India Unveils Sovereign AI Box to Combat Enterprise Data Risks
As artificial intelligence adoption accelerates globally, two Indian companies have introduced what they claim is a game-changing solution to address mounting concerns over data security and corporate intelligence exposure. At the India AI Impact Summit 2026, Arinox AI and KOGO unveiled CommandCORE, described as India's first sovereign AI system designed to operate entirely offline.
The innovation represents a strategic pivot away from cloud-dependent AI services that have dominated the market, offering enterprises a private alternative that processes data locally without internet connectivity. Built on Nvidia hardware through partnerships with the chip giant and Qualcomm, the system challenges conventional wisdom about AI deployment.
The Privacy Imperative
"The future of AI is private, on an enterprise level too. You simply cannot farm out your intelligence," explains Raj K Gopalakrishnan, CEO and Co-Founder of KOGO AI. "The only way an organisation can exponentially increase its own intelligence and learning is by keeping AI private. It must own the AI."
This philosophy directly addresses growing enterprise concerns about data exposure through popular AI services. Recent analysis by security platform HiddenLayer reveals that 88% of enterprises worry about vulnerabilities introduced through third-party AI integrations, including widely used tools like OpenAI's ChatGPT, Microsoft Copilot, and Google Gemini.
The concern is well-founded. An MIT report from August noted that 95% of generative AI pilots at companies failed to launch, with privacy being a significant factor. Gopalakrishnan emphasizes that organizations using public foundational models aren't just processing prompts but exposing operational insights.
Technical Architecture and Market Positioning
CommandCORE operates on four key layers: custom Nvidia hardware, KOGO's agentic operating system, an Enterprise Agent Suite with over 500 connectors for enterprise workflows, and open-source models for sovereign AI capabilities.
The system offers three configuration tiers, starting at ₹10 lakh (approximately $120,000). The entry-level option handles models between 1 billion to 7 billion parameters, suitable for basic enterprise agents and human resource processes. Medium configurations support 20 billion to 30 billion parameters for complex inference tasks, while enterprise-grade versions rival Nvidia's DGX clusters, capable of handling models up to 405 billion parameters.
"This box is designed to cut through complexities of hardware, software and application layers, which an enterprise would have to independently orchestrate," notes Angad Ahluwalia, chief spokesperson of Arinox AI.
Economic Logic Behind Local Processing
Beyond security considerations, the economic argument for local AI processing is compelling. Gopalakrishnan illustrates this with commercial EV charging stations, each generating up to 30TB of daily data. For an organization operating 1,000 stations, cloud transmission costs become prohibitive.
Local processing dramatically reduces these expenses. "A small device sitting in every station without needing internet, they'll probably send just 200GB data to a cloud instead for processing," he explains. This approach filters and processes locally, transmits selectively, and reduces both bandwidth and cloud compute costs.
Vishal Dhupar, Managing Director of Nvidia India, supports this direction: "As AI adoption expands across regulated and sensitive environments, organisations need accelerated computing platforms that can operate entirely on-premise and under strict security controls."
Market Targeting and Future Implications
Arinox and KOGO are particularly targeting sensitive sectors including finance, banking, government services, and defense, where data sovereignty concerns are paramount. The companies expect to release additional model configurations in coming months as demand develops.
This development signals a potential shift in enterprise AI adoption patterns, particularly in markets where regulatory compliance and data sovereignty are critical considerations. For developing economies seeking to build technological independence while maintaining security standards, such solutions may prove increasingly attractive.
The success of CommandCORE and similar sovereign AI initiatives could influence global enterprise AI strategies, potentially reducing reliance on foreign cloud services while keeping sensitive intelligence processing within national borders.