Video surveillance is undergoing a bigger change than simply replacing local recorders with cloud storage. In 2026, cameras are increasingly becoming data...
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Video surveillance is undergoing a bigger change than simply replacing local recorders with cloud storage. In 2026, cameras are increasingly becoming data sources for AI systems that can identify relevant activity, organise large volumes of footage, support investigations, and give security teams a clearer view across multiple locations. At the same time, cloud infrastructure is making those capabilities easier to manage across distributed environments.
The shift is visible in industry data. Genetec’s 2026 State of Physical Security Report surveyed more than 7,300 security professionals and found that interest in adopting AI among end users had more than doubled compared with the previous year. The same research found that 70% of end users had concerns about AI design, data use, and understanding how the technology works.
Traditional surveillance systems were designed primarily to record video. Investigators often had to know when an incident occurred and manually search through recordings from individual cameras or sites. That model becomes increasingly difficult when organisations operate hundreds or thousands of cameras.
AI and cloud infrastructure are now converging to change that workflow. Instead of treating footage as passive recordings, modern surveillance environments can turn video into searchable, centrally accessible information that supports faster investigations, remote management, and more proactive security operations.
For decades, the basic surveillance workflow was straightforward. Cameras captured footage, a DVR or NVR stored it, and somebody reviewed the recording when an incident occurred. The system provided evidence, but most of its value appeared after something had already happened.
AI changes that relationship with video. Computer vision models can examine visual information for people, vehicles, movement patterns, objects, or specific events. Rather than requiring an operator to continuously watch dozens of feeds, software can help narrow large amounts of video down to footage that deserves attention.
This becomes particularly valuable in large environments. A security team responsible for several warehouses, campuses, retail stores, or office locations may receive video from hundreds of cameras every hour. Manual monitoring does not scale at the same rate as camera deployments.
The practical change is therefore not simply better image quality. Surveillance systems are gradually moving from recording infrastructure toward information systems. Cameras produce the visual data, AI helps interpret it, and security teams decide how that information should influence an investigation or response.
AI processing becomes considerably more useful when video is no longer isolated inside individual buildings. Cloud-connected architecture can give authorised teams centralised access to cameras, archived footage, alerts, and management controls across multiple locations.
That matters particularly for distributed organisations. A retailer with dozens of branches should not have to send an employee to each store whenever corporate security needs footage. Similarly, a company operating warehouses in several states may want its security leadership to review incidents from one location rather than maintaining completely separate workflows at every facility.
The industry is increasingly favoring flexible deployment models rather than treating cloud migration as an all-or-nothing decision. Genetec reported that 43% of end users in its 2025 physical security research expected hybrid deployments to be their preferred model within five years, compared with 18% favoring fully cloud-based systems and 17% expecting to remain completely on-premises.
That preference has continued into 2026. Organisations are evaluating where individual workloads should run based on bandwidth, storage requirements, cybersecurity, cost, data residency, and operational needs. Video might remain locally available while management, analytics, backups, or selected workloads use cloud infrastructure.
The convergence of AI and cloud surveillance is therefore not necessarily about sending every second of every camera feed directly to a remote data center. Modern architectures can divide work between cameras, local appliances, edge computing resources, and cloud services.
Time-sensitive processing may happen close to the camera, while the cloud provides centralised administration, longer-term storage, software updates, investigation tools, and access for geographically distributed teams. The result is an architecture that can combine local responsiveness with cloud scalability.
A cloud surveillance system can also use AI to make stored footage substantially easier to investigate. Coram, for example, describes a hybrid approach in which its AI-powered video management can search footage for specific events, objects, or people, while storage can be balanced between local and cloud environments. The supplied platform information also describes customisable retention options ranging from 30 to 365 days, allowing organisations to adjust how long footage is kept according to operational or compliance requirements.
The wider significance is interoperability between storage and intelligence. Video storage becomes more useful when analytics can examine it, analytics becomes more practical when results can be accessed centrally, and centralised management becomes more valuable when organisations can apply consistent policies across multiple locations.
One of the clearest benefits is investigation speed. Traditional systems can force security teams to identify a specific camera, determine an approximate time, retrieve the recording, and manually move through large stretches of video. AI-assisted search can reduce the amount of footage a person needs to inspect.
