zkML – How Zero-Knowledge Proofs Enable Private AI in Crypto September, 2025

Zero-knowledge machine learning (zkML) combines cryptography and AI to deliver verifiable outputs without exposing sensitive data. This guide explores how zkML works, its use cases, benefits, challenges, and future in Web3.

Last updated Sep 29, 2025
16 minute read
AI
Written by Nikolas Sargeant

Artificial intelligence increasingly powers decisions online, while blockchains make trust explicit through verifiable records. Bringing these together opens the door to AI whose outputs can be proven.

Privacy is the missing piece. Training data can be sensitive, models are valuable intellectual property, and many AI applications rely on personal inputs. Public blockchains are transparent by default, which conflicts with these needs.

Zero-knowledge proofs (ZKPs) offer a solution. They let someone prove that a computation was done correctly without exposing the underlying data or model. When applied to machine learning, this becomes zkML: zero-knowledge machine learning. It allows verification that a model produced a result from specific inputs without revealing the data or the model itself.

This article explains zkML, why it matters, and how it works in simple terms. We will look at use cases across finance, identity, oracles, NFTs, and governance, explore advantages and limitations, highlight projects building in this space, and consider what the future may hold.

What is zkML?

Zero-knowledge machine learning, or zkML, is the combination of two technologies that at first seem unrelated: zero-knowledge proofs and machine learning. Each plays a distinct role, and understanding them separately makes it easier to see why their union is so powerful.

Zero-knowledge proofs (ZKPs) are a cryptographic method that allow one party to prove that a statement is true without revealing the underlying information. Imagine proving you are over 18 without showing your date of birth, or proving you have enough funds for a purchase without displaying your entire bank balance. The key idea is verification without disclosure.

Machine learning (ML) is the branch of artificial intelligence where algorithms learn patterns from data in order to make predictions or decisions. Models can forecast price trends, detect fraudulent transactions, or analyze language. These models are often trained on sensitive datasets, and the models themselves are treated as valuable intellectual property.

When combined, zkML makes it possible to prove that a machine learning model generated a particular result from specific inputs without revealing either the inputs or the inner workings of the model. A simple analogy is baking a cake: zkML allows you to prove you followed the recipe and used the right ingredients without sharing the recipe itself or handing over the raw materials.

In practical terms, zkML creates a way for blockchain systems to interact with AI outputs with mathematical certainty. Developers can offer the benefits of their models without exposing trade secrets, users can receive AI-driven insights without giving up private data, and blockchains can verify the outcome without rerunning resource-intensive computations. It is a technical bridge that connects the power of AI with the trust and transparency of decentralized networks.

The value of zkML becomes clear once you look at the specific problems it is designed to address. Artificial intelligence and blockchain both hold enormous promise, but bringing them together introduces challenges that traditional tools cannot resolve.

Machine learning models rely on data, and much of that data is private or commercially sensitive.

  • In finance, it could be transaction records or credit scores.
  • In healthcare, it might be medical histories or lab results. You 
  • For social applications, it can include personal identifiers or communication logs.

Blockchains, however, are transparent by default. Exposing this information on-chain would be unacceptable for individuals and businesses alike.

Trust is the foundation of blockchain systems, but AI outputs are not inherently verifiable.

  • If an AI model says “approve this loan” or “flag this wallet as suspicious,” how can others confirm the result?
  • Without verifiability, AI-driven outputs are little more than black-box guesses, which undermines the principle of trustless systems.

Even if data is handled carefully, there is the problem of ensuring the right model is being used.

  • A compromised or altered model could produce false signals.
  • In DeFi, this could mean lending platforms mispricing risk.
  • In governance, it might lead to biased or manipulated recommendations.

Running AI models on-chain is impractical.

