Lumana: How AI is Redefining the Way Smart Cities See and Support Community Safety

From passive cameras to real-time urban intelligence, a new era of city operations is emerging

For more than a decade, the evolution of video surveillance has been reshaping the foundations of urban management. What began as simple passive recording has matured into a real-time intelligence layer powering safer, more efficient, and more responsive cities. As Lumana CEO Sagi Ben-Moshe explains , “Organizations now expect far more than reactive video recording, which is where advanced AI-based solutions come in, driving safety and security for modern smart cities and their communities.”

The impact of AI on smart city video surveillance

AI has created a fundamental shift in how municipalities perceive and use video. Instead of serving only as evidence after an incident, video streams have become an active source of insight, automation, and early-warning detection. Cities can now identify critical risks, such as weapon brandishing, vandalism, fires, illegal dumping, or traffic violations the moment they occur.

Because most urban environments already maintain extensive camera networks, AI can add value immediately and at scale. The technology unlocks new capabilities like automated monitoring, instant alerts, and rapid citywide investigations. This allows cities to respond faster, more effectively, and deploy resources with far greater precision.

Ben-Moshe adds “Beyond safety, AI transforms raw video into operational data. It helps to understand mobility patterns, crowd density, service bottlenecks, or infrastructure issues in ways that previously required significant manual effort. For smart cities striving for efficiency, sustainability, and resilience, the AI insights turn video into a powerful decision-making engine.”

Turning cameras into a distributed intelligence network

Lumana helps cities, rural municipalities, and communities shift from passive video recording to a proactive urban intelligence layer and delivers real-time insights to keep communities safer and smarter.

Lumana’s end-to-end AI video security platform detects fires, weapons, intrusions, crowd anomalies, and infrastructure issues in real time and alerts the appropriate teams instantly. Responses become faster, more coordinated, and more accurate. Investigations that once took hours now take seconds, thanks to the ability to search across thousands of video streams in parallel.

In real-world deployments across smart city customers worldwide, Lumana has delivered measurable improvements: fewer break-ins, reduced vandalism, safer public spaces, and significant savings for taxpayers. All of this is achieved without installing new hardware, maximizing the value of existing infrastructure.

The Advantages of Edge Computing for Smart Cities

At the core of Lumana’s platform is its hybrid cloud–edge architecture. Ben-Moshe explains that processing video intelligence directly on devices delivers several critical advantages for municipalities:

Speed: millisecond-level detection enables immediate platform response, directly reducing first-responder reaction times.

Bandwidth Efficiency: video stays local which significantly lowers infrastructure costs, maintenance demands, and long-term capital investment.

Scalability and up-to-date: cities can instantly unlock new AI capabilities on their existing camera infrastructure, strengthening community safety without additional hardware spend, while remaining continuously up to date.

Privacy: sensitive footage remains on-site, reducing exposure risks and regulatory overhead This helps municipalities stay compliant with guidelines in regulated sectors like healthcare and education, supporting integrations across schools, hospitals, and other critical facilities.

As Ben-Moshe notes, “Hybrid edge–cloud AI brings the best of both worlds, cloud AI performance, flexibility and scale combined with the privacy, offline, and cost control of on-premises systems.

For smart cities, this means smarter operations without compromising resilience, privacy, or governance standards.

Recommendations for future-ready smart cities

Drawing from Lumana’s work with municipalities worldwide, Ben-Moshe outlines several guiding principles for leaders modernizing their video ecosystems:

Start with clear outcomes: whether the goal is public safety, mobility, waste reduction, operational efficiency, or emergency coordination, define what success looks like.

Adopt hybrid-first architectures: this reduces total cost-of-ownership, accelerates detection, and strengthens privacy protections.

Break down departmental silos: integrate video insights across traffic management, emergency services, public works, and citizen service platforms.

State of the art AI technology: AI models that continuously learn and adapt ensure long-term relevance and maximum ROI.

Select platforms built for privacy: ensure the system handles data governance, retention, anonymization, and compliance end-to-end.

Focus on usability: tools must empower frontline staff, not overwhelm them.

Looking ahead, Ben-Moshe envisions a future where video systems do not just detect events, but understand them: “AI-powered video is already moving beyond simple detection toward real-time reasoning, enabling first responders, together with AI systems, to understand intent, context, and the likely trajectory of events so they can act faster and more effectively.”