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2025.12.03
The evolution of AI technology, driven by Generative AI, has accelerated at an unprecedented pace. This year, in particular, its impact has been maximized, shifting paradigms across various industries. This spark of innovation is now extending into the video surveillance sector.
Hanwha Vision predicts that 2026 will be a pivotal turning point. We foresee AI moving beyond simple adoption to becoming the essential foundation of the entire industry. Most notably, the emergence of «Autonomous AI Agents» is expected to reshape the very structure and operational methods of video surveillance systems.
Amidst these waves of change, Hanwha Vision highlights five key trends that the industry must focus on.
These trends signal a future where AI serves as the core engine, elevating video surveillance from simple monitoring systems to central pillars of operational efficiency and sustainability.
As AI analysis technology becomes ubiquitous, the principle of «Garbage In, Garbage Out» becomes increasingly critical in video surveillance. Visual noise and distortion caused by challenging environments—such as low light, backlighting, or fog—are primary causes of AI malfunction and false alarms. By 2026, establishing a «Trusted Data Environment» to solve these issues will become the industry’s top priority.
With the performance of AI analysis engines leveling up across the board, the focus of investment is shifting toward securing high-quality video data that AI can interpret without error.
A prime example is the investment in minimizing noise and distortion in extreme environments through AI-based high-performance ISP (Image Signal Processing) technology and the use of larger sensors. AI-based ISP employs deep learning to differentiate between objects and noise, effectively eliminating noise while optimizing object details to provide real-time data most conducive to AI analysis. Larger image sensors capture more light, which fundamentally suppresses video noise generation, starting from low-light conditions.
Hanwha Vision‘s 2nd Generation P series AI Cameras feature a Dual NPU design, the Wisenet 9 chipset with AI-based image enhancement, and a large 1/1.2” sensor, guaranteeing crystal-clear images optimized for AI analysis even in the harshest environments.
In parallel, as the ethical use of AI becomes a major concern, the mandatory adoption of AI governance systems is approaching. Global standards, such as the European Union’s AI Act, classify video surveillance AI used in public safety as High-Risk technology. This imposes a legal obligation on manufacturers to ensure Transparency in AI from the design phase, accelerating the industry’s push to build genuinely trustworthy AI.
Furthermore, Hanwha Vision plans to upgrade its WiseAI app leveraging its capabilities in trusted data acquisition. Specifically, we will add an Auto Calibration feature that determines the distance information of a scene to enhance data reliability, and new AI event features to analyze abnormal behaviors like fighting and falling will be included in the 2026 product releases.
As AI evolves from simple detection to an agent capable of analyzing complex scenarios and proposing initial responses, the role of the monitoring operator is set for a fundamental overhaul. Humans will delegate repetitive surveillance tasks to AI Agents and focus on more critical, high-level functions.
While previous AI systems in video surveillance merely reduced the operator’s workload by automating repetitive tasks like object search, tracking, and alarm generation, the AI Agent takes this a step further. It autonomously conducts complex situational analysis, automatically executes an initial response, and recommends the most effective follow-up actions to the monitoring operator.
For example, an AI Agent can independently assess an intrusion, initiate preliminary steps such as sounding an alarm, and then propose the final decision options (e.g., whether to call the police) to the operator. Simultaneously, it automatically generates a comprehensive report detailing real-time video of the intrusion area, access records, a log of the AI’s initial actions, and suggested optimal response strategies.
Consequently, monitoring operators will transition into the role of commanders, making final decisions that require nuanced judgment, complex analysis, and consideration of legal and contextual implications. They will also take on the role of an AI governance manager, transparently tracking and supervising all autonomous actions and reasoning processes executed by the AI Agent. This essential function, which prevents system misuse, demands a significant elevation of the monitoring operator’s skill set.
The explosive growth of generative AI is accelerating a ‘Technological Energy Crisis’. According to IEA reports, power consumption by data centers is projected to more than double by 2030 due to the rising demand for AI servers.
The video surveillance industry is at a crossroads where it can no longer prioritize performance without limit, facing the dual challenge of surging high-resolution video data and the computational burden of Edge AI. Consequently, Sustainable Security, which prioritizes operational longevity and minimizing environmental costs, is set to become a core competency for achieving TCO (Total Cost of Ownership) reductions and meeting ESG goals.
