The medtech space is fascinating. It used to be bolt on security and worry about it later. Some of that philosophy has changed , the FDA made regulations and compliance more mandatory and less checkbox assessments and governance non technical advice, but still , many challenges remain.
What about trying to implement ZKP or homomorphic encryption into medical devices or embedded instruments? Even Post Quantum security techniques merging techniques that crypto and blockchain have researched, Google has written some white papers on and cryptographers are testing ?
Looking more into medtech and how a lot of medical devices generate far more sensitive data now , and across a system of parties with different trust relationships is a growing concern. Think about what a medical device actually is now vs just some pacemaker. They are a medical device, gateways, hospital network, patient phone and network, cloud infrastructure, analytics platforms, providers, payers, and regulators.
The problem is not simply protecting the device. It is deciding what each party can see, what each party can verify, and what happens when those trust assumptions change. And devices may send electrical or other signals now not just Bluetooth connectivity.
Conventional controls handle much of the foundation. Secure boot, signed firmware, hardware roots of trust, device identity, attestation, and authenticated communications establish device and software integrity. That trust chain begins long before deployment. You have factory provisioning of device credentials and keys, boot ROM and hardware root of trust integrity, and contract manufacturing custody that determine whether the root of trust is valid from the moment the device is powered on.
If the requirement is simply proving that a device is running approved firmware, a signed measurement is generally more appropriate than a zero knowledge proof. We do assume manufacturing is trustworthy and not installing software or hardware backdoors. Should we though ?
Once deployed, a gateway may act as a transparent relay or, if local protocol translation or edge processing is required, as a plaintext trust boundary. The security problems diverge once data leaves the device. Homomorphic encryption can allow certain cloud computations without exposing the underlying plaintext to the compute environment, but its practical use remains limited to workloads where the performance and complexity are justified.
It is not a solution for most real time physiological telemetry. Zero knowledge proofs address a different problem entirely. Proving a defined claim about private data without disclosing the data itself. Neither technology establishes that a sensor measurement reflects physical reality. An attack against an analog sensor interface can alter a measurement before software or cryptographic controls ever see it.
That distinction matters because medical device security has different consequences. A confidentiality failure can expose protected health information and create regulatory and legal risk. An integrity or availability failure can affect a clinical decision or interrupt therapy. The controls, threat models, and acceptable failure modes are not the same.
The fifteen-year lifecycle compounds these risks. Cryptographic algorithms can be deprecated, keys can be compromised, standards can change and regulatory requirements can evolve. But the hardware ages too. Memory retention, component degradation, sensor drift, clock accuracy, physical tampering, side channel attacks, and limited processing, storage, and battery capacity can constrain what security changes are actually possible. And who knows the more AI is pushed what else becomes a vulnerability.
Cryptographic agility therefore cannot mean simply having the ability to install a new algorithm. The device has to have enough hardware capacity to support the migration, enough storage for recovery mechanisms and alternative trust material, and an update architecture capable of delivering the change.
Maybe we need to look at how the Voyager spaceships still operate and can be updated and fixed after 40+ years. And nobody can actually go land on the ship and change or fix it. Different use case but learning from that engineering philosophy may go a long way for medical devices and AI.
A medical device without reliable access to its trust infrastructure creates another problem. A revocation and key replacement may not be available when needed. When trust anchors fail, the system requires defined safety-state policies such as graceful degradation to standalone operation. A security mechanism that bricks a safety-critical device is an unacceptable failure mode.
Regulation adds another constraint. A manufacturer can engineer for change, but a cryptographic modification can affect verification, validation, documentation, and the approved configuration of the device. Meanwhile, the manufacturer does not control every trust boundary. Cloud providers, EHR vendors, certificate authorities, network operators, and other third parties can change infrastructure and trust relationships independently.
The real design question is therefore not which cryptographic primitive to choose. It is which security assumptions are controlled by the manufacturer, which are delegated to third parties, how those assumptions can change over the device lifecycle, and what happens when they fail.
Which parties are trusted with plaintext? Which only need a verifiable claim? Which computations must remain confidential even from the infrastructure executing them? What happens when a key is compromised, a device is offline, hardware degrades, or a security update conflicts with clinical safety?
A long-lived medical device needs more than cryptographic agility. It needs a security architecture that can evolve without turning a change in the security environment into a change in patient safety.
The future is bright. It's also challenging.
