Secure ML Model Deployment for Edge Devices

Update Your AI System – Ensuring Security, Robustness, and Privacy

Secure AI Update Solution

Updating AI systems involves significant security threats.

aicas’ edge-to-cloud solution for embedded systems provides a secure way to deploy AI applications and their components such as machine learning models to remote edge devices and vehicles.

It ensures seamless transfer of ML model updates, including transmission, installation, and operation. With encrypted, signed components and secure communication channels, the solution offers maximum security, robustness, and protection against unauthorized access, thus ensuring safe operation of edge AI systems.

/ The Challenge

Security Threats in Transferring ML Models to Edge Devices

AI systems constantly evolve, leveraging data to enhance performance. Updating machine learning models and securely transmitting them to remote edge devices pose critical security challenges. These devices often operate in remote or heterogeneous environments, making them susceptible to unauthorized access, data leaks, theft, or manipulation. Failing to address these risks can lead to severe financial, legal, and reputational consequences, underscoring the need for robust security measures during AI system updates.

Key Challenges: How to Tackle Them Effectively

Robustness

Preventing the system to go down in case of model incompatibilities with the target system or AI application, malicious or accidental alterations, or functional errors.

Security

Protecting models from misuse, theft, tampering, or denial-of-service attacks.

Privacy

Protecting sensitive information encoded in models, both in transit and at rest. Protecting sensitive information assembled when gathering training data and feedback.

Operational Integrity

Secure live status reporting on the update and execution of the AI application and its model.

/ The Solution

ML Model Lifecycle Workflow with aicas

aicas enables seamless transfer of ML models from development systems, via the cloud, to edge devices in an MLOps workflow:

MLOps Workflow

Train a ML model (upstream process).
Securely deploy the model to devices at the edge (aicas’ solution).
Use the model in edge applications.
Gather performance data.
Improve the model with enhanced data (downstream process).
Repeat.

We integrate standard tools (e.g., Python, TensorFlow) for training and inference, enabling experts to leverage their preferred tools and frameworks. Recognizing that ML models are only part of a larger application, our modular microservices ensure models and applications are fully aligned—offering flexibility and scalability in building tailored solutions. Operational insights from edge deployments (“loop backwards”) drive continuous model and system improvement.

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/ Key Benefits Offered by Our Solution

Security Protection That Avoids Costs and Revenue Losses

aicas’ solution eliminates security risks when updating AI applications on edge devices or vehicle fleets. Benefit from:

Prevention of Unauthorized Model Manipulation

Our solution ensures secure model updates, protecting against alterations that could lead to operational disruptions or safety hazards.

Data Protection
and Privacy

We safeguard sensitive data during transmission, ensuring compliance with regulations and protecting intellectual property from theft.

Ease-of-Use and Ease-of-Integration

The solution is largely automated whilst always providing detailed information and control over the operational status. It integrates with the most common AI tools, CI/CD systems, and embedded computing platforms.

Resilience Against Attacks and Flaws

Our secure update process minimizes downtime, ensuring continuous operations and preventing revenue loss from service interruptions.

Reputation
Safeguard

By ensuring secure model updates, our solution helps to maintain customer trust and protects your brand’s reputation.

/ Use Case Examples

AI Systems Advanced by aicas' Secure Solution

AI systems that benefit most from aicas‘ solution operate edge devices in remote locations and require secure updates outside of a firewall. Below are examples of devices running at the edge:

IIoT: Industrial Automation

Industrial devices such as sensors and actuators
Building technologies like security cameras and presence detection systems
Robotics for manufacturing and warehouse automation
Predictive maintenance sensors on machines and equipment
Smart meters for energy and resource monitoring
Environmental monitoring devices like air quality sensors
Automated quality control systems using AI-driven cameras
Asset tracking systems using GPS and RFID technologies

Mobility and Automotive

Autonomously controlled vehicles like drones and self-driving cars
Smart traffic management systems
Electric vehicle (EV) charging stations with intelligent monitoring
Vehicle-to-everything (V2X) communication devices
Fleet management systems for realtime monitoring of vehicles
In-vehicle AI for driver assistance and safety systems
Connected infotainment systems in vehicles
Advanced driver-assistance systems (ADAS) in cars
Telemetry systems for vehicle performance tracking

/ Solution Details

Key Features of the Comprehensive Protection for Your Edge AI Systems

Key Feature

Encrypted and Signed Model Updates

Benefit

Secure and Reliable Model Transmission

Our solution ensures that model updates are encrypted, protecting them from unauthorized access or tampering. Digital signatures verify the authenticity of the models, guaranteeing that only trusted updates are deployed to edge devices.

Key Feature

Digital Signatures and Version Control

Benefit

Reliable Model Authentication and Integrity

Digital signatures verify model updates. The system ensures immutability and traceability, preserving the integrity of the models. Version control allows easy tracking of updates, ensuring that only compatible and authorized versions are installed.

Key Feature

End-to-End Encryption and Role-Based Access Control

Benefit

Data Privacy

End-to-end encryption ensures that data remains secure during transmission. Role-based access control limits access to sensitive data.

Key Feature

Centralized Management, Over-the-Air (OTA) Updates, and Device Health Monitoring

Benefit

Simplified Management of Diverse Edge Devices

Our solution allows for centralized management and Over-the-Air (OTA) updates, simplifying the deployment of updates across a diverse range of edge devices. Device health monitoring provides realtime status reports and alerts, ensuring devices are functioning optimally and securely.

/ Core Components

The Solution Components

aicas Edge Device Portal

Model Management: Stores the packaged and encrypted ML models while “in motion.”
Secure Connectivity: Manages secure connections between the training system and target systems.
Distribution Oversight: Supervises the ML model’s distribution process.
Operator Feedback: Provides visual feedback for human operators.

AI Agent on JamaicaAMS

Model Deployment: Executes the distribution process, unpacks, triggers decryption, and installs the ML model in the inference engine. Supervises the ML application and provides feedback and data for training.

Swissbit Hardware Security

Enhanced Protection: Provides the hardware anchor for advanced security in-system validation, encryption, and digital signatures—even plug-in for devices that do not yet have a dedicated security module.

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