  • Models can contain millions of parameters.
  • Recomputing results just to check them would be too costly and slow.
  • Networks need lightweight ways to confirm outputs without repeating the entire computation.

zkML provides answers to all of these problems. It safeguards privacy, enables mathematical verification of results, proves that the correct model was used, and allows for efficient validation on-chain. With these capabilities, zkML makes it possible for AI to be a trusted building block in decentralized systems.

The mechanics of zkML can seem complex, but the overall flow is straightforward once broken into stages. At a high level, zkML is about proving that a machine learning inference was done correctly and then verifying that proof on-chain.

The model is created and trained off-chain, just like in conventional machine learning.

  • Developers use sensitive datasets or proprietary techniques.
  • The model’s weights and structure remain private intellectual property.
  • zkML does not change the training process itself, but it ensures the resulting model can be used securely in decentralized systems.

Once trained, the model can make predictions or decisions.

  • Inputs are provided to the model (for example, a credit application, a transaction history, or an image).
  • The model produces an output, such as an approval, a fraud flag, or a classification.
  • This inference step also happens off-chain or in a hybrid environment, since running full models directly on blockchain nodes is not practical.

Here is where zero-knowledge cryptography comes into play.

  • A specialized circuit is built that represents the computation of the model.
  • The model’s execution on a given input generates not only the output but also a cryptographic proof.
  • The proof certifies that “this output truly came from this model, given this input” without revealing the model weights or the raw input data.

The generated proof is sent to the blockchain.

  • Smart contracts can verify the proof quickly and with minimal computation.
  • This means anyone can confirm the output is genuine without rerunning the model.
  • Verification is efficient, making zkML suitable for decentralized systems where cost and speed matter.

Several projects are building the infrastructure to make zkML practical:

  • zkSync and Aleo are integrating ZK circuits that support complex computations.
  • RISC Zero provides a zkVM (zero-knowledge virtual machine) capable of proving general-purpose code execution.
  • Emerging zkML libraries focus specifically on translating ML models into verifiable ZK circuits.

Despite its promise, zkML still faces hurdles:

  • Computational overhead: generating proofs for large models can be resource-intensive.
  • Model size limits: most current zkML frameworks can only handle small to medium-sized models efficiently.
  • Speed trade-offs: proof generation is still slower than traditional inference.

In essence, zkML shifts the heavy lifting off-chain but brings verification on-chain. The model runs where it is efficient and private, while the blockchain provides trust and transparency through proofs. This architecture allows AI and decentralized systems to complement one another without compromise.

zkML is more than a technical curiosity. It unlocks specific, high-value applications across the crypto ecosystem where privacy, verifiability, and efficiency are essential.

Credit and lending platforms often need to evaluate whether a user is trustworthy.

  • zkML allows platforms to run credit risk models on sensitive financial histories without exposing raw data.
  • Borrowers prove they meet requirements without revealing account balances or income sources.
  • Lenders can rely on the outputs with cryptographic certainty, reducing default risk without compromising privacy.

Oracles bridge off-chain data into blockchain systems, but they are vulnerable to manipulation.

  • With zkML, AI models can filter, aggregate, or interpret raw data feeds before they hit the blockchain.
  • A proof can then show the oracle data was processed correctly, even if the underlying sources are hidden.
  • This strengthens DeFi platforms that rely on external prices, weather data, or supply chain inputs.

The value of NFTs often depends on rarity or subjective traits.

  • Machine learning models can evaluate rarity and predict market value.
  • zkML ensures these assessments are provably correct without revealing the proprietary valuation model.
  • Collectors and marketplaces gain transparency while creators protect their algorithms.
  • Identity and KYC

Know-your-customer checks are a sticking point for many decentralized applications.

  • zkML enables “proof of verification” without disclosing personal documents.
  • A user can prove they passed a KYC check by an accredited service without sharing passport scans or addresses.
  • This balances regulatory requirements with user privacy.

Anomaly detection models are widely used to flag suspicious behavior.

