What is Zama?

End-to-end encryption changed how we communicate. Apps like WhatsApp made it normal for messages to stay private – readable only by the sender and receiver. But outside messaging, most digital systems still don’t work this way.

AI systems process highly sensitive information, from medical history to financial patterns. Public blockchains, meanwhile, reveal every transaction and balance to the entire network. Your identity may be hidden, but your activity isn’t. 

Other digital services, like standard cloud systems, encrypt your data to keep hackers out, but the service providers themselves retain a master key. To run applications, they must continually decrypt your information .

This creates a clear tension: we keep handing over private data to systems that offer limited privacy in return.

Enter Zama – a $1B cryptography company extending the logic of end-to-end encryption to computation itself. Instead of decrypting data to use it, Zama’s technology lets AI models and smart contracts operate on information that stays encrypted from start to finish.

The moment data is decrypted, even for a split second, it becomes exposed to servers, cloud providers, employees, malware, or anyone who breaches the system. That single moment is where almost every major privacy leak, AI data breach, cloud compromise, and blockchain exploit happens today. 

By removing that exposure window entirely, Zama eliminates the most dangerous part of the process: the part where your supposedly “private” data is actually sitting in plaintext somewhere, waiting to be misused.

So, is the era of complete digital privacy finally within reach?

Today, we’ll look at how Zama works, why it matters, and how Fully Homomorphic Encryption (FHE) could bring real privacy to the AI and blockchain infrastructures we rely on.

What is Zama?

Zama is the first $1B unicorn open-source cryptography company aiming to make fully homomorphic encryption practical. In layman’s terms, this means allowing computations to be performed on encrypted data, without decrypting it first and offering the same result as if it had been unencrypted. 

For example, with FHE, a bank could run fraud-detection models on your fully encrypted transaction history, catching suspicious activity the moment it occurs, without ever seeing where you’ve spent your money or other private financial data. 

Zama is perhaps the first company at this scale, to commercialise a full-stack FHE platform available to developers and enterprises. Co-founded by CEO Rand Hindi (serial AI entrepreneur) and Chief Scientist Pascal Paillier (inventor of the Paillier cryptosystem, FHE pioneer), the Paris-Zug team blends 37 PhDs in crypto/ML with remote engineers from 26 countries with additional hiring underway through Zama careers.

Right now, the most valuable data in the world (medical records, institutional finance, crypto/DeFi activity, digital identity) is trapped in silos. It is “locked away” because the risk of exposing it to the cloud is simply too high, it would cost you your privacy. 

Zama’s innovative usage of FHE is the key to unlocking this value ethically and efficiently. 

With FHE, we no longer need to trust mega-corporations with our data. Hospitals could train AI on patient data with zero risk of exposure and financial records or identity attributes can be acknowledged without decrypting them, removing the risks of exposing any data.

So how does Zama achieve this?

How Does Zama Work?

Zama works by allowing computation on encrypted data, unlocking more complex functions for programs like AI models or smart contracts without revealing the underlying data. Under the hood, this is powered by three core components:

• TFHE encryption, 
• compilers that translate code into encrypted operations, and 
• runtimes that execute those operations without ever decrypting anything.

1. Torus Fully Homomorphic Encryption (TFHE) 

TFHE is a type of homomorphic encryption that breaks computations into tiny encrypted steps, like simple yes/no checks, or small arithmetic operations — all while keeping the data fully unreadable. By chaining together lots of these tiny encrypted steps, TFHE can run full programs, like running AI models or executing a smart contract, without ever exposing the underlying data.

To understand why this matters, it helps to compare TFHE to general Fully Homomorphic Encryption (FHE). 

Traditional FHE also lets you compute on encrypted data, but it usually works in larger, slower blocks of computation. These large encrypted operations accumulate “noise” quickly — a form of mathematical static that eventually makes the encrypted value unusable unless it’s refreshed.

 In many FHE schemes, that refresh step is extremely heavy, which is why FHE was long considered impractically slow. TFHE approaches this differently. 

Because all computations are broken into very small steps, the system can tightly control noise growth and refresh it more efficiently. Each operation still adds noise (for security), but TFHE includes a built-in mechanism called “bootstrapping” — a kind of encrypted “reset” that clears noise without ever decrypting the data.

Zama’s innovation lies in making this bootstrapping step significantly faster through optimized algorithms and parameter choices, enabling long, complex computations to run efficiently while data stays fully encrypted. 

