The emergence of AI has turned data into a powerful strength, yet a potential liability. Adversarial inputs, poisoning, and hijacked AI agents are just a few of the numerous new threats confronting today's AI systems, especially in Web3, where fake context can release unauthorized actions. Enterprises need data verifiability and integrity urgently. Orochi Network fulfills the needs with the first-ever Verifiable Data Infrastructure by Zero-Knowledge Proofs, which secures every computation and data point on the AI market.
AI Security Issues in the Marketplace
AI systems face rising security and trust challenges as they scale across industries. Attackers exploit the unique traits of AI models and data in several key ways:
Data Poisoning: Malicious data injected during training can embed “backdoors” in AI models. These poisoned inputs often look normal, making attacks hard to detect. IBM reported in 2023 that data breaches cost $4.45M on average, with AI-heavy sectors like finance and healthcare hit hardest.
Prompt Injection: Generative models can be manipulated with crafted prompts, causing them to output false or sensitive information.
Context Manipulation (AI Agents): In Web3, attackers can alter an AI agent’s memory, tricking it into unauthorized actions like transferring funds, without breaching the blockchain itself.
Lack of Data Provenance: AI systems often lack verifiable data lineage. Deepfakes or tampered datasets can enter undetected, undermining trust and regulatory compliance.
Source: Abnormal
These risks lead to unpredictable behavior, especially dangerous in critical sectors. Researchers at Princeton showed that AI agents in DeFi could be manipulated through internal context alone—no hack required. Traditional infrastructure simply isn’t enough to make AI trustworthy at scale.
Orochi Network's Verifiable Data Infrastructure for AI
To solve these challenges, Orochi Network provides a Zero-Knowledge-based stack of data that guarantees any action and calculation over data is provable. Instead of depending on an AI dataset or model being complete, users are able to prove it cryptographically. zkDatabase, the world's first provable database for Web3 and AI, is at the core. zkDatabase acts like a traditional database or data store, but every write, read or update generates a ZK-SNARK proof attesting to its correctness. The result is a “Proof-of-Everything” framework where data provenance, verifiable, and integrity are guaranteed at every step.
The key benefits of Orochi’s approach
Verifiable Data Provenance
With zkDatabase, each piece of training data or inference input is cryptographically linked to an immutable history. As one practitioner explains, Orochi’s architecture ensures “deepfakes, copyright-infringing datasets or poisoned weights can’t slip in unnoticed; each artifact ships with an on-chain passport”. In practical terms, regulators or auditors can instantly verify that a dataset was built from approved, untampered sources, rather than trust it based on paperwork or reputation.
Data Security and Privacy
Orochi can uphold data privacy in ensuring its correctness. zkDatabase makes it possible for privacy-preserving proofs, enabling a company to demonstrate correctness without exposing raw data. This captures the demand for confidential computing in AI, whose examples include AI being used by hospitals on patient data, which would be capable of proving that they used legitimate health records without showing patient identities.
Lower Audit Costs
Traditionally, AI projects relied on audits or manual checks to ascertain whether data had changed. Orochi streamlines this in huge ways. Instead of verifying huge levels of data, you only validate a Zero-Knowledge Proof—something that takes milliseconds. This is time-saving, cost-effective, and makes it simple for AI projects to stay compliant.
Increased Scalability
In the background, zkDatabase is rooted in a distributed storage engine that natively supports ZK-data rollups. That is, it batches up lots of data operations in batch together with one proof, yielding much higher throughput. Through this, scalability is enabled such that enormous AI workloads (e.g., training data lakes or streaming sensor inputs) can be processed without central bottlenecks while maintaining verifiability.
Orochi Network’s engagement with the AI community is evident through strategic partnerships, particularly with OG Labs and BluWhale AI. The partnership with OG Labs focuses on integrating Zero-Knowledge (ZK) guarantees into AI workflows.
This collaboration fuses Orochi’s zkDatabase, Orand, and Orocle with OG Labs’ decentralized storage and compute layer, aiming to make AI models’ data, training, and inference cryptographically provable.
Orand - Verifiable Randomness for Fair AI
Many AI applications rely on randomness – e.g., random initialization of neural network weights, training validation separation, randomized algorithms, or differential privacy noise. The central source of randomness can be a point of bias or manipulation, however. Orand steps in to replace weak random number generator implementations based on cryptography with a high-throughput, trustless source of randomness backed by cryptography. It utilizes Elliptic-Curve Verifiable Random Functions (ECVRF) so proofs of unpredictability and correctness are provided along with each random number.
