By Bhardwaj Naik, Chief Executive Officer, Videonetics
India’s relationship with video surveillance has historically been defined more by scale than sophistication. Cities, campuses, corporate parks, and public institutions have expanded their camera networks at an unprecedented pace and invested heavily in extensive CCTV infrastructure. Despite this growing density of surveillance, much of the infrastructure continues to serve a primarily recording function rather than enabling real-time intelligence.
Many organisations continue to rely on the traditional Network Video Recorders to capture and store footage, with teams of personnel monitoring multiple screens around the clock from control rooms. Even larger organisations that have adopted Video Management Systems often use them primarily to manage and retrieve footage during an incident, with limited intelligence being derived from the vast volumes of video data generated. The objective, therefore, must be to move beyond this reactive approach of reviewing footage after an event has occurred towards a proactive approach that enables real-time insights, early detection of potential risks, and timely intervention.
This distinction is more consequential than it may initially appear. At its core, conventional CCTV infrastructure is designed to record and store events while Video Management Systems (VMS) manage and monitor the broader surveillance apparatus. A VMS brings multiple camera feeds into a centralized platform, enables operators to view and manage live and recorded footage, monitors camera and system health, manages access and permissions, and makes it easier to search, retrieve, and review footage when required.
The next evolution is not simply about deploying more cameras but making existing infrastructure significantly more intelligent. AI adds an analytical layer that detects, classifies, and interprets activities as they unfold. More recently, Vision Language Models or VLMs are bringing generative AI capabilities into video surveillance, enabling systems to understand video in context and respond to more complex visual queries. This shifts surveillance from asking ‘what happened’ to understanding ‘what is happening’ in real time, creating opportunities for faster and more informed intervention.
India has already taken long strides within this realm. AI-enabled video analytics is already being deployed at meaningful scale across Indian cities, transport networks, airports, critical infrastructure, large scale manufacturing, enterprises, and public infrastructure, with applications spanning transport networks, government facilities, and increasingly, educational institutions. However, its significance extends well beyond security and surveillance.
As organisations integrate AI into their video infrastructure, cameras are increasingly becoming a source of business and operational intelligence in helping organisations understand how spaces are being utilized, identify operational bottlenecks, monitor processes, improve resource allocation, enhance service delivery, make faster data-driven decisions and ensure compliance of statutory requirements. The challenge to wider adoption is therefore no longer one of technical feasibility, but of institutional readiness: how quickly organisations are prepared to move beyond simply deploying cameras to unlock the operational and business value embedded within their existing video infrastructure.
Privacy considerations are equally central to the responsible deployment of AI-enabled video analytics. The right to privacy has been recognized as a fundamental right under Article 21, while the Digital Personal Data Protection Act, 2023 establishes a framework for the responsible handling of personal data, including requirements around purpose, consent, and data governance.
These considerations have assumed greater urgency amid recent public concerns over the use of surveillance technologies, particularly around collection, retention, access, and potential misuse of personal data. As video analytics becomes more pervasive, organizations must therefore ensure that innovation is accompanied by clear governance frameworks, transparency, appropriate safeguards, and accountability, so that the benefits of intelligent surveillance do not come at the expense of individual privacy and public trust.
The future of video surveillance lies in moving beyond fragmented systems towards a unified platform that brings together everything required for modern VMS i.e. from centralized camera and device management, real-time monitoring, system health, intelligent video analytics, search, alerts, reporting, and enabling efficient operational and business decision making with actionable insights.
By consolidating these capabilities within a single ecosystem and an AI-driven unified platform, organizations can simplify operations, improve efficiency, optimize cost, reduce the complexity of managing disparate technologies, and make better use of their existing surveillance infrastructure. As
India’s surveillance landscape continues to evolve, the real opportunity will be in creating smarter, more integrated systems that can turn the vast volumes of video being generated every day into timely, meaningful, and actionable intelligence.