Originally published on LinkedIn. Lightly edited for clarity.
Fully homomorphic encryption (FHE) is one of the few cryptographic ideas that actually changes what is possible.
It allows computation on data that never becomes plaintext to the compute environment. For AI workloads, that promise is obvious: you could run models on sensitive data without exposing the input.
What FHE enables
FHE lets you process encrypted inputs and produce encrypted outputs. The data stays encrypted in memory and at rest.
In theory, you can run inference on protected data without trusting the host.
The practical constraints
The tradeoffs are significant:
- Performance overhead. Computation is much slower than plaintext operations.
- Limited operations. Not every model or function translates cleanly to FHE-friendly math.
- Model design changes. You often need to simplify or restructure models to work under FHE constraints.
That does not make it unusable, but it does constrain where it makes sense.
Where it makes sense today
FHE is most practical when:
- The data is extremely sensitive.
- The computation is narrow and well-defined.
- Latency is less important than confidentiality.
For broad, real-time AI systems, FHE is still a research and design constraint rather than a default setting.
The security model still matters
FHE reduces exposure of the data, but it does not solve every problem.
You still need:
- Integrity checks to prevent tampering.
- Access controls around the model and outputs.
- Auditability for how outputs are used.
The encryption is a powerful tool, not a complete system.
2026 Perspective
Tooling and libraries have improved, but the fundamental tradeoffs remain.
FHE is viable in narrow, high-sensitivity workflows, and that is an important slice of the world. It is still not a universal answer for AI security, but it is no longer theoretical either.