For example; let’s say a hospital wants to run an AI model on encrypted medical scans. Each computation step adds a bit of cryptographic “noise” to the encrypted data. In most FHE systems, too much noise forces a decryption step — which would expose the patient’s scan. TFHE avoids this by “refreshing” the encrypted data without ever decrypting it.

2. Concrete – Compiling normal code into encrypted circuits

To make it easy for all developers to implement, Zama provides tools in popular crypto and non-crypto development languages such as Python, Rust and Solidity. Zama’s Concrete compiler then takes that code and translates it into a form that TFHE can run: a fully encrypted version of the program’s logic.

The developer doesn’t need to understand how the encryption works; the compiler generates the secure, FHE-ready version automatically. 

3.  The Runtimes: Server & fhEVM

After compilation, the encrypted program needs a place to run. Zama provides two specific runtimes for this:

• For AI: A high-speed server runtime that processes encrypted data for machine learning.
• For Blockchain: The fhEVM (Fully Homomorphic EVM), a specialized smart contract engine that allows Ethereum-compatible chains to process encrypted transaction data directly on-chain.

Zama’s runtime follows the encrypted instructions step by step, updating the encrypted data as it goes—without ever seeing or revealing what the data actually is. 

At every stage, servers and blockchain nodes work only with unreadable ciphertext, yet the final decrypted result for the user is no different from if they had shared the sensitive data with an external program. This means they get the same result, without sacrificing their privacy.

Why Zama Matters in AI & Crypto

The idea of computers working on encrypted data has existed for years, but was always too slow to be practical. Zama is the first to make that capability usable for real applications. It removes the most vulnerable point in modern computing: the moment we decrypt data to use it. There are a few key benefits of using Zama in blockchain and AI, such as:

Data Exposure During Processing

Normally, when your data is being processed by an AI model, (or cloud service, or a blockchain) someone has to see it in plain text. With Zama, the system only ever sees encrypted data, so your information stays private even while it’s in use.

Public Blockchains Leak Activity

On blockchains, anyone can watch your transactions and even predict or exploit your next move. By keeping contract inputs and balances encrypted, Zama prevents this kind of spying and front-running, unlocking private onchain activity.

Zama’s technology allows companies to experiment with encrypted analytics, where they study customer behaviour without actually accessing customer data. 

Today, companies hoard massive troves of “dark data” (customer habits, purchase patterns, or browsing trends), sitting unused in vaults because touching it risks leaks, lawsuits, or PR nightmares from breaches. 

With Zama, algorithms crunch encrypted versions of this data to spot trends, personalize recommendations, or predict sales, delivering the same sharp insights (like training a recommendation engine on hidden user behavior) while the raw info stays mathematically locked away.

AI Can’t Safely Use Sensitive Data Today

Hospitals, banks, and governments often can’t send private data (like medical records or credit scores) to external AI models because it violates privacy laws. With Zama, the data stays encrypted from start to finish, allowing the AI to process it without ever gaining access to the real values.

With Zama, an AI system can take encrypted medical inputs and return a diagnosis without ever seeing the raw scan. You can try it out yourself right now in Zama’s live demo: simply enter your symptoms, hit encrypt, and watch the model diagnose you while the server stays completely blind!

Future Use Cases of Zama

If the technology continues to scale and becomes mainstream, everyday digital life could look very different. 

Financial apps could verify your income or creditworthiness without ever accessing your salary details. DeFi platforms could match trades privately, eliminating MEV and protecting users from onchain surveillance. Hospitals could outsource AI diagnostics without patient records leaving the building. 

Even consumer apps—like assistants that sort your emails or recommend content—could operate without reading your messages in plaintext.

Zama’s product stack is built specifically to address these real-world problems and make privacy-preserving computation usable outside of research labs.

Zama Product Line 

From low-level primitives to blockchain integrations to AI tooling, Zama products form a complete ecosystem for building applications that remain end-to-end encrypted.

1. TFHE-rs: Low-level FHE library (Rust)

TFHE-rs is Zama’s core cryptographic library. It’s the cryptographic library that generates keys, handles encrypted values, and performs the bootstrapping steps needed to keep computations accurate. 

2. Concrete Framework: High-level FHE toolkit

Concrete is the higher-level toolkit built on top of that engine. Developers write normal Python or Rust code, and Concrete automatically converts it into a version that runs on encrypted data. This makes FHE usable without needing to understand any cryptography.

3. fhEVM: Encrypted smart contracts

The fhEVM is Zama’s adaptation of the Ethereum Virtual Machine that supports encrypted variables and operations. It allows Solidity developers to write smart contracts where balances, inputs, and state updates remain encrypted, while still being fully compatible with existing EVM infrastructures. 

