Unlock Your Most Valuable Data for AI: NEAR AI Introduces Verifiable, Hardware-Enforced Privacy
NEAR AI has launched two new products – NEAR AI Cloud and Private Chat – designed around a simple but transformative idea: users should own their AI. These products introduce hardware-backed, verifiable privacy, addressing one of the biggest barriers preventing sensitive and proprietary data from entering the AI ecosystem.
Traditional AI systems force users to trust opaque infrastructure, unverifiable “private modes,” and software promises that can fail due to misconfiguration or insider access. NEAR AI aims to solve this by enabling cryptographically verifiable privacy at the hardware level.
The AI Privacy Gap Is Blocking Enterprise Adoption
AI adoption is accelerating, but valuable datasets remain locked away. Most AI APIs require sending sensitive information to environments users cannot inspect or verify. Even self-hosted solutions demand significant engineering investment and operational complexity.
Traditional trust mechanisms – policies, contracts, certifications – are easily broken. One configuration error can compromise an entire system. This is why enterprises avoid sending their most important data to AI models and why the industry urgently needs verifiable privacy, not trust-based assurances.
According to NEAR Protocol co-founder Illia Polosukhin, verifiable privacy is essential to unlocking the full value of AI by allowing users to safely introduce more context, data, and personalization.
NEAR AI Delivers Privacy You Can Verify
The NEAR AI product suite closes the AI privacy gap using confidential computing. Every request to NEAR AI Cloud is executed inside Intel TDX and NVIDIA Confidential Computing hardware. Data is processed within secure, sealed environments that remain inaccessible even to the hardware owner or NEAR AI itself.
Each inference generates a cryptographic attestation proving:
• the model ran inside genuine, verified hardware
• the correct code was executed
• the computation can be independently validated via Intel and NVIDIA attestation services
NEAR Private Chat extends these guarantees to everyday use cases such as financial planning, research, and personal advice. Users can now leverage AI safely, with verifiable confidentiality and zero exposure of sensitive information.
Already Integrated with Brave Nightly, OpenMind, and Phala
NEAR AI Cloud is already in production with several high-profile partners, including:
• Brave Nightly, the privacy-focused browser
• OpenMind, a robotics operating system provider
• Phala, a confidential cloud platform
Together these partners serve over 100 million users globally.
Working with their engineering and security teams, NEAR validated confidential computing under real-world conditions involving large-scale throughput, deterministic latency requirements, and strict compliance environments.
How NEAR AI Cloud Works
NEAR AI Cloud enables private inference through three steps:
- Encrypt
The user’s prompt is encrypted locally and transmitted to a Confidential Virtual Machine (CVM). - Isolate
The prompt is decrypted inside the Trusted Execution Environment (TEE), processed by the model, and the output is re-encrypted before leaving the enclave. - Verify
The user decrypts the response locally and receives a proof of execution. Developers can verify attestation by calling:
/v1/attestation/report?model={model_name}
The endpoint returns signed proofs from Intel TDX and NVIDIA TEE.
NEAR Private Chat uses this same process, offering familiar chat functionality with cryptographic privacy.
A Foundation for Decentralized Confidential Machine Learning
NEAR AI Cloud is an early step in NEAR’s roadmap to Decentralized Confidential Machine Learning (DCML) – a long-term vision where:
• private computation scales globally
• verification happens on-chain
• users retain ownership of their data and AI context
• the NEAR token powers settlement, verification, and incentives
This positions NEAR as a foundational layer for the emerging confidential AI economy.
For Developers: A Lift-and-Shift Path to Private AI
NEAR AI Cloud is fully OpenAI-compatible, enabling seamless migration:
• existing OpenAI clients work without architectural changes
• Python and TypeScript SDKs automate encryption, key management, and attestation
• latency increases only 5–10% while supporting up to 100 requests per second per tenant
• enterprise features include streaming, KV caching, and persistent model hosting
This makes confidential computing accessible as a cloud service without requiring specialized hardware or expertise.
What’s Next for NEAR AI
In H1 2026, NEAR AI plans to introduce multimodal models and expand functionality beyond inference into user-owned private data, portable memories, and broader DCML capabilities.
The long-term goal is clear: make verifiable privacy the default for all AI workloads, from everyday chat use to mission-critical enterprise systems.
Market Impact Analysis
NEAR’s move into verifiable confidential computing positions the ecosystem at the intersection of AI infrastructure and blockchain-based verification. As demand for private inference grows across enterprises, healthcare, finance, and regulated sectors, NEAR could capture a strategically important niche: providing cryptographically guaranteed confidentiality at scale. This strengthens NEAR’s competitive stance not only against crypto-native AI projects, but also against centralized cloud providers struggling with trust and data-sovereignty challenges.
By aligning hardware-backed privacy with an OpenAI-compatible API, NEAR reduces friction for developers while expanding real-world utility for the NEAR token. If adoption continues to accelerate through partners like Brave and Phala, NEAR could become a leading settlement and verification layer for confidential AI workloads.
Forward-Looking Sentiment and Risk Factors
Market sentiment toward NEAR AI is increasingly positive as investors and developers seek alternatives to opaque centralized AI systems. However, adoption will depend on several factors: the speed of enterprise migration, competition from large cloud providers building their own confidential computing stacks, and the ecosystem’s ability to scale secure enclaves without performance degradation. Regulatory shifts around AI privacy and cryptographic verification may also define the pace of growth.
If NEAR successfully delivers decentralized confidential machine learning, the ecosystem could benefit from long-term, sustainable demand for both compute and on-chain verification. But execution risk remains high, and the competitive landscape will intensify as AI and blockchain architectures continue to converge.
BTCUSA Comment
From an industry perspective, NEAR’s pivot toward verifiable confidential computing signals a broader shift in AI infrastructure: the move away from trust-based privacy to cryptographically enforced guarantees. This aligns with a growing demand from enterprises that want to leverage AI but cannot expose sensitive datasets to opaque external systems. The combination of OpenAI-compatible APIs, hardware-backed privacy, and on-chain verification gives NEAR a technological angle that many competitors currently lack.
For investors, developers, and ecosystem analysts, the key question becomes whether NEAR can scale this model fast enough to secure meaningful market share before major cloud providers close the gap. If NEAR continues to land integrations of the caliber of Brave, OpenMind, and Phala, it may emerge as a foundational player in the confidential AI space — one where blockchain is not a marketing add-on, but a critical part of the verification pipeline. BTCUSA will continue monitoring NEAR’s partnerships, developer activity, and token-economic shifts as this category evolves.
