Securing Embedded Systems in the Quantum Age: A Practical Guide to Post-Quantum Cryptography

Post-Quantum Cryptography Embedded Systems Quantum Security
Sneha Sharma
Sneha Sharma

Co-Founder

 
July 28, 2025 3 min read

TL;DR

This article covers the urgent need for Post-Quantum Cryptography (PQC) in embedded systems, detailing current NIST standards and migration challenges like performance limitations and hardware constraints. It provides practical solutions for future-proofing embedded devices against quantum computing threats, focusing on hybrid approaches, cryptographic agility, and the importance of proactive security measures.

The Looming Quantum Threat to Embedded Systems

Quantum computers are coming, and they threaten embedded systems. These systems, found in everything from cars to medical devices, are attractive targets. Let's examine why they face such risk.

  • Embedded systems are everywhere in critical infrastructure and IoT.
  • Long lifecycles increase the risk of "store now, decrypt later" attacks.
  • Resource constraints make them particularly vulnerable.

The urgency of securing these systems is clear. Next, we'll explore how quantum computers undermine current cryptography.

Understanding Post-Quantum Cryptography (PQC)

Quantum computers threaten current cryptographic systems. But, Post-Quantum Cryptography (PQC) offers a solution. It aims to secure systems against both classical and quantum computers.

  • PQC focuses on algorithms deployable on existing hardware.
  • Different PQC families exist, like lattice-based and hash-based.
  • Standardized algorithms are vital for interoperability.

NIST's efforts play a crucial role in PQC standardization. Next, we'll explore NIST's standardization efforts in detail.

The Unique Challenges of Implementing PQC in Embedded Systems

Implementing Post-Quantum Cryptography (PQC) in embedded systems presents unique hurdles. Unlike traditional systems, these devices face strict limitations. Let’s explore these challenges.

Embedded systems often have limited memory, processing power, and energy. These constraints impact the feasibility of implementing PQC algorithms. For instance, a white paper by NXP highlights that PQC key sizes require significantly more storage compared to ECC.

  • Memory: Limited RAM and flash storage pose challenges for storing larger PQC keys and executing complex algorithms.
  • Processing Power: Slower processors struggle with the computational intensity of PQC, impacting performance.
  • Energy Consumption: Battery-powered devices face reduced lifespans due to the higher energy demands of PQC.

Embedded systems are particularly vulnerable to side-channel attacks. These attacks exploit the physical characteristics of a device like timing and power consumption to extract secret keys. Robust countermeasures are essential, but they increase complexity and cost.

Bridging the gap between robust security and resource limitations is a critical challenge. In the next section, we'll explore the lack of hardware acceleration for PQC in embedded systems.

Practical Migration Strategies for Embedded Systems

Migrating to post-quantum cryptography (PQC) in embedded systems is a complex task. Resource constraints and varied application requirements demand careful planning.

One practical strategy involves hybrid cryptography. This approach combines traditional algorithms, like ECC, with PQC algorithms.

  • It offers a layered defense, protecting against compromises in either algorithm.
  • For example, you can combine ECDH with ML-KEM for key exchange. This ensures that even if ECC is broken, ML-KEM still provides security.

Cryptographic agility is another critical strategy. It means designing systems that can easily switch between cryptographic algorithms.

  • Standardized interfaces and protocols become extremely important.
  • Consider firmware updates and remote management capabilities. This will allow for quick responses to new threats.

Prioritizing migration based on risk and implementing these strategies can make your systems more secure. In the next section, we’ll explore hybrid cryptography in more detail.

The Future of PQC in Embedded Systems

The quantum revolution is on the horizon, and embedded systems must evolve. How can we ensure these devices remain secure in a world with quantum computers?

  • Dedicated PQC hardware accelerators will boost performance. These specialized chips handle complex math faster and more efficiently.

  • FPGA-based implementations offer flexibility for prototyping. Developers can test and customize PQC algorithms before committing to fixed hardware.

  • Chip vendors play a key role in enabling PQC. Their support is essential for widespread adoption.

  • Memory footprint and computational complexity are key targets. Researchers are finding ways to make PQC algorithms smaller and faster.

  • Trade-offs exist between performance and security. Developers must balance speed with robust protection.

  • Open-source libraries accelerate PQC implementation.

The future of PQC relies on teamwork.

  • PQC standards will continue to evolve.
  • Industry groups and research institutions play a vital role.
  • Collaboration ensures interoperability and security.

As embedded systems evolve, PQC ensures they remain safe. Next, let's recap the key points.

Sneha Sharma
Sneha Sharma

Co-Founder

 

My work has extended to the utilization of different data governance tools, such as Enterprise Data Catalog (EDC) and AXON. I've actively configured AXON and developed various scanners and curation processes using EDC. In addition, I've seamlessly integrated these tools with IDQ to execute data validation and standardization tasks. Worked on dataset and attribute relationships.

Related Articles

AI investment

Enterprises Prepare for Increased AI Investment Amid Data Challenges

Explore how enterprises are increasing AI investment despite data challenges. Learn strategies for data management, ai solutions, and leveraging Salesforce for AI success.

By Sneha Sharma October 5, 2025 14 min read
Read full article
AI

Enhancing Complex, Multi-Model Data with AI Technologies

Discover how AI technologies can enhance complex, multi-model data within Salesforce CRM. Learn to improve data quality and drive better business outcomes with AI.

By Anushka Kumari October 5, 2025 13 min read
Read full article
Semantics

Implementing Semantics and AI in Private Data Solutions

Discover how to implement semantics and AI in private data solutions, focusing on Salesforce CRM, data intelligence, and digital transformation. Learn practical strategies for enhanced data governance.

By Anushka Kumari October 5, 2025 18 min read
Read full article
AI business analytics

Unlocking Rapid Value from AI in Business Analytics

Discover how to unlock rapid value from AI in business analytics with Salesforce. Learn to integrate AI for faster insights, automation, and better decisions.

By Sneha Sharma October 5, 2025 14 min read
Read full article