AI‑powered SaaS is turning CCTV from passive recording into a proactive, searchable sensor network—unifying cameras, access control, intrusion, and analytics in the cloud with real‑time detection, fast investigations, and open integrations.
Cloud vision services add turnkey models and pipelines so teams can ingest, analyze, and deploy computer vision at scale without building bespoke infrastructure, accelerating time‑to‑value for security and operations.
What’s new in 2025
- Unified, open cloud VMS: Genetec Security Center SaaS delivers video, access, intrusion, automation, and forensic search in one platform with frequent feature drops and broad device support.
- Direct‑to‑cloud + edge resilience: Updates add direct‑to‑cloud cameras, SD‑card edge recording, and WebRTC streaming for low‑latency live views, enabling hybrid deployments at scale.
- Managed AI pipelines: Vertex AI Vision centralizes ingestion, analysis, storage, and deployment for computer vision apps, reducing integration overhead and governance risk.
Core capabilities that matter
- Forensic search in minutes: AI motion and attribute search let teams jump to moments of interest instead of scrubbing hours of video, cutting investigation time dramatically.
- Real‑time alerts and LPR: Cloud platforms ship smart video search and license‑plate recognition to trigger accurate, low‑latency alerts across existing camera fleets.
- People/vehicle analytics and heatmaps: People and vehicle detection with privacy controls like face blur improve safety and insights while protecting identities.
- Metadata‑driven analytics: ONVIF Profile M standardizes object, face, and license‑plate metadata and MQTT events for queue detection and IoT workflows.
- Video understanding services: Azure AI Video Indexer adds face detection, OCR, labels, scene segmentation, and moderation to extract structured insights from video libraries.
Architecture blueprint
- Edge + cloud hybrid: Run analytics at the edge for latency and resilience while centralizing management and search in a cloud VMS that supports open hardware and deployment choice.
- Standards first: Adopt ONVIF Profile M for consistent analytics metadata and eventing across cameras, VMS, and cloud services, including MQTT bridges to IoT.
- Build vs. buy AI: Use Vertex AI Vision or Azure AI Video Indexer for managed models and pipelines; note AWS Panorama’s end‑of‑support timeline and plan alternatives if migrating.
Compliance, privacy, and ethics
- EU AI Act guardrails: Real‑time remote biometric identification in public spaces for law enforcement is tightly restricted with narrow exceptions; design deployments accordingly.
- Responsible AI framework: Apply NIST AI RMF 1.0 to manage bias, privacy, and safety risks across the AI lifecycle with documented governance and controls.
- Privacy by design: Use features like role‑based access, masking, and face blur to minimize exposure of identities while enabling authorized investigations.
High‑impact use cases
- Retail ops and safety: Queue detection via ONVIF/MQTT, people counting, and heatmaps optimize staffing and layouts while reducing loss events.
- Campuses and enterprises: Unified video, access, and intrusion with direct‑to‑cloud streaming and forensic search improve response and simplify multi‑site management.
- Smart cities and logistics: Cloud LPR and smart search across distributed sites accelerate incident resolution and traffic or yard management.
KPIs to prove impact
- Investigations: Mean time to evidence (MTTE) for finding relevant clips via smart search versus manual scrub baselines.
- Detection quality: False‑alarm rate and alert‑to‑response time for LPR and analytics events across critical zones.
- Uptime and coverage: Percentage of cameras in direct‑to‑cloud/edge‑recording mode and success rate of WebRTC live call‑ups.
- Privacy and compliance: Share of streams with masking/face‑blur and documented adherence to AI Act/NIST governance policies.
60–90 day rollout plan
- Weeks 1–2: Foundation and standards
- Weeks 3–6: Pilot analytics
- Weeks 7–10: AI services and privacy
- Weeks 11–12: Governance and scale
Buyer checklist
- Openness and cadence: Open architecture with third‑party analytics, direct‑to‑cloud camera support, and fast, continuous updates.
- Low‑latency search and LPR: Proven smart video search and plate recognition across heterogeneous camera fleets.
- Metadata standards: ONVIF Profile M conformance for consistent analytics and event interfaces, including MQTT for IoT workflows.
- AI service fit: Managed pipelines for ingestion, analysis, and deployment with enterprise governance and scalability.
Governance essentials
- Document lawful bases: Map where biometric analytics are used, restrict or disable prohibited modes, and retain evidence trails for audits under the EU AI Act.
- Risk and assurance: Apply NIST AI RMF to assess bias, privacy, robustness, and security, with monitoring and human‑in‑the‑loop escalation paths.
FAQs
- Can we keep existing cameras and go cloud‑first?
- How do we standardize analytics across vendors?
- What if we need library‑scale video insights?
The bottom line
- Cloud‑first, AI‑enhanced video analytics is delivering faster investigations, better detection, and richer operational insights on an open, standards‑based foundation—with privacy and governance built in.
- Teams that combine an open SaaS VMS, standards like ONVIF Profile M, and managed vision services see measurable gains in speed, safety, and compliance while future‑proofing their surveillance stack.
Related
How does Genetec’s Security Center SaaS handle direct-to-cloud camera integrations
What new third-party analytics were added to Genetec’s platform
How do Verkada’s smart motion search and Genetec’s forensic search compare
What privacy controls do vendors offer for AI face and data protection
How will edge recording improvements change incident response times