By John Lutz Boorman, Head of Product and Marketing, Hanwha Vision Europe

In recent years, the development and adoption of AI technology has accelerated at an unprecedented pace, impacting various industries. Naturally, the innovation impulse provided by AI is already a feature of the video surveillance sector. However, Hanwha Vision predicts that 2026 will be a decisive turning point for AI.

We foresee AI moving beyond simple adoption to become the essential foundation of the entire industry – the emergence of so-called “autonomous AI agents” will reshape the structure and operation 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 in which AI serves as a core engine, elevating video surveillance beyond monitoring to become a central pillar of operational efficiency and sustainability.

1: 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 distortions caused by difficult environments – such as poor lighting, backlighting or fog – are the main causes of false alarms derived from AI. In 2026, creating a “trustworthy data environment” to solve these problems will become the industry’s top priority.

With the performance of AI analytics engines improving everywhere, the focus of investment is shifting to securing high-quality video data that AI can interpret accurately.

One example of this is the minimisation of 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 uses deep learning to distinguish between objects and noise, effectively eliminating noise while optimising object details to deliver real-time data that is best suited for AI analysis. Larger image sensors capture more light, which fundamentally suppresses the generation of video noise, starting in low-light conditions.

In parallel, the ethical use of AI is becoming a major concern, and the mandatory introduction of AI governance systems is approaching. The EU’s AI uses a risk-based classification of AI systems used in public spaces and imposes a legal obligation on manufacturers to ensure transparency in AI from the design phase onwards, and this can only accelerate the industry’s efforts to create truly trustworthy AI.

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 that guarantees crystal-clear images optimised for AI analysis even in the harshest environments.

To strengthen its reputation for trustworthy AI, Hanwha Vision plans to update its WiseAI app in 2026 and leverage its capabilities in trustworthy data collection. An auto-calibration feature will determine the distance information of a scene to increase data reliability, and new AI event features will analyse abnormal behaviour such as fights and falls. These will be included in our 2026 product releases.

2: The AI agent partnership – from tool to teammate

As AI evolves from simple detection to an agent capable of analysing complex scenes and suggesting initial responses, the role of the operator will change fundamentally. Humans will delegate repetitive surveillance tasks to AI agents, freeing themselves up for more critical, high-level activities.

While earlier AI systems in video surveillance merely reduced the operator’s workload by automating repetitive tasks such as object search, tracking and alarm generation, the AI agent will be able to take this a step further. It will autonomously perform complex situation analyses, automatically execute an initial response and suggest the most effective follow-up measures to the surveillance operator.

For example, an AI agent can independently assess a break-in, initiate preliminary steps such as triggering an alarm, and then suggest the final decision options (e.g., whether to call the police) to the operator.

At the same time, it can automatically generate a comprehensive report containing real-time videos of the intrusion area, access logs, 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 judgement, complex analysis, and consideration of legal and contextual implications. 

They will also take on the role of AI governance manager, transparently tracking and monitoring all autonomous actions and thought processes performed by the AI agent. This essential function, which prevents misuse of the system, requires a significant increase in the skill level of the surveillance operator.

3: Promoting sustainable security

The explosive growth of generative AI is driving demand for energy. According to the International Energy Agency (IEA), data centre electricity consumption will more than double by 2030 in their baseline scenario – due to demand for AI.

The video surveillance industry can no longer prioritise performance without limits as it faces the dual challenge of dealing with high-resolution video data and the computational load of AI at the edge. Therefore, “sustainable security”, which prioritises operational longevity and minimising environmental impact, is becoming a core competency for reducing TCO (total cost of ownership) and achieving ESG goals.

To realise sustainable security, the industry is moving towards the development of power-efficient AI chipsets that drastically reduce power consumption while maintaining high-quality imaging and AI processing performance. It is also prioritising technologies that ensure data efficiency directly on the edge device (camera).

For example, Hanwha Vision’s AI-based WiseStream technology maximises video data management efficiency, helping to reduce power consumption. It does this by intelligently separating areas of interest from non-interesting areas within a scene and adjusting the compression ratio accordingly. This maximises data traffic efficiency while securely retaining all necessary information. In addition, cameras equipped with Wisenet 9 have improved baseline data transfer efficiency as they reuse images from static areas.

4: 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 perceive and understand – is becoming a reality.

This means that video surveillance goes beyond simple monitoring and becomes 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 physical asset created in a computer-based virtual environment.

Currently, AI information (metadata) extracted by AI cameras is already being used as business intelligence to optimise operations in areas such as smart cities, retail and advanced manufacturing.

In the future, this metadata will be merged with diverse information from access control devices, IoT sensors and environmental sensors to complete a unified, intelligent digital twin environment.

This digital twin environment will revolutionise the surveillance experience. Instead of complex, fragmented screens, operators will get a holistic view of the relationships between events 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 ultimately become an autonomous intelligent space that deeply understands situations and independently manages and solves problems.

The addition of the latest AI technology could give security managers or operators greater control over system operations. For example, AI can instantly understand natural language queries such as ‘Find a person who entered the server room after 10 p.m. yesterday’ and automatically analyse access and video logs to report the results. This means true situational awareness that can go far beyond simple complex search parameters.

5: Hybrid architecture: Distributed power

The rising cost of transmitting high-definition video data, coupled with concerns about data sovereignty and regulation, pose challenges for purely cloud-based systems. As a result, “hybrid architecture”, which preserves the advantages of the cloud while mitigating operational burdens, is rapidly establishing itself as the optimal solution for the video surveillance sector.

Hybrid architecture gives users ultimate control and flexibility over system operations. By enabling system functions to be moved to the most efficient location based on an organisation’s business needs, budget and legal/regulatory environment, it is becoming a key strategy for maximising TCO.

From a video surveillance perspective, hybrid architecture maximises efficiency by flexibly distributing functions between on-premises and cloud environments. On-premises environments can host real-time monitoring functions and critical functions that must meet short-term video retention and storage requirements. Functions that involve local processing and control of highly sensitive data are also placed on-premises to strengthen control over data security and ensure immediate on-site responsiveness.

Meanwhile, the cloud environment is used for functions such as remote centralised management, large-scale data analysis, deep learning for AI models, and long-term archiving. Using the cloud in this way ensures system scalability and operational simplicity.

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 devices (camera/NVR) handle the first layer of computation, perform real-time detections, and transmit only necessary data to the cloud. This reduces network bandwidth load and maximises speed and storage efficiency. The cloud environment (central server) then performs the second layer of deep analysis and large-scale machine learning based on the filtered data from the edge, significantly improving the accuracy and sophistication of AI functions.

By 2026, I believe AI will be firmly established as the new standard for security infrastructure. To achieve this, Hanwha Vision will provide users with trustworthy data and sustainable security value by delivering solutions based on a hybrid architecture optimised for AI analysis and processing. It looks like it’s going to be an exciting year!