Confidential Computing Protecting Data in Use

Data in use is the phase when information is processed by a program. It is often the most exposed state, because data travels through memory, CPU, and software paths. Confidential computing combines hardware and software to protect data and code during this processing. By running work inside protected enclaves, it keeps inputs, outputs, and even the processing rules private from the host system.

Why this matters

  • In healthcare or finance, analysts need to run queries or models without exposing personal data.
  • In multi‑party collaboration, teams can share insights without revealing raw inputs.
  • In regulated industries, confidential computing helps meet privacy requirements while still gaining value from data.

How it works

  • Trusted execution environments create an isolated area for computation. Data inside the enclave stays encrypted and inaccessible to other software on the host.
  • Remote attestation lets you verify that the right software is running inside the enclave before data is released.
  • Memory encryption and sealing bind keys to the hardware, so data can be recovered only on trusted devices or sessions.

Key technologies

  • TEEs and enclaves, such as secure memory regions within CPUs.
  • Remote attestation to prove software integrity.
  • Memory encryption and sealing to protect data at rest inside the enclave.
  • Some solutions extend to secure multi‑party computation and protected APIs.

Cloud options

  • AWS Nitro Enclaves isolates processing in dedicated hardware for sensitive tasks.
  • Azure Confidential Computing protects data in use with trusted hardware and services.
  • Google Confidential VMs and confidential containers bring the same ideas to cloud workloads. Each option provides core guarantees: isolation, attestation, and controlled data flow.

Getting started

  • Map your data flows and identify where data in use occurs.
  • Choose a confidential computing option that fits your workload and compliance needs.
  • Do a small pilot, enable attestation, and validate inputs and outputs.
  • Use trusted libraries, monitor performance, and plan for side‑channel awareness.

Limitations

  • Confidential computing adds complexity and potential performance overhead.
  • It does not eliminate all risks; misconfigurations or weak keys can still leak data.
  • It should be part of a broader security strategy with strong access controls and encryption at rest.

Key Takeaways

  • Data in use is protected by isolating computation in trusted environments.
  • TEEs, enclaves, and remote attestation provide isolation and verification during processing.
  • Start with a small pilot, assess tradeoffs, and integrate with broader data‑security practices.