Zero-Knowledge Proofs Beyond Crypto: How ZK Tech Is Moving Into Enterprise Privacy

Zero-knowledge proofs spent their first decade as a blockchain curiosity — the cryptographic machinery behind Zcash's shielded transactions and Ethereum's ZK-rollups. That framing is now obsolete. Enterprise deployments of ZK proofs in identity verification, regulatory compliance, and cross-organization data sharing are live in production at companies including HSBC, ING, and Siemens, using ZK infrastructure that has nothing to do with cryptocurrency.

The shift is driven by a specific problem that organizations face when they need to share sensitive information: proving something is true without revealing the underlying data. This is exactly the problem ZK proofs were designed to solve, and it turns out to be far more common in enterprise contexts than in blockchain applications.

What Zero-Knowledge Proofs Actually Prove

A zero-knowledge proof allows a prover to convince a verifier that a statement is true without revealing any information beyond the truth of that statement itself. The canonical example: proving you know a password without sending the password. The enterprise translation: proving a customer's income exceeds a threshold without revealing the actual income figure; proving a supplier's carbon emissions fall within a regulatory band without disclosing their full emissions data; proving a user is over 18 without disclosing their birthdate.

Modern ZK proof systems — specifically zk-SNARKs (Succinct Non-interactive Arguments of Knowledge) and zk-STARKs (Scalable Transparent Arguments of Knowledge) — can prove arbitrary computations, not just simple statements. A ZK proof can verify that a machine learning model's output was computed correctly on private data, that a financial transaction complies with sanctions screening rules, or that a document was signed by an authorized party — all without revealing the inputs to the computation.

Identity Verification Without Data Sharing

The identity sector has the most mature enterprise ZK deployments. Traditional Know Your Customer (KYC) processes require organizations to collect, store, and share raw personal data — passport scans, utility bills, tax documents — creating data liability and GDPR exposure at every handoff. ZK-based identity systems flip this: the user proves attributes about themselves to a trusted issuer (a government, a bank, an employer), receives a ZK credential, and then presents proofs derived from that credential to third parties without the third party ever seeing the underlying data.

Polygon ID, built on ZK technology originally developed for Ethereum, has been adopted by several European financial institutions for exactly this use case. The Dutch bank ING piloted a ZK-based age verification system in 2024 that reduced GDPR data retention obligations by eliminating the need to store birth dates — the proof replaced the data. Idemia, one of the largest identity document companies in the world, launched a ZK credential layer for government-issued digital IDs in 2025 that is now deployed in three EU member states.

Compliance Proofs: Auditing Without Exposure

Regulatory compliance creates a perennial tension: regulators need to verify that companies follow rules, but companies cannot always share the underlying data without harming competitive positions or violating client confidentiality. ZK proofs offer a resolution.

HSBC and ING collaborated on a proof-of-concept for ZK-based trade finance compliance in 2023, demonstrating that a bank could prove to a regulator that a transaction complies with sanctions screening without revealing the counterparty details. The proof system used a ZK circuit built on top of Groth16 (an efficient zk-SNARK scheme) that encoded the OFAC sanctions list as a Merkle tree; the proof verifies membership or non-membership in the list without exposing the transaction.

Siemens deployed a ZK-based system in 2024 for supply chain emissions reporting under the EU Corporate Sustainability Reporting Directive (CSRD). Suppliers submit ZK proofs that their emissions data falls within reported ranges, satisfying Siemens' reporting obligations without requiring suppliers to share raw emissions figures that could reveal production volumes or manufacturing processes.

The Technical Maturity Gap — and How It's Closing

Three years ago, ZK proofs were impractical for most enterprise deployments because proof generation was prohibitively slow. Generating a proof for a complex computation could take minutes to hours on commodity hardware. This has changed dramatically:

Hardware acceleration: Ingonyama, Cysic, and Ulvetanna have built application-specific integrated circuits (ASICs) and FPGAs optimized for ZK proof generation. Cysic's ZK accelerator chip, sampling in 2025, achieves proof generation speeds 100–1000x faster than general-purpose GPUs for specific proof systems.
Recursive proofs: Proof systems like Halo2 and Nova support recursive composition, where one proof verifies another proof. This enables batching: instead of generating and verifying thousands of individual proofs, you generate one proof that attests to the validity of all of them. Aztec's Honk proving system uses this to reduce verification costs by 90%+ for batch operations.
Developer tooling: Circom and Noir (developed by Aztec) are circuit programming languages that abstract ZK mathematics into programmable constraints. An engineer who has never studied cryptography can write a Noir program specifying what needs to be proved, and the compiler generates the underlying ZK circuit. This has dramatically lowered the skill barrier for enterprise ZK deployment.

Data Sharing and Federated Analytics

Perhaps the least-discussed but most commercially significant application is cross-organizational data analytics. Companies frequently want to compute statistics across datasets they cannot legally or competitively share. ZK proofs, combined with multi-party computation (MPC), enable this.

Google and Meta have separately invested in privacy-preserving analytics infrastructure using ZK and MPC techniques. A consortium of European banks — organized under the European Banking Authority's sandbox program — ran a pilot in 2025 where five banks computed shared fraud detection statistics across their transaction data using ZK-verified MPC, without any bank seeing another's raw transaction records. The detection rate for cross-bank fraud patterns improved by 23% compared to siloed models.

What Enterprise ZK Does Not Yet Solve

ZK proofs verify computation, not data provenance. They can prove that a computation was performed correctly on claimed inputs, but they cannot prove that the inputs themselves were accurate. If a company submits false emissions data to generate a ZK proof of compliance, the proof will verify correctly — it proves the computation, not the truth of the inputs. This is the "garbage in, garbage out" problem, and it requires complementary mechanisms: oracle networks, IoT sensor attestation, or audited input certification.

Proof generation costs also remain non-trivial. Even with hardware acceleration, generating a proof for a complex computation costs real time and money. For high-frequency, low-latency applications, ZK proofs are still often impractical without significant infrastructure investment.

Actionable Takeaways

If your organization handles KYC or age verification, evaluate ZK credential systems (Polygon ID, Idemia's digital ID layer) as a path to reducing GDPR data retention liability — the technology is production-ready in this use case.
For compliance reporting under CSRD or similar frameworks, ZK-based proof submission is actively being piloted with regulators in the EU — engage with your industry association to understand the timelines.
Engineering teams wanting to experiment with ZK can start with Noir (docs at noir-lang.org) — it requires no prior cryptography knowledge and has active enterprise tooling support.
Do not conflate ZK proof integrity with input data accuracy — any enterprise deployment needs a verified data ingestion layer before the ZK circuit, or the proofs are only as trustworthy as the people submitting the inputs.
Hardware acceleration is narrowing the proof generation bottleneck rapidly; if a ZK use case was impractical 18 months ago due to latency, it is worth re-evaluating now.