Real deployments illustrate the operational impact. Australian retailer Kogan, which manages security across 21 warehouses and a head office, reported reducing the time required to find, save, and share footage by more than 90% after adopting a hybrid-cloud video environment. Recorded clips could also be saved to cloud archives and shared without transferring footage through physical USB devices.
Video analytics can also produce information beyond conventional incident investigation. At selected Nisa retail stores, network cameras were combined with cloud-based machine learning software to analyse shelf availability. The trial maintained stock availability at approximately 95%, showing how camera infrastructure can support operational decisions as well as security.
Similar principles apply elsewhere. Manufacturers can investigate activity around production areas, campuses can review incidents across multiple buildings, logistics companies can examine vehicle and loading-zone activity, and retailers can connect visual information with broader operational processes. Human judgment remains essential, but AI can reduce the amount of raw footage people must manually process before making a decision.
More capable surveillance creates more responsibility. A system does not become better simply because it processes more data or adds more AI models. Organisations must determine what information should be collected, who can access it, how long it should remain available, and which uses are appropriate.
Cybersecurity is particularly important because cloud-connected cameras, identities, stored recordings, APIs, and administrative accounts all become part of a larger digital environment. IBM’s 2026 Cost of a Data Breach research placed the global average cost of a breach at $4.99 million, 12% higher than the previous year. The report also found a 56% increase in AI-driven attacks.
Cost deserves similar attention. Cloud removes some local hardware requirements, but storage, data transfer, analytics, and AI processing consume resources continuously. Flexera’s 2026 State of the Cloud found that 85% of surveyed organisations still regarded managing cloud spend as a major challenge, while estimated wasted cloud spending had risen to 29%.
Organisations therefore need retention policies, role-based permissions, cybersecurity controls, network planning, employee training, and clear AI governance before expanding surveillance capabilities. They should also evaluate which data genuinely needs cloud processing instead of assuming that moving every workload off-site will automatically improve security or economics.
The next stage of surveillance is likely to be defined less by camera specifications and more by what organisations can understand from their video environment. Resolution and image quality will remain important, but the ability to retrieve useful information from thousands of hours of footage will increasingly influence system design.
AI models are also moving from narrow detection toward more flexible investigation tools. Instead of security teams navigating camera-by-camera timelines, future workflows will increasingly allow people to describe what they are looking for, narrow events by attributes or context, and correlate information from different systems.
Cloud infrastructure makes continuous improvement easier because analytics, management software, and security features can evolve without organisations rebuilding every local recording environment. Hybrid architecture will remain important where bandwidth, resilience, privacy, or regulatory requirements make fully cloud-hosted systems impractical.
The strongest systems will therefore not be those that simply collect the most footage. They will be the ones that help organisations find meaningful information quickly, apply appropriate controls to that information, and preserve human oversight when AI influences security decisions.
Cloud surveillance is moving beyond remote video storage and viewing. AI analytics, centralised management, intelligent search, and hybrid computing are increasingly becoming part of the same surveillance architecture.
AI can analyse large amounts of video and help identify relevant people, objects, movements, or events. This can reduce the amount of footage security personnel need to review manually and make investigations more efficient.
No. Many organisations are adopting hybrid architectures where some recording or processing remains on site while cloud infrastructure handles functions such as centralised management, remote access, selected analytics, or backup storage.
It depends on the cameras, management platform, networking environment, and compatibility requirements. Organisations should audit existing hardware before migrating so they can determine which components can remain and which need replacement or integration.
Privacy, cybersecurity, retention periods, network capacity, AI accuracy, user permissions, integration, operating costs, and applicable regulations should all be considered. Clear policies are especially important when analytics process information involving employees, customers, students, patients, or members of the public.
AI and cloud surveillance are converging because each technology addresses a limitation of the other. AI can extract useful information from enormous quantities of video, while cloud and hybrid infrastructure can make that information accessible, manageable, and scalable across locations.
In 2026, the goal is increasingly moving beyond simply recording more footage. Organisations should focus on building surveillance environments that make video easier to understand, investigate, and govern while protecting privacy and cybersecurity. The long-term value will come from turning cameras into useful sources of operational intelligence without losing human judgment or responsible control.