In applications of AI, Orand's characteristics yield tangible benefits:
Fairness and Non-Manipulation
Training and testing procedures that use randomness (e.g., shuffling or data splitting) are made explicit. Policymakers and developers can verify that the seed or random outcome wasn't manipulated by any party, which ensures algorithms remain unbiased and impartial.
Secure Privacy Techniques
Provable-randomness functionality underlies the majority of privacy-protecting techniques (e.g., differential privacy and secure multi-party computation). Through guarantees of provable entropy, Orand adds cryptographic assurances to such methods.
Cost-Smart Scaling
Orand allows batch submission of many randomness requests per epoch, saving on-chain costs without compromising security. For the OG Labs partnership, that means "cost-efficient and scalable" deployment of AI models.
The overall effect is that AI services on the Orochi network receive an openly verifiable source of randomness. To blockchain games or any AI service, this means randomness isn't a trust assumption but a provable proof – an integral aspect of a secure AI pipeline.
Orocle - Trusted Oracle for Real-World Data
AI models usually base themselves on external data (sensor readings, market prices, web data, etc.). Orocle solves this by offering a decentralized, tamper-proof oracle service for AI. In essence, Orocle posts real-world data on-chain in a way such that everyone can audit it and no one can censor.
Decentralization & Trust
There is no one party in control of the data feed. Data is aggregated and consolidated before it is released, so it cannot be arbitrarily fiddled with. For AI systems, this translates to training inputs or inference data from the outside world having high assurance.
Chain-Agnostic Integration
Orocle's architecture is blockchain-agnostic, and thus any AI DApp on any chain can subscribe. This is indispensable for multi-chain AI services or hybrid models connecting Web3 and legacy systems.
High Throughput and Flexibility. Orocle is built to process large volumes of data with little latency. It can sustain diverse use cases – from real-time market feeds in DeFi to weather information in supply-chain AI – without becoming a point of bottleneck.
zkVMs (Zero-Knowledge Virtual Machines)
These are zkVMs or blockchains specifically designed to run AI workloads and generate ZK proofs of computation. The idea is that the inference or training phase of an AI model can be executed on a zkVM and yield not only a result but also a proof in cryptography that it executed correctly on some input. The entire process can be audited like a blockchain transaction. Orochi’s zkVMs will target “L2-level gas fees,” meaning AI compute can scale cheaply while preserving trust.
zkDAL (Data Availability Layer)
This module provides effective, secure access to off-chain data sources. In effect, it enables any DApp to securely access an unlimited volume of external data under ZK-guardrails. For AI purposes, zkDAL could power real-time data ingestion (e.g., web scraping, IoT sensors) with proofs combined.
As an example, a developer can import a million records from a public dataset and receive a provable ledger of what was imported. Orochi describes zkDAL as making "seamless integration with external data sources" provable.
Together, zkVMs and zkDAL complete Orochi’s vision of “every workflow becomes cryptographically proven”. AI developers get a full-stack environment where data in, computation done, and data out are all backed by zero-knowledge proofs. This means models can be shared or sold with confidence that they weren’t trained on illicit data, results can be audited by regulators, and end-users can verify outputs without revealing raw details.
Partnerships and Real-World Impact
Orochi technology is already being implemented in AI-focused ventures. With its strategic collaboration with OG Labs, Orochi integrated its products into a "trust-first" AI stack. Key highlights of this collaboration are:
zkDatabase is coupled with OG Labs' IPFS/GPU platform to protect AI training data. This makes zk-ML models "trustworthy and verifiable" through the preservation of data authenticity at any stage in its lifecycle.
Source: OG Labs witter
In gaming, for instance, projects like Zypher are already using Orand and Orocle to make on-chain games fair and interactive. In finance, zkDatabase’s provable ledgers can eliminate the risk of fraudulent audit trails. Across industries, Orochi’s stack is being positioned as the foundation for “verifiable AI” in Web3.
Source: Orochi Twitter
Conclusion
At the heart of the future of AI lies trust, and Orochi Network delivers it: in data, in computation, and in results. By integrating Zero-Knowledge Proofs at every level of data handling and computation, Orochi provides AI applications with a verifiable basis. Companies can build AI solutions transparently and securely by design, from data collection to decision-making. In a world where "don't trust, verify" is the mantra of blockchain, Orochi brings the same promise to machine learning and AI. Its revolutionary products (zkDatabase, Orand, Orocle, zkVMs, zkDAL) collectively solve the integrity, privacy, and scalability challenges plaguing AI today.
