Homomorphic Encryption Explained — And Why Most Teams Don't Actually Need It

Picture this: your team is in a meeting about data privacy and compliance.

An executive brings up homomorphic encryption and asks whether it can unlock your sensitive-data AI project. Before you answer, it’s worth understanding exactly what homomorphic encryption promises, what it costs, and what the alternatives do instead.

This post is not a dismissal of homomorphic encryption. It’s a map for when to use it — and when a different tool gets you there faster.

What homomorphic encryption actually is

Homomorphic encryption (HE) is a class of encryption schemes that allow computation on ciphertext. The results, when decrypted, match the results you would have gotten computing on plaintext.

Fully homomorphic encryption (FHE) is the most ambitious variant: it supports any computation, not just a fixed set of operations. When Craig Gentry first proposed FHE in 2009, it was immediately recognized as a theoretical breakthrough — and immediately recognized as a million times too slow for commercial use.

The intervening fifteen years have seen enormous progress: custom FHE compilers, hardware acceleration, new scheme variants (CKKS, BGV, BFV, TFHE). The gap has narrowed. FHE is no longer purely academic.

But narrowed is not closed.

The cost of FHE today

Two numbers summarize the state of FHE in 2024:

Performance overhead. An optimized FHE program still requires thousands of times the compute resources of an equivalent unencrypted program. For real-time, software-embedded applications — analytics dashboards, fraud detection pipelines, LLM inference — this overhead makes FHE impractical.

Tooling immaturity. Despite progress on FHE compilers and open-source libraries, no single framework has emerged as a production-ready standard for general-purpose software. Integration complexity is high, and growing that integration into existing data stacks remains a significant engineering investment.

In practice: operations that take 4–24 hours with FHE can complete in 50 seconds on 1M records using symmetric encryption-in-use approaches (Naive Bayes, 6 features → 1 target — a representative fraud-detection workload). That is not a rounding error. That is the difference between a batch job and a real-time system.

When FHE is the right choice

FHE is not going away, and for specific use cases it remains the best tool available:

• Multi-party computation where no single party should hold keys. Cross-institutional research (clinical trials, financial crime detection across banks) where even a trusted intermediary is legally or politically unacceptable.
• Long-running batch analytics where latency doesn’t matter. If you’re computing aggregate statistics overnight and have the infrastructure, FHE’s overhead may be acceptable.
• Regulatory environments that explicitly require Turing-complete computation on ciphertext. Rare, but they exist in some government and defense contexts.

If your use case fits any of these, FHE is worth the investment. If it doesn’t, you are likely paying FHE’s overhead for properties you don’t need.

What most regulated-data AI projects actually need

The majority of real-time, software-embedded data workloads need three things:

1. Encrypted queries. Range queries, keyword search, aggregates — on data that stays encrypted at rest and in use.
2. Machine learning on sensitive data. Train models on encrypted data — logistic regression, Naive Bayes, gradient boosting — with zero accuracy loss versus plaintext.
3. LLM inference on regulated data. Run LLMs on encrypted data so neither the model provider nor the infrastructure layer ever sees plaintext records.

These workloads don’t require FHE’s Turing-completeness. They require fast symmetric encryption-in-use: FIPS-compliant AES-GCM-256, database-agnostic, sub-second at scale.

The practical comparison looks like this:

	Fully Homomorphic Encryption	Encryption-in-use (symmetric)
Computation on ciphertext	Any computation	Queries, analytics, ML, LLM inference
Performance	4–24 hours for typical workloads	50 seconds on 1M records
FIPS / NIST certification	No (research-grade schemes)	Yes (AES-GCM-256, FIPS 140-3)
Production readiness	High integration complexity	SDK, REST API, database-agnostic
Regulatory acceptance	Limited (no FIPS standard)	DORA Art. 9(2), GDPR, HIPAA, SOC 2
Right for	Multi-party, no-trusted-intermediary scenarios	Real-time AI + analytics on regulated data

Real-world impact

Consider the workload: a financial services team needs fraud detection on encrypted customer transaction records. Legal has blocked the project because the ML pipeline requires decrypting data for training.

With encryption-in-use, that team can train the model directly on encrypted data — 1M records, Naive Bayes, 6 features to 1 target, 50 seconds, zero accuracy loss versus the plaintext baseline. Legal’s objection disappears. The pipeline ships.

That same team can later connect an LLM to query encrypted records for case investigation, running LLM inference on encrypted data without ever exposing plaintext to the model provider.

Patrick McKinney, VP of Security and IT at Invisible Technologies, described what this unlocks: “I put Blind Insight on the roadmap because it’s the first time I’ve seen query performance on encrypted data that’s actually usable in production. The approach is spot-on.”

None of this requires FHE. It requires the right tool for the actual job.

The analogy that holds

FHE is like a cruise liner: everything you could ever need, built to do anything, genuinely impressive — and slow. Encryption-in-use is like a catamaran: purpose-built, fast, and designed for the routes you actually sail.

Most business AI projects are sailing to specific destinations: train a fraud model, run LLM queries on patient records, analyze encrypted customer behavior in real time. The cruise liner can get you there. But it will take overnight, and it’ll burn more fuel than you have.

So the next time an executive proposes fully homomorphic encryption as the solution to your data access problem, you now have a more complete map. FHE is powerful. For real-time AI on regulated data, it’s also the wrong tool — unless your use case genuinely requires computation no intermediary should ever see.

For everything else, the encrypted data platform is already running.