Embedded Systems Development and AI Engineering: Quantum-Safe Cryptography for Constrained IoT Field Devices
Introduction: The Challenge of Future-Proofing Security on Edge AI
Clients required a migration path to post-quantum security but were severely constrained by MCU memory, compute, and energy budgets. The goal was to create a hybrid key exchange that provides PQC (Post - Quantum Cryptography) fallback while preserving existing classical trust models — a critical step in future-proofing any smart infrastructure solution.
Solution Overview: Hybrid PQC Key Exchange on Constrained Embedded Systems
EurthTech delivered a feasibility study and prototype integrating hybrid PQC patterns (ECDH + Kyber-like KEM) into microcontroller firmware (STM32L4/nRF52840-class). The prototype used a hybrid handshake where the classical ECDH provides immediate connectivity and the PQC component re-keys asynchronously. This demonstrates a vital Edge AI embedded system capability for defense-grade security.
Technical Implementation: Cryptography Engineering and Embedded AI India
The embedded systems development focused on efficiency: bench-marking algorithms against latency, RAM footprint, and energy consumption. The firmware separated provisioning-time heavy operations (full PQC exchange) from lightweight runtime operations. We implemented constant-time routines, used hardware-accelerated AES where available, and secured key storage via secure elements or authenticated external flash regions, showcasing the advanced competency of our Embedded AI India team.
Results & Impact: AI Engineering and Strategic Risk Reduction
Benchmarks validated feasible PQC inclusion for session rekey operations with latency in the 100s of milliseconds on mid - range MCUs. The project delivered a feasibility report and a clear roadmap for migration. By implementing hybrid schemes, we reduced the strategic risk of future quantum decryption for recorded traffic while enabling existing infrastructure continuity—a high-value outcome delivered through advanced AI engineering solutions.
Scalability & GIS Context: Hybrid schemes buy time for the client’s migration plan, including phased adoption and secure-element upgrades on next-gen hardware. Monitoring crypto health and managing global key rotation policies for a distributed fleet are core geospatial engineering services requirements. This security layer is foundational for all high-assurance Predictive maintenance AI IoT platforms.