The Role of Confidential Computing in Defence
Proving Infrastructure Integrity in Untrusted Environments
Implicit trust in infrastructure operators, cloud providers, administrators, and operational processes becomes increasingly fragile in regulated and mission-critical environments. For example, in defence, where systems manage classified intelligence, satellite imagery, mission planning, and weapons platforms, implicit trust could be a liability.
Confidential computing changes this model. Instead of relying on people or processes, it uses hardware-backed cryptography to protect data that is in use. Even privileged administrators are significantly constrained from inspecting workload memory or modifying protected execution state without detection mechanisms such as attestation failures or integrity violations
The Risk: Data “in Use”
Critical programs already encrypt data:
- At rest (on storage)
- In transit (over networks)
But there’s a third state that often gets ignored: data in use.
Whenever software or AI models process information, the in-memory data is in plaintext. That’s when it’s most exposed.
If an attacker compromises the host, an admin account is compromised, or malware slips in, sensitive information can be accessed silently — without obvious traces.
For sensitive sectors like defence, this becomes a real operational weakness.
The Mitigation: Secure Enclaves
Confidential computing introduces Trusted Execution Environments (secure enclaves). These are protected areas inside modern CPUs where:
- Data stays encrypted even during processing
- Encryption keys never leave the hardware
- The system can cryptographically prove that only approved code and firmware are running
This proof, called attestation, lets you remotely verify that your workload is operating in a genuine secure environment before sending it any sensitive data.
Instead of implicit trust, you get explicit, cryptographically verifiable trust.
Note that like any security technologies, TEEs are not immune to implementation flaws or side-channel attacks, but they substantially reduce the trusted computing base and operational exposure.
Why This Matters for Defence?
1. Safely Process Classified Data in Sovereign Cloud
Imagine analysing classified satellite imagery using cloud-based AI.
With confidential computing, the raw imagery stays encrypted inside secure enclaves. The cloud provider is cryptographically constrained from directly accessing plaintext workload memory. Administrators can’t access it. Only the final results come back out.
Confidential computing and privacy-preserving technologies help enforce data sovereignty guarantees, even when processing data in the cloud.
2. Reducing Insider Risk
Some of the most difficult threats to defend against come from inside: privileged admins, contractors, or compromised supply chains.
Confidential computing limits the damage they can do.
Even with full system access, attackers only see encrypted memory. They can’t extract secrets or spy on workloads. The keys are tied directly to the processor hardware.
In effect, security no longer depends on trusting operators.
This same mechanism extends to software supply chain verification.
Using remote attestation, systems can cryptographically verify that only approved, unmodified software is running. Attestation allows a relying party to verify measurements of firmware, boot state, and workload identity before releasing sensitive data or cryptographic keys.
For example, before a drone is allowed to arm sensors, receive mission parameters, or connect to a command network, it can be required to attest that its flight software, AI models, and boot firmware exactly match an approved baseline.
If someone tampers with the software during manufacturing, updates, or deployment, the system automatically denies access due to a failed attestation.
3. Sovereign AI and Trusted Hardware
As defence organisations increasingly deploy AI for intelligence analysis, autonomous systems, and decision support, confidential computing also becomes an AI assurance mechanism. It enables protected model execution, controlled access to sensitive training data, and verifiable inference environments for both proprietary and mission-critical AI systems.
Most commercial confidential computing today runs on CPUs from Intel or AMD and GPUs from NVIDIA. These platforms support strong isolation and remote attestation. However there is a deeper question that you must ask:
How do I trust the hardware vendor itself?
Confidential computing offers two paths.
First: you can use Intel or AMD platforms with cryptographic attestation. Before any classified data is released, the CPU verifies which firmware and code are running. If anything looks wrong, the workload doesn’t start. Trust is minimised and continuously verified. But you have to trust the CPU vendor, i.e., Intel or AMD, here. Same goes GPUs, where you must trust the GPU vendor, i.e, NVIDIA.
Second (and particularly relevant for defence): ARM Confidential Compute Architecture (CCA) and emerging open architectures such as RISC-V confidential computing initiatives enable greater flexibility in building sovereign or domestically controlled trusted stack.
That means:
- Domestic silicon programs
- Trusted foundries
- Sovereign boot chains
- Full control over firmware and root of trust
Instead of trusting foreign hardware, agencies can build confidential computing platforms using infrastructure they control end to end. This approach enables true sovereign AI; running advanced models on classified data on nationally controlled hardware, with cryptographic proof that every step is secure.
4. Safer Collaboration Between Allies
Coalition operations depend on sharing intelligence, but no country wants to expose raw data.
Confidential computing enables a new model: multiple nations contribute encrypted datasets to a shared analysis environment. Everything operates within secure enclaves, and we reveal only approved results.
This enables policy-controlled federated analytics where participating parties retain control over raw intelligence while sharing only approved outputs or derived signals. Raw intelligence never leaves protected memory.
In commercial environments, we refer to this model as a data cleanroom.
Why Now?
It’s Becoming a Requirement, Not a Nice-to-Have
Zero-trust strategies increasingly emphasise verification over implicit trust. Confidential computing directly supports these goals by making security verifiable and measurable.
The Operational Viability Has Improved
A few years ago, this technology was niche, expensive, and the hardware was limited in availability. Today:
- Modern CPUs ships with production-grade TEE capabilities.
- Cloud providers now expose confidential VMs and confidential container platforms.
- Open-source attestation and orchestration tooling has matured significantly.
- Performance and operational overheads have improved.
Compared to the cost of a breach or intelligence compromise, the added expense is small.
Faster Deployment
Pre-integrated confidential computing stacks are also shrinking deployment timelines. What once took years can now take months, a big deal for defence modernisation programs.
Standardisation
Industry standardisation efforts through organisations such as the Confidential Computing Consortium are also helping improve interoperability and ecosystem maturity.
The Road Ahead
Confidential computing does not eliminate all classes of risk. Side-channel risks, implementation challenges, and evolving threats still exist — and require ongoing mitigation.
But it represents a fundamental shift: from operational trust assumptions toward continuously verifiable infrastructure integrity.
For defence and other sensitive domains, this moves security from policy and process into cryptography and hardware-enforced guarantees.
How to get started?
If you’re exploring confidential computing, this curated resource covers available open-source stacks, commercial offerings, and ecosystem projects:
If you’d like to discuss use cases, architectures, or trade-offs in more detail, feel free to reach out.
