Beyond Monitoring: How Trustworthy AI and Sustainability Will Redefine Video Surveillance by 2026

The rapid evolution of artificial intelligence, particularly driven by generative AI, has unleashed a wave of innovation across nearly every industry, and the video surveillance sector is no exception. This isn’t just about integrating AI; it’s about fundamentally reshaping how security systems operate.

Hanwha Vision predicts that 2026 will mark a pivotal turning point, where AI transcends mere adoption to become the indispensable backbone of the entire industry. This transformation is set to be spearheaded by the emergence of “Autonomous AI Agents,” poised to revolutionize the structure and operational methodologies of video surveillance systems.

Amidst these profound shifts, Hanwha Vision has identified five crucial trends that will dictate the industry’s focus:

* Trustworthy AI: Data Quality and Responsible Use
* The AI Agent Partnership, From Tool to Teammate
* Driving Sustainable Security
* Smart Spaces Powered by Video Intelligence
* Hybrid Architecture: The Distributed Power

These trends collectively signal a future where AI acts as the core engine, elevating video surveillance from basic monitoring tools to central pillars of operational efficiency, intelligence, and environmental responsibility.

01. Trustworthy AI: Data quality and responsible use

As AI analysis technology becomes ubiquitous, the adage “Garbage In, Garbage Out” has never been more pertinent for video surveillance. Visual noise and distortion—spawned by challenging environments such as low light, intense backlighting, or fog—are notorious culprits behind AI malfunctions and disruptive false alarms. By 2026, the industry’s paramount objective will be to establish a “Trusted Data Environment” to rigorously address these issues.

With the analytical prowess of AI engines reaching new heights across the board, investment is strategically shifting towards securing high-quality video data that AI can interpret flawlessly. A prime example of this commitment is the significant investment in minimizing noise and distortion in extreme conditions through advanced AI-based ISP (Image Signal Processing) technology and the deployment of larger sensors. AI-based ISP leverages deep learning to intelligently differentiate between genuine objects and visual noise, effectively eliminating distortions while optimizing object details to provide real-time data perfectly suited for AI analysis. Concurrently, larger image sensors inherently capture more light, thereby fundamentally suppressing video noise generation, particularly in low-light scenarios.

Hanwha Vision’s 2nd Generation P series AI Cameras, featuring a Dual NPU design, the Wisenet 9 chipset with AI-based image enhancement, and a large 1/1.2” sensor, exemplify this commitment, guaranteeing crystal-clear images optimized for AI analysis even in the most demanding environments.

In parallel, as the ethical deployment of AI escalates into a major global concern, the mandatory adoption of robust AI governance systems is swiftly approaching. International benchmarks, such as the European Union’s AI Act, explicitly classify video surveillance AI utilized in public safety as “High-Risk technology.” This critical classification imposes a legal imperative on manufacturers to ensure AI Transparency from the design phase, accelerating the industry’s push to build genuinely trustworthy and accountable AI systems.

Furthermore, Hanwha Vision plans to significantly upgrade its WiseAI app, capitalizing on its capabilities in trusted data acquisition. Specifically, a new Auto Calibration feature will be integrated to determine scene distance information, enhancing data reliability. Additionally, new AI event features designed to analyze abnormal behaviors like fighting and falling will be included in the 2026 product releases.

02. The AI agent partnership, from tool to teammate

As AI advances beyond mere detection to sophisticated agents capable of analyzing intricate scenarios and proposing initial responses, the role of the monitoring operator is poised for a profound transformation. Humans will increasingly delegate repetitive surveillance tasks to AI Agents, freeing them to focus on more critical, high-level functions that demand nuanced judgment.

While previous AI systems in video surveillance merely aimed to reduce operator workload by automating mundane tasks such as object search, tracking, and basic alarm generation, the AI Agent represents a quantum leap. It autonomously conducts complex situational analysis, automatically executes preliminary responses, and intelligently recommends the most effective follow-up actions to the human monitoring operator.

Consider this scenario: an AI Agent can independently assess an intrusion event, initiate preliminary steps such as activating an alarm, and then propose final decision options (e.g., whether to alert law enforcement) 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 evolve into strategic commanders, making crucial final decisions that require intricate analysis, nuanced judgment, and consideration of legal and contextual implications. They will also assume the vital role of an AI governance manager, transparently tracking and supervising all autonomous actions and reasoning processes executed by the AI Agent. This essential oversight function, critical for preventing system misuse, necessitates a significant elevation of the monitoring operator’s skill set and expertise.

03. Driving sustainable security

The explosive growth of generative AI is undeniably fueling a ‘Technological Energy Crisis.’ According to International Energy Agency (IEA) reports, power consumption by data centers is projected to more than double by 2030, driven by the escalating demand for high-performance AI servers.