To realize sustainable security, the industry is commonly moving towards developing ‘low-power AI chipsets’ that drastically reduce power consumption while preserving high-quality imaging and AI processing power. It is also prioritizing technologies that ensure data efficiency directly on the edge device (camera).
For instance, Hanwha Vision‘s AI-based WiseStream technology maximizes video data management efficiency, contributing to lower power consumption. It intelligently separates regions of interest from non-interest within the video and adjusts the compression ratio based on importance. This maximizes traffic efficiency while securely retaining all necessary information. Furthermore, cameras equipped with Wisenet 9 have improved baseline data transmission efficiency by reusing images from static regions.
These intelligent data management strategies simultaneously meet both performance and efficiency demands and are regarded as the most effective means to directly reduce the power consumption required for server expansion and cooling systems.
With AI integrated into cameras and advances in cloud technology for large-scale data processing, the concept of a ‘Sentient Space’—a space that can sense and understand—is becoming a reality.
In this shift, the role of video surveillance expands beyond simple monitoring to become a core data source for Digital Twin technology, which reflects the physical environment in real-time. A Digital Twin is a virtual replica of a real-world physical asset, created in a computer-based virtual environment.
Currently, the AI information (metadata) extracted by AI cameras is already being used as business intelligence to optimize operations in many smart sectors like cities, retail, and factories. Moving forward, this metadata will be fused with diverse information from access control devices, IoT sensors, and environmental sensors to complete a unified, intelligent Digital Twin environment.
This Digital Twin environment revolutionizes the monitoring experience. Instead of complex, fragmented screens, operators gain a holistic view of event relationships on a map-based interface that integrates the VMS (Video Management System) and access control systems. Within this perfectly mirrored digital space, the system evolves into an Autonomous Intelligent Space that deeply understands situations and manages and resolves issues independently, without human intervention.
Adding the latest AI technology provides security managers or operators with complete control over system operations. For example, AI can instantly comprehend natural language questions like, «Find a person who entered the server room after 10 PM last night,» and automatically analyze access and video records to report the results. This signifies true situational awareness that moves far beyond basic complex search parameters.
The skyrocketing costs of transmitting high-definition video data, coupled with regional data sovereignty and regulatory concerns, pose operational limitations for purely cloud-based systems. In this context, Hybrid Architecture, which preserves the benefits of the cloud while mitigating operational strain, is rapidly establishing itself as the optimal solution for the video surveillance sector. By 2026, this hybrid model is expected to be firmly entrenched as the standard security infrastructure for the AI era.
Hybrid architecture grants users ultimate control and flexibility over system operations. Because it allows system functions to be deployed to the most efficient location based on an organization’s business needs, budget, and legal/regulatory environment, it becomes a key strategy for maximizing TCO efficiency.
From a video surveillance standpoint, hybrid architecture maximizes efficiency by flexibly distributing functions between the on-premises and cloud environments. On-premise environments can host real-time monitoring functions and critical functions that must comply with regulations for short-term video storage and retention. Furthermore, functions involving the local processing and control of highly sensitive data are also placed on-premises to bolster data security control and ensure immediate response capabilities at the site.
Conversely, the cloud environment is leveraged for functions such as remote centralized management, large-scale data analysis, deep learning for AI models, and long-term archiving. This utilization of the cloud ensures system scalability and operational ease.
Beyond simple infrastructure separation, this architecture also supports the optimal distributed computing structure necessary for the successful operation of AI-analysis-based video surveillance systems.
In this structure, edge (camera/NVR) devices handle the first layer of computation, performing real-time detection and only selectively transmitting necessary data to the cloud. This reduces network bandwidth strain and maximizes speed. Following this, the cloud (central server) environment conducts the second layer of deep analysis and large-scale machine learning based on the filtered data from the edge, significantly enhancing the accuracy and sophistication of AI functions.
In conclusion, this distributed computing model serves as the critical infrastructure foundation that simultaneously boosts the edge’s immediate responsiveness and the cloud’s advanced analytical capabilities.
A Hanwha Vision representative commented, “2026 is the point when AI will be firmly established as the new standard for security infrastructure,” adding, “We will secure trustworthy data and deliver sustainable security value to the market by providing solutions based on a hybrid architecture optimized for AI analysis and processing.”
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Hanwha Vision is the leader in global video surveillance with the world's best optical design / manufacturing technology and image processing technology focusing on video surveillance business for 30 years since 1990.
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