Deploy lightweight, hardware-secured AI on edge devices to process sensitive sensor data locally, eliminating cloud privacy risks.
Services
Confidential AI for Edge and IoT Devices
Deploy hardware-secured AI models directly on edge devices and gateways. Process sensitive video, audio, and sensor data locally within Trusted Execution Environments (TEEs), eliminating cloud data transfer risks and ensuring privacy-by-design.
CONFIDENTIAL AI FOR EDGE AND IOT
The Edge AI Privacy Dilemma
Process video, audio, and biometric data on-device with 99.9% data sovereignty, ensuring raw data never leaves the secure perimeter. We deploy lightweight Trusted Execution Environments (TEEs) on edge gateways and IoT devices, enabling local AI inference without transmitting sensitive data to the cloud.
- Hardware-Rooted Security: Leverage
Intel SGXorARM TrustZoneto create secure memory enclaves, isolating AI models and live sensor data from the host OS and other applications. - Privacy-by-Design: Achieve compliance with GDPR and the EU AI Act by design, as personal data is processed and anonymized at the source.
- Reduced Latency & Bandwidth: Execute inference in <100ms on the device, cutting cloud round-trip latency and operational bandwidth costs by over 60%.
We architect end-to-end confidential AI data pipelines where data is decrypted, processed, and re-encrypted solely within the TEE. This is critical for applications like biometric access control, industrial video analytics, and patient monitoring in healthcare.
Explore our broader capabilities in Confidential Computing for AI Workloads or see how we apply similar principles for **Financial Algorithmic Modeling in Secure Enclaves](/services/financial-algorithmic-modeling-in-secure-enclaves).
DELIVERING TANGIBLE ROI
Business Outcomes of Confidential Edge AI
Deploying AI at the edge introduces unique data privacy and security challenges. Our confidential computing solutions for edge and IoT devices deliver measurable business value by protecting your most sensitive assets while enabling new capabilities.
01
Privacy-by-Design for Sensitive Sensor Data
Perform local AI inference on video, audio, and biometric data directly on edge devices using hardware-based Trusted Execution Environments (TEEs). Raw sensor data never leaves the secure enclave, eliminating cloud data transfer risks and ensuring compliance with regulations like GDPR and the EU AI Act. This enables use cases in healthcare, smart cities, and retail that were previously too risky.
02
Reduced Latency & Bandwidth Costs
By processing data locally within a secure enclave, you eliminate the round-trip latency of sending data to the cloud for inference. Achieve real-time decision-making for applications like autonomous robotics or industrial inspection while cutting cloud egress and compute costs by up to 70% for high-volume sensor deployments.
< 100ms
Local Inference Latency
70%
Potential Bandwidth Savings
03
Protection of Proprietary AI Models
Your model weights and inference logic are encrypted in memory and protected from the host operating system, other applications, and even the cloud provider. This safeguards your core intellectual property when deploying AI to untrusted or multi-tenant edge hardware, a critical requirement for competitive industries. Learn more about securing model IP in our guide on Encrypted AI Model Deployment and Management.
04
Compliance with Evolving AI Regulations
Hardware-based attestation provides verifiable proof that your AI workload is running in a genuine, uncompromised TEE. This creates an immutable audit trail for data-in-use, directly supporting compliance with the EU AI Act's requirements for high-risk AI systems and other global mandates demanding provable data protection.
ISO/IEC 27001
Security Framework Alignment
NIST AI RMF
Risk Management Integration
05
Enablement of Secure Multi-Party Analytics
Confidential edge AI allows multiple entities (e.g., different departments or partner organizations) to contribute data to a joint inference model without exposing their raw datasets. This enables collaborative intelligence, such as cross-hospital pandemic trend analysis or multi-manufacturer supply chain optimization, while preserving data sovereignty. Explore our related service for Secure Multi-Party AI Computation Services.
06
Future-Proofed Edge AI Architecture
Our implementations use standardized TEE frameworks (Intel SGX, AMD SEV, ARM TrustZone) and orchestration tools like Kubernetes with attestation plugins. This avoids vendor lock-in and provides a scalable, portable foundation for deploying confidential AI across heterogeneous edge fleets, from gateways to embedded sensors. For complex deployments, see our Confidential AI Data Pipeline Architecture service.
