I’m Niher Halder, a BUET graduate civil engineer with over 15 years of experience in safety-critical infrastructure, including roads, bridges, MRT, power plants, and industrial projects. My work has consistently involved environments where safety, reliability, and operational discipline are essential.
I am currently transitioning my focus toward OT/ICS cybersecurity, with particular interest in how network security and AI can be applied responsibly within gas and power systems. This transition is deliberate and structured, grounded in fundamentals rather than rapid specialization.
I’m building a strong foundation in cybersecurity for industrial systems, with a focused interest in how automation and AI can support anomaly detection and threat modeling in ICS/OT environments. My learning is oriented toward power and energy, where security must work alongside safety, uptime, and operational continuity. I’m especially drawn to approaches that stay process-aware and explainable under real constraints.
This website is a structured space where I share my ongoing transition — what I’m studying, what I’m building, and the way I think about security in industrial contexts. Over time it will grow through field perspective, focused writing, and small, carefully documented projects, reflecting steady progress rather than a finished destination.
Industrial systems operate under constraints very different from traditional IT environments. In OT/ICS, security decisions must respect operations — excessive alerts, disruptive controls, or poorly understood changes can introduce real operational risk. My background in safety-critical execution naturally aligns with this way of thinking.
In industrial environments, availability is a core requirement. Security mechanisms must be predictable, controlled, and compatible with existing operational practices.
In gas and power systems, cyber events can translate into physical consequences. Understanding processes, assets, and operational states is essential to meaningful risk management.
AI can support security teams only when its outputs are understandable and actionable. I am particularly interested in approaches that help explain anomalies in operational terms rather than abstract model scores.
My current work focuses on building strong fundamentals first, then applying them through realistic exercises, writing, and small projects.
Understanding industrial architectures, segmentation concepts, visibility challenges, and how OT environments differ from enterprise IT in both design and operation.
Developing solid network fundamentals for defensive monitoring, including traffic behavior, baselining, and interpreting deviations in context.
Exploring techniques for working with noisy, time-dependent data and learning how to evaluate anomalies in ways that remain meaningful to operators.
My work is guided by a long-term view of how cybersecurity can responsibly support power and gas infrastructure as these systems become more connected and automated. Through gradual learning and practical engagement — both in field environments and technical work — I aim to deepen my understanding of how AI, machine learning, networking, security, and automation intersect in industrial systems. I am particularly interested in approaches that strengthen visibility and resilience without compromising safety or operational stability. As my experience grows, my focus will remain on contributing in ways that respect the realities of critical infrastructure: reliability first, clarity over complexity, and security that supports operations rather than disrupts them.