The video surveillance industry finds itself at a critical juncture, where an unchecked pursuit of performance is no longer sustainable. It confronts the dual challenge of managing surging volumes of high-resolution video data and the intensive computational burden of Edge AI. As a result, Sustainable Security—which prioritizes operational longevity and minimizing environmental costs—is rapidly emerging as a core competency for achieving substantial Total Cost of Ownership (TCO) reductions and meeting crucial ESG (Environmental, Social, and Governance) objectives.

To realize truly sustainable security, the industry is widely embracing the development of ‘low-power AI chipsets’ designed to drastically reduce energy consumption while preserving high-quality imaging and robust AI processing capabilities. Simultaneously, there’s a heightened focus on technologies that ensure data efficiency directly at the edge device (the camera itself).

For instance, Hanwha Vision’s AI-based WiseStream technology significantly maximizes video data management efficiency, directly contributing to lower power consumption. This intelligent system separates regions of interest from non-interest within the video stream and dynamically adjusts the compression ratio based on their importance. This process maximizes network traffic efficiency while securely retaining all critical information. Furthermore, cameras equipped with the Wisenet 9 chipset have improved baseline data transmission efficiency by intelligently reusing images from static regions.

These intelligent data management strategies concurrently satisfy both performance and efficiency demands, proving to be the most effective means to directly reduce the power consumption required for server expansion and cooling systems.

04. Smart spaces powered by video intelligence

With AI seamlessly integrated into cameras and rapid advancements in cloud technology enabling large-scale data processing, the transformative concept of a ‘Sentient Space’—an environment capable of sensing, understanding, and even anticipating—is fast becoming a reality.

In this paradigm shift, the role of video surveillance expands far beyond simple monitoring. It evolves into a core data source for Digital Twin technology, providing real-time reflections of the physical environment. A Digital Twin, in essence, is a dynamic virtual replica of a real-world physical asset, meticulously created within a computer-based virtual environment.

Presently, the AI information (metadata) extracted by advanced AI cameras is already being leveraged as invaluable business intelligence to optimize operations across numerous smart sectors, including smart cities, retail, and manufacturing facilities. Looking ahead, this metadata will be intricately fused with diverse information streams from access control devices, IoT sensors, and environmental sensors to construct a unified, intelligently interactive Digital Twin environment.

This comprehensive Digital Twin environment promises to revolutionize the monitoring experience. Instead of fragmented screens displaying disparate data, operators will gain a holistic, map-based interface that seamlessly integrates Video Management Systems (VMS) and access control. Within this perfectly mirrored digital space, the system will evolve into an Autonomous Intelligent Space—one that profoundly understands situations and can independently manage and resolve issues without constant human intervention.

The integration of the latest AI technology provides security managers and operators with unprecedented control over system operations. For instance, 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 deliver precise results. This signifies a true leap into situational awareness that moves far beyond basic complex search parameters.

05. Hybrid architecture: The distributed power

The escalating costs associated with transmitting high-definition video data, coupled with critical regional data sovereignty requirements and regulatory concerns, are increasingly imposing operational limitations on purely cloud-based systems. In this context, Hybrid Architecture—which masterfully preserves the benefits of cloud computing while simultaneously mitigating operational strain—is rapidly asserting 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 empowers users with ultimate control and unparalleled flexibility over system operations. By allowing critical system functions to be strategically deployed to the most efficient location based on an organization’s specific business needs, budgetary constraints, and legal/regulatory environment, it emerges as a key strategy for maximizing Total Cost of Ownership (TCO) efficiency.

From a video surveillance perspective, hybrid architecture optimizes efficiency by intelligently distributing functions between on-premises and cloud environments. On-premise setups can host real-time monitoring functions and critical operations that necessitate compliance with regulations for short-term video storage and retention. Furthermore, functions involving the local processing and control of highly sensitive data are also ideally placed on-premises to bolster data security control and ensure immediate response capabilities at the site.

Conversely, the cloud environment is strategically leveraged for functions such as remote centralized management, large-scale data analysis, deep learning for sophisticated AI models, and long-term archiving. This intelligent utilization of the cloud ensures unparalleled system scalability and operational ease.

Beyond simple infrastructure separation, this architecture also provides the optimal distributed computing structure essential for the successful operation of advanced AI-analysis-based video surveillance systems.

In this innovative structure, edge devices (cameras/NVRs) handle the initial layer of computation, performing real-time detection and selectively transmitting only necessary data to the cloud. This significantly reduces network bandwidth strain and maximizes processing 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, thereby substantially enhancing the accuracy and sophistication of AI functions.

In conclusion, this distributed computing model serves as the critical infrastructure foundation that simultaneously boosts the immediate responsiveness of edge devices and the advanced analytical capabilities of the cloud.

A Hanwha Vision representative commented, “2026 is the point when AI will be firmly established as the new standard for security infrastructure. We are committed to securing trustworthy data and delivering sustainable security value to the market by providing solutions based on a hybrid architecture optimized for AI analysis and processing.”