Designed for manufacturers of embedded devices, AI Bunker redefines AI security on embedded systems, enabling seamless, secure, and efficient model deployment.
Protect proprietary AI models from theft, reverse engineering, and unauthorized access.
Integrate secure model storage seamlessly into standard AI workflows, reducing complexity.
Ensure real-time inference performance
AI bunker has won the 2025 Embedded Awards!
Protecting AI models without disrupting workflow
Embedded devices increasingly rely on AI models to perform disparate tasks. Companies need to spend a lot of effort, time, and money to build AI models, such as deep neural networks, due to a long process that includes onerous design and development phases. AI models are however susceptible to theft, tampering, and unauthorized access due to the vulnerabilities of hardware and software environments. Concerning theft, when no proper countermeasures are in place, attackers could steal intellectual property that required enormous investments to be generated.
Protecting AI models on embedded devices while maintaining a seamless usage workflow is hence a major challenge. AI Bunker addresses this by enabling secure storage of AI models within a dedicated, strongly-isolated software partition on the embedded device, ensuring high levels of protection. At the same time, it transparently allows invoking the model’s inference using a standard workflow, making it appear as if the model resides in a regular environment.
Core Features
Secure Model Storage in Isolated Partitions
Seamless Model Invocation
Advanced Security Protocols and Update
Real-Time Performance
The Architecture
AI Bunker
Key strengths
Strongly-isolated Integrated Secure Partitioning leveraging hardware- and software-based mechanisms through next-generation hypervisor technology, security hardening techniques, and containerization at the edge
Standard programming interface for a transparent inference workflow
Low latency and low overhead optimized for embedded devices
Who’s AI Bunker built for?
Manufacturers of AI-powered embedded devices
Automotive, Railway, Medical, Smart Home and City, Any embedded device with sensitive AI models that must remain secure even at the edge
Use Cases
Personalized and customized LLMs
Medical: AI-powered diagnostic
Automotive & Robotics: ADAS, SLAM, Path Planning, Motion Control
Industrial IoT: anomaly detection, quality control