Secure ML Model Deployment for Edge Devices
Update Edge AI Systems with Security, Robustness, and Privacy ensured
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:
- 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
Benefit
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
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
Benefit
End-to-end encryption ensures that data remains secure during transmission. Role-based access control limits access to sensitive data.
Key Feature
Benefit
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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Book your individual meeting with one of our experts!