4. Zama AI Tools: Privacy-preserving machine learning

Zama provides FHE-ready tools for running AI models on encrypted data. Developers can encrypt user inputs, run models entirely under FHE, and then decrypt only the final output. This unlocks private recommendation systems, medical-data inference, fraud-detection pipelines, and other sensitive workloads where data exposure is unacceptable.

The $ZAMA Crypto Token

Zama is building a confidentiality protocol that brings fully encrypted smart contracts to existing blockchains. Instead of creating a new L1, the protocol plugs into chains like Ethereum and adds a missing capability: the ability to process encrypted inputs, encrypted balances, and encrypted state updates just as easily as normal ones.

A network like this needs independent operators to verify encrypted inputs, run FHE computations, store ciphertexts, and perform threshold decryption. To coordinate and reward those operators, the protocol introduces a native asset: the $ZAMA token.

The token is used to pay for confidential operations (such as verifying proofs, decrypting results, or bridging encrypted assets), and operators stake it to secure the network and earn rewards. In short, $ZAMA is the economic layer that keeps a global encrypted-computation engine running.

The token isn’t live yet, and its real utility begins when the protocol mainnet launches. Until then, developers can already build private AI and encrypted smart contracts using Zama’s open-source tooling.

Zama vs ZK Proofs vs MPC

Before FHE, most privacy tools came with trade-offs.

Zero-knowledge Proofs

Zero-knowledge proofs let you prove something is true without revealing the data, but they cannot do the actual work that produces the result. Think of it like checking someone’s exam answers without ever seeing how they solved the questions. ZK proofs only confirm the answers are valid, but it cannot solve the problems for you.

Trusted Execution Environments (TEEs)

TEEs are special secure areas inside a processor where code can run privately. But for them to work, you must trust the company that built the chip. And while the code runs, your data does become visible inside that secure area, meaning privacy depends entirely on whether that hardware truly stays secure. If the chip is hacked, your data is exposed.

Multi-Party Computation (MPC)

MPC avoids a single point of failure by splitting your data into pieces and giving each piece to different parties. No one gets the full picture, but each party still sees a part of your information. This adds complexity and still requires trusting that none of the parties misbehave or collude.

TFHE doesn’t prove, assume, or split. It computes just like a normal computer, while the data stays encrypted the entire time. Zama’s approach is unique as it brings the full programmability of TEEs, the trustlessness of ZK, and the collaborative security of MPC all without inheriting their weaknesses.

Drawbacks of Zama

While Zama’s technology is promising, it isn’t ready to power large-scale, everyday applications yet. 

Fully Homomorphic Encryption is still far slower than normal computation, which means running AI models or smart contracts under FHE can take seconds or minutes instead of milliseconds. This makes real-time applications like trading systems or interactive apps hard to build today. 

Furthermore, most uses are still in the prototype or testnet stage, and developers often need to redesign their apps to fit within FHE’s performance limits. On blockchains, fhEVM is early and not yet supported across major tooling or wallets. And because the ecosystem is evolving quickly, teams must work with libraries that are still changing. 

Lastly, because FHE enables full data confidentiality, deployments in regulated industries (finance, healthcare, identity) will require clear compliance frameworks. This isn’t a blocker, but it does mean large institutions can’t adopt FHE widely until standards and regulatory guidance mature.

Conclusion

Zama’s Fully Homomorphic Encryption is built for the long-term future; for blockchains and AI that compute on encrypted data without ever seeing it. 

Ledger is already helping build that future as one of Zama’s institutional MPC operators securing its confidential blockchain. 

But you don’t have to wait. 

Today, your Ledger secure touchscreen signers are the gold standard in immediate, unbreakable digital identity protection. With the Security Key app, your signer supports passkeys, FIDO2, and WebAuthn logins. Crucially, private keys never leave the certified Secure Element chip, so every action requires your physical verification.

It renders phishing and server breaches useless, since no remote attacker can ever impersonate you. Secure your digital identity today with hardware that is already helping build the encrypted internet of tomorrow.

Frequently Asked Questions

Q: Is Zama a blockchain?

A: Zama is primarily a cryptography company and tooling provider; its protocol acts as a confidentiality layer compatible with existing blockchains rather than a single new L1 that replaces them.

Q: Does Zama have a token now?

A: No, but there is a $ZAMA crypto token planned for its confidentiality protocol coming soon.

Q: Can I run any ML model with FHE today?

A: Small to medium models (quantized, optimized) are practical in many cases. Large, unquantized models are still challenging due to compute and memory cost.