Post-Quantum Crypto for Defense Systems, Faster

A quantum computer that can run Shor’s algorithm at scale doesn’t need to “hack” your network the Hollywood way. It just needs a copy of your encrypted traffic—captured today, stored quietly, and decrypted later when the math finally breaks. That’s why post-quantum cryptography (PQC) has become one of the most practical national security problems on the board: you don’t get to start the migration when the first cryptographically relevant quantum machine shows up.

This week’s news about a European partnership—SEALSQ (quantum-safe chips) and Airmod (secure electronics and middleware for aerospace and drones)—isn’t just industry cooperation. It’s a signal that the market is shifting from “PQC is a standards exercise” to “PQC is an integration and manufacturing problem.” And for the AI in Defense & National Security crowd, that shift matters: AI-enabled mission systems depend on trustworthy data, authenticated updates, and resilient communications. If the crypto underneath cracks, AI becomes faster at making bad decisions.

Why post-quantum cryptography is a defense problem right now

PQC is urgent because adversaries can collect encrypted data today and decrypt it later. This “harvest now, decrypt later” pattern is especially dangerous for defense organizations because the value of many secrets doesn’t expire quickly.

That includes:

• Weapon system telemetry and engineering data
• ISR tasking and dissemination
• Partner and coalition communications
• Identity credentials used across long-lived programs
• Supply chain provenance for components and firmware

Here’s the uncomfortable truth I’ve seen repeatedly: many programs assume crypto modernization is a “network team” issue. It’s not. PQC hits radios, datalinks, avionics, embedded controllers, and the update mechanisms that keep them safe. If your AI-enabled drone swarm needs secure device-to-device comms, you’re in PQC territory whether you like it or not.

The timeline risk most teams underestimate

Security professionals increasingly converge on the idea that a quantum system capable of breaking widely used public-key cryptography could emerge before the mid-2030s. The precise year is debated, but the planning implication is not: migration takes years, not months.

And migration isn’t a single switch. You’re changing algorithms, certificate chains, hardware acceleration choices, key sizes, memory footprints, and often the protocols themselves.

“Bigger crypto” is the real engineering tax

The hard part of PQC isn’t picking an algorithm; it’s fitting it into real systems. Post-quantum algorithms generally require larger keys and/or larger signatures than classical public-key crypto.

In enterprise IT, “larger” is annoying.

In defense platforms, “larger” can be mission-impacting:

• More CPU cycles → less compute for autonomy and sensor fusion
• More memory use → pressure on constrained embedded devices
• More power draw → reduced endurance for drones and edge nodes
• More bandwidth overhead → slower handshakes, more fragile links

If you’re fielding autonomous systems at the tactical edge, every watt matters. Every millisecond of latency matters. And if you’re doing this inside contested electromagnetic environments, overhead becomes operational risk.

Side-channel reality: passing a checklist isn’t the same as being safe

Another widely missed point: meeting a standard doesn’t magically eliminate implementation risk. Side-channel attacks (timing, power analysis, EM leakage) can compromise cryptography even when the algorithm is solid.

For defense, side-channel resilience matters because:

• Systems operate in hostile proximity (captured devices, lab analysis)
• Drones and sensors are often physically exposed
• Supply chains are complex, and hardware assurance is uneven

So when a partnership claims it can speed integration, the right reaction isn’t “nice marketing.” It’s: does this reduce the chance my implementation is fragile?

What the SEALSQ + Airmod partnership actually changes

This partnership targets the integration bottleneck: embedding PQC into devices without blowing up schedules. Their pitch is straightforward—combine quantum-safe chips with middleware that helps teams reuse and adapt software components rather than rebuilding cryptographic integration from scratch.

If it works as advertised, it addresses three chronic problems in defense tech delivery:

1) The “months to days” integration claim—why it’s plausible

Crypto integration often drags because teams have to:

• modify legacy code paths
• update protocol stacks
• rebuild secure boot and update flows
• re-run compliance and regression tests
• validate performance on constrained hardware

Middleware that standardizes interfaces and abstracts complexity can legitimately compress that timeline—especially for teams that repeatedly integrate security modules across product variants.

2) Making PQC practical for drones and aerospace electronics

Uncrewed systems are where PQC pain is most visible:

• Short development cycles
• High churn in components
• Pressure to use commercially available chips
• Real-world electronic warfare adaptation

PQC that’s too heavy won’t get fielded. The value of this partnership is that it aims to make “quantum-resistant” a buildable requirement rather than a whitepaper requirement.