Kubernetes
Orchestration Native
Cross-Cloud
Portable Design
From Initial Assessment to Production Deployment
Typical Project Timeline & Deliverables
A structured breakdown of our phased approach to deploying hardware-secured AI on your edge and IoT devices, ensuring predictable delivery and measurable outcomes.
| Phase & Deliverables | Starter (Proof-of-Concept) | Professional (Production Pilot) | Enterprise (Full Fleet Deployment) |
|---|---|---|---|
Project Duration | 4-6 weeks | 8-12 weeks | 12-16 weeks |
Security Architecture Review | |||
TEE Hardware Compatibility Assessment | 1-2 device types | Up to 5 device types | Custom fleet assessment |
Lightweight Model Optimization for TEE | 1 model variant | 2-3 model variants | Custom model family optimization |
On-Device Enclave Prototype | |||
Secure Key Management & Attestation Setup | Basic attestation | Automated attestation pipeline | Custom PKI integration |
Local Inference Performance Benchmarking | < 100ms latency target | < 50ms latency target | Custom SLA (< 20ms typical) |
End-to-End Data Pipeline (Sensor to Enclave) | Basic pipeline | Resilient, fault-tolerant pipeline | Multi-sensor fusion pipeline |
Integration with Existing IoT/Edge Platform | API-level integration | SDK & agent deployment | Full platform orchestration |
Deployment & Fleet Management Tooling | Manual scripts | Automated OTA update framework | Enterprise-grade management console |
Security Audit & Penetration Testing Report | Included | Comprehensive audit + ongoing | |
Ongoing Support & Maintenance | Email support | SLA with 24h response | Dedicated engineering team |
SECURE, LOCAL AI INFERENCE
Industry Applications & Use Cases
Deploy confidential AI directly on edge devices and IoT gateways to process sensitive sensor data locally. Our hardware-based Trusted Execution Environments (TEEs) ensure privacy-by-design, eliminating the need to send raw data to the cloud.
01
Smart City & Public Safety Video Analytics
Perform real-time object detection and facial recognition on live CCTV feeds within secure enclaves on edge servers. Protect citizen privacy by ensuring raw video frames are never exposed to the operating system or network. Learn more about our approach to Confidential AI Inference Enclave Development.
< 100ms
Inference Latency
On-Device
Data Processing
02
Healthcare IoT & Wearable Diagnostics
Process sensitive biometric data (ECG, PPG, audio) from medical wearables and bedside monitors within TEEs on gateway devices. Enable local AI diagnostics for patient monitoring while ensuring HIPAA/GDPR compliance for data-in-use. Explore our specialized Confidential Computing for Biometric AI Processing.
HIPAA
Compliance Ready
Local
Diagnostics
03
Industrial Predictive Maintenance
Run vibration, thermal, and acoustic anomaly detection models on factory floor gateways. Protect proprietary operational data and machine learning IP from exfiltration, even on shared or potentially compromised industrial networks. Our Hardware-Based TEE Integration for AI Workloads ensures end-to-end security.
99.9%
Model Integrity
Air-Gapped
Option Available
04
Autonomous Vehicle & Drone Perception
Secure LiDAR, radar, and camera fusion algorithms in vehicle/drone compute units. Protect perception models and sensitive geolocation data from runtime attacks, ensuring the safety and security of autonomous navigation systems.
ASIL-D
Safety Standards
Hardware-Rooted
Trust
05
Retail & Smart Shelf Analytics
Deploy on-edge computer vision for inventory tracking and customer behavior analysis within store gateways. Process video of shoppers locally to protect privacy, generating anonymized insights without sending footage to the cloud.
Real-time
Inventory Count
PII Protected
By Design
06
Defense & Field Intelligence
Enable classified image/signal analysis on ruggedized edge devices in contested environments. TEEs provide a hardware-rooted trust anchor, preventing data and model compromise even if the device is physically captured. This aligns with our work in TEE-Based AI for Defense and Intelligence.
Air-Gapped
Deployment
TEMPEST
Mitigations
Technical and Commercial Considerations
Frequently Asked Questions on Confidential Edge AI
Common questions from CTOs and engineering leaders evaluating secure AI deployment for edge and IoT environments.
Contact
Talk to the team about your AI system.
Share what you are building, where you need help, and what needs to ship next. We will reply with the right next step.
01
NDA available
We can start under NDA when the work requires it.
02
Direct team access
You speak directly with the team doing the technical work.
03
Clear next step
We reply with a practical recommendation on scope, implementation, or rollout.
30m
working session
Direct
team access