  • Exchanges or DeFi platforms can use zkML to verify that a flagged transaction truly triggered the model.
  • Regulators can be satisfied that checks are in place without gaining access to private user data.
  • This approach reduces false positives and builds trust with authorities.

Decentralized organizations make collective decisions, often based on complex proposals.

  • AI can analyze proposals and suggest outcomes.
  • zkML ensures that the analysis is genuine and unbiased, without exposing internal logic that could be gamed.
  • Members can vote with more confidence when recommendations are verifiable.

These use cases illustrate the range of applications where zkML can add value: finance, data integrity, digital assets, identity, compliance, and governance. The common thread is that all require trust without full disclosure a balance that zkML is uniquely equipped to deliver.

The promise of zkML lies in how it reshapes the relationship between AI and blockchain. By combining verifiability with privacy, it brings a set of benefits that neither technology could achieve alone.

Blockchains thrive on trustless verification, but AI has long been a black box. zkML changes this dynamic.

  • Outputs can be mathematically proven correct.
  • Users no longer need to take the model creator’s word for it.
  • This trust in AI decisions expands the range of applications suitable for decentralized systems.

Sensitive data and proprietary models remain secure.

  • Users can engage with AI without revealing personal information.
  • Developers can share the utility of their models without exposing valuable intellectual property.
  • This dual protection solves one of the biggest barriers to AI adoption in crypto environments.

Re-running AI models on-chain would be computationally impossible. zkML sidesteps the issue.

  • Proofs are lightweight compared to full model executions.
  • Verification can happen quickly and at low cost.
  • This makes AI integration feasible even on networks with limited block space.

As crypto matures, regulators demand stronger compliance. zkML can support this by proving checks have been done without revealing private details.

  • KYC verification without disclosing documents.
  • Proof of compliance without exposing user-level data.
  • Auditability that satisfies regulators while maintaining decentralization.

In short, zkML combines trust, privacy, efficiency, and compliance in a single framework. These advantages are why many believe it will become a standard component of future blockchain ecosystems.

For all its promise, zkML is still an early-stage technology, and it faces hurdles that limit how widely it can be applied today. The most pressing issue is computational intensity. Generating proofs for machine learning models requires significant resources, far more than the cost of running the models themselves. This creates a trade-off between security and usability, where the benefits of verifiability must be weighed against the overhead of proof generation.

Model complexity is another obstacle. Most real-world machine learning models, particularly deep neural networks, are massive. zkML frameworks can currently handle only relatively small or simplified versions. This means that while zkML can demonstrate its value in areas like anomaly detection or basic scoring systems, it is not yet practical for very large models used in advanced natural language processing or image recognition.

There is also the matter of speed. Proof generation is slower than traditional inference, and in fast-moving environments like trading or high-frequency transactions, this delay could undermine the utility of the system. Developers are working on optimizations, but at present the technology is best suited to use cases where time sensitivity is lower.

Finally, the ecosystem itself needs standardization. Different zkML frameworks are emerging, each with its own approach to circuits, verification, and integration. Without common standards, interoperability will be limited, and adoption could fragment across incompatible tools. The field will require collaboration between cryptographers, AI researchers, and blockchain developers to reach maturity.

In short, zkML offers a glimpse of a powerful future, but its current form is constrained by performance, complexity, and coordination challenges. Recognizing these limitations is key to setting realistic expectations for the near term.

zkML is still young, but a handful of pioneering projects are already laying the groundwork. These initiatives show how zero-knowledge machine learning can move from theory into practice.

Aleo is building a privacy-first blockchain that supports zero-knowledge proofs at the base layer. It allows developers to write applications in Leo, a custom programming language designed for ZK circuits. Aleo’s infrastructure makes it possible to integrate zkML models into decentralized apps, proving computations while keeping data hidden.