3) Supply chain and sovereignty: the subtext is strategic

Airmod’s focus on reducing dependence on Chinese-sourced components and enabling more localized production steps (including customer-specific data injection) ties directly into a wider defense trend: security isn’t only about algorithms; it’s about where your trust anchors come from.

Even if you trust the math, you still need to trust:

• the silicon
• the firmware
• the toolchain
• the provisioning process

That’s why Europe’s continued investment in domestic semiconductor capacity matters here. PQC isn’t just a crypto story—it’s also a semiconductor and industrial policy story.

Where AI fits: PQC is the foundation under AI-enabled defense

AI systems don’t “replace” cryptography; they depend on it. If you’re building AI into defense and national security, PQC should show up in your architecture reviews alongside model risk and data governance.

Here are the most concrete intersections.

AI-driven cyber defense needs quantum-resistant trust

Security analytics and autonomous response are only as good as the integrity of the telemetry.

If an adversary can:

• spoof identities,
• impersonate update servers,
• replay or forge signed messages,

then AI-based detection will be trained and tuned on poisoned ground truth. PQC helps preserve the authenticity layer that makes AI telemetry meaningful.

Mission AI needs secure updates and attestations

Modern autonomy is update-driven:

• models are patched
• perception stacks are improved
• threat libraries change

That creates an obvious target: the software update pipeline. Post-quantum digital signatures (and hybrid approaches during transition) protect update integrity when adversaries eventually gain quantum capability.

A simple stance: if your program treats secure update as an afterthought, it’s not “AI-enabled.” It’s just “networked and vulnerable.”

AI can accelerate the PQC transition (but won’t save you from it)

AI can materially reduce migration friction in a few places:

• Codebase discovery: map where cryptographic libraries and protocols are embedded
• Dependency analysis: identify transitive crypto usage in firmware and third-party packages
• Test generation: expand regression tests for handshake failures, latency, and fallback behavior
• Performance profiling: quickly surface the hotspots when PQC increases compute/memory load

But AI can’t eliminate the engineering constraints. If the chip can’t handle the algorithm within power and latency budgets, you still need hardware changes.

A practical roadmap for defense teams planning PQC now

The best time to plan PQC was years ago. The second-best time is during your next refresh cycle. Here’s a roadmap that works in real programs.

1) Inventory what you actually need to protect

Start with a short list:

• data types with 10+ year secrecy requirements
• systems with long field life (15–30 years is common)
• communications paths that cross coalition boundaries

If you can’t name those, you’re not ready for a PQC program plan.

2) Identify your “trust anchors” and upgrade paths

Trust anchors include:

• device identity keys
• signing keys for firmware and models
• certificate authorities
• secure elements / TPM-like components

Ask one blunt question: Can we rotate these keys in the field, at scale, under contested conditions? If the answer is “maybe,” fix that first.

3) Use hybrid crypto during the transition

Most serious migrations will use hybrid approaches—classical + post-quantum—until PQC is proven across ecosystems.

That’s not indecision; it’s risk management:

• protects against near-term classical threats
• adds PQC resistance for long-lived data
• reduces interoperability shock

4) Engineer for performance before you field for compliance

PQC failures in defense won’t look like “we got hacked.” They’ll look like:

• radios that can’t complete handshakes reliably
• drones that lose endurance
• edge devices that overheat
• update packages that exceed bandwidth limits

Treat latency, power, and memory as security requirements.

5) Demand integration accelerators from vendors

This is where partnerships like SEALSQ + Airmod are relevant. When you evaluate vendors, ask for:

• reference implementations on your target class of hardware
• side-channel mitigation approach
• provisioning and key injection options aligned with your security boundary
• test artifacts that shorten your accreditation timeline

If the vendor can’t show realistic integration patterns, you’re buying R&D disguised as product.

What this means for 2026 planning and budgets

PQC is becoming a procurement discriminator. Over the next budget cycle, expect more RFP language that requires:

• NIST-aligned post-quantum cryptography readiness
• crypto agility (ability to swap algorithms later)
• hardware-rooted trust suitable for long-lived platforms
• resilient update mechanisms for AI and software-defined capability

If you’re leading an AI program in defense, you should treat PQC the way you treat data pipelines: foundational, not optional.

The sharper question for leadership isn’t “Are we quantum-safe?” It’s: “Which of our mission systems will fail first when cryptography gets heavier and adversaries get smarter?”

If you’re planning a modernization effort—especially for autonomy, drones, ISR processing at the edge, or secure coalition data sharing—now is the time to design PQC in, not bolt it on.