, anomaly identification, and automated incident response can make infrastructure inherently more secure — not just at the perimeter, but from within every layer of the stack.
Research Direction: Investigating machine learning models for real-time detection of anomalous infrastructure behaviour in cloud environments — building on hands-on experience with Prometheus, Grafana, and AWS CloudWatch to design AI-augmented monitoring systems that reduce mean time to detect (MTTD) and respond (MTTR) to security incidents.
AI-Powered Threat Detection
Exploring ML models that detect unusual infrastructure behaviour patterns before they escalate — moving from reactive to predictive security posture.
DevSecOps — Security by Design
Embedding security into every stage of the CI/CD pipeline as a continuous, automated process that scales with the infrastructure.
AI-Augmented Incident Response
Using AI to automate the first critical minutes of incident response — faster triage, intelligent runbook execution, reduced human error under pressure.