RISC Zero provides a zero-knowledge virtual machine that can generate proofs for general-purpose code. This means developers can write machine learning code in familiar languages, run it, and produce proofs that are verifiable on-chain. It bridges the gap between traditional ML development and the cryptographic requirements of zkML.

zkSync focuses on scalability and low-cost transactions through zero-knowledge rollups. While its core mission is throughput, the underlying technology supports zk-based verification that can extend to zkML. By combining efficiency with verifiability, zkSync offers a potential home for AI-driven applications that require proof of correctness.

Modulus Labs is a dedicated zkML project aiming to prove machine learning inferences with zero-knowledge proofs. Their work includes building specialized circuits optimized for neural networks and demonstrating zkML in applications such as fraud detection and automated decision-making.

Giza is a framework for deploying machine learning models on-chain using Cairo, the programming language of StarkNet. It focuses on converting existing ML models into formats compatible with zero-knowledge systems, making zkML more accessible to developers who already work with AI.

Worldcoin has attracted attention with its controversial biometric identity system. While not purely zkML, it highlights the intersection of AI, cryptography, and privacy. Its use of zero-knowledge proofs for identity verification points toward the kinds of large-scale applications where zkML could become vital.

Taken together, these projects illustrate the range of approaches: dedicated zkML platforms, general-purpose zkVMs, scaling solutions with zk support, and application-specific experiments. The ecosystem is fragmented but rapidly evolving, and the lessons learned in these early pilots will shape how zkML is adopted across the broader crypto landscape.

Although zkML is still experimental, its trajectory points toward becoming a core building block for Web3. Over the next few years, we are likely to see the technology evolve along several key paths.

AI agents that can act independently in decentralized systems are gaining momentum. With zkML, these agents could prove their reasoning processes and decisions on-chain. A trading agent, for example, could show that it followed a risk policy without revealing its full strategy. This blend of autonomy and accountability could change how DAOs and DeFi protocols operate.

As financial regulators push for greater oversight of crypto, zkML may provide a middle ground. Institutions could prove compliance checks were performed without disclosing sensitive customer data. This approach aligns with both regulatory goals and the decentralization ethos, offering a way to bridge two often conflicting worlds.

Federated learning allows multiple parties to train a shared model without pooling their data. Combining it with zkML would mean that each participant can prove their contribution was valid, while still keeping local data private. This could be especially powerful in industries like healthcare or finance, where data privacy is paramount.

Today’s zkML implementations are constrained by computational limits, but hardware and cryptographic research are advancing quickly. As proof generation becomes faster and circuits more efficient, zkML could handle larger neural networks. This would open the door to applying verifiable AI in areas like language models, image recognition, and advanced forecasting.

Finally, zkML may evolve into a shared layer that spans multiple blockchains. Just as cross-chain bridges connect liquidity today, zkML services could provide verifiable AI outputs across different ecosystems. This would make AI a universal service in Web3, not tied to a single chain or protocol.

Zero-knowledge machine learning is still in its infancy, but it already points to a future where artificial intelligence and blockchain can work together without compromising privacy or trust. By allowing AI outputs to be verified on-chain without exposing sensitive data or proprietary models, zkML bridges the gap between transparency and confidentiality.

The technology addresses the main roadblocks that have held back deeper AI integration in decentralized systems: it safeguards user privacy, protects developer intellectual property, ensures outputs are correct, and does so in a way that blockchains can efficiently verify. These qualities make it an essential building block for applications in finance, identity, governance, and beyond.

Challenges remain, proof generation is resource-intensive, large models are difficult to handle, and the ecosystem is still fragmented. But progress is rapid, and early projects are demonstrating what is possible. If current trends continue, zkML could become a standard primitive across Web3, much like zero-knowledge rollups are today for scaling.

For developers, investors, and users, the key takeaway is simple: zkML is not just another technical acronym. It is a path toward AI systems that can be trusted, audited, and integrated into decentralized environments without exposing what should remain private. As Web3 evolves, zkML is poised to become one of the foundations on which secure, intelligent, and privacy-preserving applications are built.