As the adoption of AI technology accelerates, Hanwha Vision anticipates a significant shift in the video surveillance industry by 2026.
The company predicts that AI will become the foundational cornerstone of the industry, leading to the introduction of 'Autonomous AI Agents' that will fundamentally change the operation and structure of video surveillance systems.
Key AI Trends in Video Surveillance
To adapt to these transformative changes, Hanwha Vision has highlighted five critical trends that are expected to shape the future of the industry.
The integration of AI is set to elevate video surveillance from mere monitoring to a central element of operational efficiency and sustainability.
Trustworthy AI Through Data Quality
By 2026, ensuring high-quality video data for accurate AI interpretation will be a top priority
The importance of high-quality data in AI analysis is becoming increasingly apparent in the field of video surveillance.
Challenges like visual noise from low light or fog can lead to false alarms, emphasizing the need for a 'Trusted Data Environment'. By 2026, ensuring high-quality video data for accurate AI interpretation will be a top priority.
Advancements in AI-based ISP
AI-based high-performance Image Signal Processing (ISP) technology is minimizing noise in challenging environments. By employing deep learning, ISP differentiates noise from objects, optimizing real-time data for AI analysis.
Large sensors that capture more light ensure less noise, especially in low-light conditions, enhancing overall data quality.
AI Ethics and Governance
Ethical AI use is gaining traction as the EU imposes clarity mandates on AI tactics used in public spaces
Ethical AI use is gaining traction as the EU imposes transparency mandates on AI systems used in public spaces.
This push is accelerating the development of AI governance systems, building trust in AI technologies throughout the industry.
The AI Agent Evolution
AI Agents are poised to transform the role of operators by taking on tasks such as scene analysis and proposing initial responses.
Operators will transition to roles that involve complex decision-making and oversight of AI actions, acting as governance managers who track AI operations to prevent misuse.
Sustainability and AI Integration
The rise of AI increases energy needs significantly, prompting the industry to balance high-resolution video
The rise of AI increases energy demands significantly, prompting the industry to balance high-resolution video with AI's computational needs.
Focusing on sustainable security through low-power AI chipsets and edge device data efficiency is becoming essential to reduce Total Cost of Ownership (TCO) and meet ESG objectives.
Creating 'Smart Spaces'
As AI integrates into cameras and cloud technology evolves, the concept of 'Sentient Space' becomes feasible.
Video surveillance expands into 'Digital Twin' technology, creating real-time virtual replicas of physical environments. This development enhances situational awareness, enabling more intelligent monitoring.
Hybrid Architecture for Efficiency
It balances the benefits of cloud technology with operational efficiency, reducing strain and maximizing TCO
With the costs of high-definition video transmission rising, and data sovereignty concerns, hybrid architecture is emerging as an ideal solution.
It balances the benefits of cloud technology with operational efficiency, reducing strain and maximizing TCO.
Maximizing Cloud and On-premise Functions
This architecture distributes functions optimally between on-premises and cloud environments. Real-time monitoring and regulatory functions remain on-premises, while the cloud handles large-scale analysis and model training. Such a setup maintains security and provides scalability.
Hanwha Vision aims to leverage this hybrid architecture to enhance AI-based video surveillance systems, making AI a new standard in security infrastructure by 2026. This approach will provide reliable data and sustainable security value for users.
In recent years, the development and adoption of AI technology has accelerated at an unprecedented pace, impacting various industries. Of course, the spark of innovation provided by AI is already a feature of the video surveillance sector. However, Hanwha Vision predicts that 2026 will be a pivotal turning point for AI.
They foresee AI moving beyond simple adoption to becoming the essential foundation of the entire industry - the emergence of so-called ‘Autonomous AI Agents’ will reshape the structure and operations of video surveillance systems.
To meet this wave of change, Hanwha Vision has identified five key trends that the industry must focus on. These trends signal a future where AI serves as the core engine, elevating video surveillance further from monitoring to providing central pillars of operational efficiency and sustainability.
Trustworthy AI: Data quality and responsible use
As AI analysis becomes ubiquitous, the principle of “Garbage In, Garbage Out” will be critical in video surveillance. Visual noise and distortion caused by challenging environments - such as low light, backlighting, or fog - are primary causes of AI-derived false alarms. In 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.
AI-based high-performance ISP
An example of this is 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 optimising 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.
In parallel, as the ethical use of AI becomes a major concern, the mandatory adoption of AI governance systems is approaching. The EU’s AI uses a risk-based classification of AI systems deployed in public spaces and imposes a legal obligation on manufacturers to ensure transparency in AI from the design phase, and this can only accelerate the industry’s push to build genuinely trustworthy AI.
The AI agent partnership-from tool to teammate
As AI evolves from straightforward detection to an agent capable of analyzing complex scenes and proposing initial responses, the role of the operator will change fundamentally. Humans will delegate repetitive surveillance tasks to AI Agents, freeing themselves for more critical, high-level activity.
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 will be able to take this a step further. It will autonomously conduct complex situational analysis, automatically execute an initial response, and recommend the most effective follow-up actions to the monitoring operator.
Role of AI governance manager
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 (for example, whether to call the police) to the operator. Simultaneously, it can automatically generate 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.
Operators will become more like 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 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.
Driving sustainable security
The explosive growth of generative AI is driving demand for energy. According to the International Energy Agency (IEA), power consumption by data centres will more than double by 2030 under its base case scenario - due to demand for AI.
The video surveillance industry can no longer prioritise performance without limit, as it faces the dual challenge of surging high-resolution video data and the computational burden of AI at the edge. As such, ‘sustainable security’, which prioritises operational longevity and minimizing environmental impact, 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 moving towards developing low-power AI chipsets that drastically reduce power consumption - while preserving high-quality imaging and AI processing power. It is also prioritising technologies that ensure data efficiency directly on the edge device (camera).
Smart spaces powered by video intelligence
As AI is integrated into cameras and advances are made 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.
This sees video surveillance expanding 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 used as business intelligence to optimize operations in sectors such as smart cities, retail and advanced manufacturing. 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.
Digital Twin environment
This Digital Twin environment will revolutionise the monitoring experience. Instead of complex, fragmented screens, operators will 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 video system will eventually evolve into an Autonomous Intelligent Space that deeply understands situations and manages and resolves issues independently.
Adding the latest AI technology could provide security managers or operators with greater 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 analyse access and video records to report the results. This signifies true situational awareness that can move far beyond basic complex search parameters.
Hybrid architecture: The distributed power
The rising cost of transmitting high-definition video data, coupled with data sovereignty and regulatory concerns, poses challenges for purely cloud-based systems. As such, ‘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.
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 will become a key strategy for maximising TCO.
Real-time monitoring functions and critical functions
From a video surveillance standpoint, hybrid architecture maximises 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. Functions involving the local processing and control of highly sensitive data are also placed on-premise to bolster data security control and ensure immediate response capabilities at the site.
Meanwhile, the cloud environment is leveraged for functions such as remote centralised management, large-scale data analysis, deep learning for AI models, and long-term archiving. Using the cloud this way 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.
New standard for security infrastructure
In this structure, edge (camera/NVR) devices handle the first layer of computation, performing real-time detection and only transmitting necessary data to the cloud. This reduces network bandwidth strain, maximises speed and storage efficiency. 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 2026, I believe AI will be firmly established as a new standard for security infrastructure. To meet this, Hanwha Vision will deliver trustworthy data and sustainable security value to users by providing solutions based on a hybrid architecture optimized for AI analysis and processing. It looks set to be an exciting year!
