Smart Building Occupancy Sensors UK 2026: Privacy-First Buyer's Guide

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Occupancy sensors detect presence and often estimate counts while spatial intelligence aggregates sensor data into analytics about how people move and use physical spaces to inform decisions such as HVAC schedules and desk allocation.

Occupancy sensor: a device or system that detects whether people are present in a space and often counts how many.
Spatial intelligence: aggregated analytics about how people move and use physical spaces, produced from sensor data to inform decisions (e.g., HVAC schedules, desk allocation).

These technologies range from simple presence detectors to AI-driven platforms that deliver occupancy heatmaps, utilisation reports, and real-time dashboards.

Privacy-first design is essential in the UK because sensor deployments may interact with personal data, potentially identifying individuals if not configured and governed correctly.

Legal framework: UK GDPR and the Data Protection Act 2018 require lawful, fair, and transparent processing of personal data; where sensor data could identify people, additional obligations apply.
Regulatory guidance: The Information Commissioner’s Office (ICO) expects organisations to undertake Data Protection Impact Assessments (DPIAs) for intrusive systems and follow data minimisation principles.
Workplace trust: Employees and visitors are sensitive to monitoring; privacy-first approaches reduce reputational risk and improve adoption.

Privacy-first sensors minimise or eliminate personally identifiable data, favouring aggregated or anonymous outputs and on-device processing.

Different sensor technologies offer trade-offs between accuracy, analytics and privacy risk. Choose based on required insights and acceptable privacy posture.

Thermal (camera-free)

Pros: Detects heat signatures, good anonymisation, less invasive than video, robust in varied lighting.
Privacy: Generally considered privacy-preserving when configured to avoid individual identification.

Passive Infrared (PIR)

Pros: Low-cost presence detection, reliable for simple occupancy triggers.
Privacy: Non-imaging, minimal privacy risk but limited analytics and counts.

Video/camera-based

Pros: High accuracy and rich analytics (counting, posture, identity potential).
Privacy: High risk — can capture identifiable images and requires strong controls if used.

Wi‑Fi/Bluetooth device sensing

Pros: Leverages device signals for presence and dwell time.
Privacy: Can be tied to devices and individuals; pseudonymisation and opt-out mechanisms needed.

CO2 and environmental sensors

Pros: Indirect occupancy estimation useful for ventilation control.
Privacy: Non-identifying, but only provides aggregate occupancy proxies.

Radar / LiDAR / UWB

Pros: Can detect movement and counts without optical imaging; high accuracy.
Privacy: Generally lower privacy risk than cameras but dependent on data processing and retention.

When privacy is a priority, camera-free options such as thermal, radar, or LiDAR combined with edge processing are common choices.

Prioritise capabilities that reduce identifiability and support compliance while delivering the metrics you need.

Camera-free sensing or image-less processing: avoids capturing identifiable images.
Edge processing: sensor processes raw data locally and sends only aggregated or anonymised results to the cloud.
Data minimisation: only necessary occupancy metrics (counts, presence, zone utilisation) are stored.
Short retention windows: anonymised or aggregated data retained only as long as needed.
Strong encryption: in transit and at rest, using industry-standard protocols.
Clear access controls & logging: role-based access and audit trails for data access.
DPIA and compliance support: vendor provides documentation and assistance to meet ICO requirements.
Independent privacy certification or third-party audit: verification of claims where possible.
Transparent user-facing policies: signage and communications to occupants about what is collected and why.

Technical planning ensures the chosen sensors meet operational, integration and security requirements.

Coverage and granularity: define zones (desks, meeting rooms, floors) and required resolution (per-person vs zone-level).
Accuracy needs: different applications require different accuracy thresholds — HVAC control tolerates coarser accuracy than headcount for safety.
Integration protocols: ensure compatibility with building systems (BACnet, Modbus, MQTT, REST APIs) and workplace platforms (room booking, CAFM).
Deployment constraints: wiring (PoE vs battery), mounting heights, ceiling types, and maintenance access.
Network architecture: segment sensor traffic on a secure VLAN and consider on-premises processing for sensitive sites.
Scalability and management: centralised device management, firmware updates, and monitoring for large estates.
Latency requirements: real-time control demands low-latency data paths and edge compute.

Request comparable performance data from vendors and pilots to validate claims.

Detection accuracy (%): true positives vs false negatives for presence and counts.
Counting precision: per-person counting vs grouped counts and performance under crowding.
Latency: time from event to actionable output.
Environmental robustness: performance across lighting, temperature, and obstructions.
False alarm rate: important for security and automation scenarios.
Uptime & reliability: measured against SLAs during pilot and full deployment.

Ask vendors for data from comparable UK deployments or independent evaluations.

Occupancy sensors deliver measurable savings and operational benefits across building operations.

HVAC and lighting optimisation: reduce energy consumption by matching services to real occupancy.
Space optimisation: right-size real estate and improve desk/meeting room utilisation.
Cleaning and maintenance: schedule services based on footfall rather than fixed timetables.
Safety and emergency management: occupancy counts for evacuation and compliance.
Workplace experience: provide employees with up-to-date availability and route guidance.

ROI typically comes from reduced energy bills, lower real estate costs, and operational efficiencies. Quantify targets before procurement and measure during pilots.

Follow a staged approach to reduce risk and validate privacy and performance assumptions.

Define objectives and KPIs: energy savings target, utilisation metrics, accuracy thresholds.
Select pilot sites: representative spaces with different use patterns.
Run a controlled pilot: test privacy features, accuracy, integration, and user acceptance.
Review DPIA and privacy controls: ensure legal and policy alignment.
Scale progressively: refine zoning, data retention, and integration based on pilot learnings.
Include contractual protections: Data Processing Agreement (DPA), SLAs for uptime and accuracy, and clear ownership of aggregated data.

Use a standard vendor questionnaire during procurement to compare privacy, performance and integration claims.

Do you capture images or personally identifiable information? If so, how is it protected?
Where is raw sensor data processed and stored — on-device, on-premise, or in the cloud?
What anonymisation and minimisation techniques do you apply?
Can you support a DPIA and provide technical documentation for audits?
What integration standards and APIs do you offer?
How do you handle firmware updates, security patches, and device management?
What are your accuracy metrics in spaces similar to ours?
Do you provide evidence of independent privacy or security assessments?
What are typical retention periods and can we configure them?
What support and training are included during and after deployment?

Butlr is an AI company specialising in privacy-first people sensing and spatial intelligence using thermal, camera-free sensors that detect heat signatures and produce anonymised occupancy analytics. Teams prioritising privacy without sacrificing spatial insights should consider vendors that combine non-imaging sensors with edge AI and privacy-by-design principles. For more information visit https://butlr.com

Final checklist before you buy

Map objectives to required sensor granularity and accuracy.
Prioritise non-imaging, edge-processing solutions where privacy is critical.
Insist on DPIA support, configurable retention, and clear access controls.
Test in a pilot that mirrors your building types and workflows.
Verify integration with building management and workplace systems.
Secure contractual guarantees for data handling, SLAs, and security.

Selecting privacy-first occupancy sensors is a legal and cultural decision; the most successful deployments balance accuracy and interoperability with transparent, robust privacy safeguards that protect individuals and build trust in the UK of 2026.