India Introduces Sovereign AI Solution Amid Data Security Concerns
As discussions regarding the adoption of artificial intelligence (AI) agents intensify, numerous organizations may overlook parallel risks associated with data security, integrity, and costs.
At the India AI Impact Summit 2026, the Indian AI-native transformation endeavor, Arinox AI, in collaboration with agentic AI company KOGO, unveiled what they herald as the nation’s inaugural sovereign AI product—a pioneering system encapsulated in the concept of “AI in a box.” Agentic AI assemblies must navigate dual concerns regarding security and privacy.
CommandCORE, the latest offering from Arinox AI and KOGO, posits a somewhat paradoxical vision for the AI future—one that is private, sovereign, and compact.
This innovative system is engineered to perform computations locally, rendering reliance on the internet unnecessary. The initiative benefits from partnerships with tech giants Nvidia and Qualcomm, with the latest iteration of CommandCORE operating on advanced Nvidia hardware.
“The future of AI is inherently private, especially at the enterprise level. Outsourcing your intelligence is untenable. The singular path for organizations to significantly amplify their intelligence and learning is by retaining ownership of AI technology,” asserts Raj K Gopalakrishnan, CEO and Co-Founder of KOGO AI, during a discussion with HT.
The core premise of “AI in a box” is as much philosophical as it is technical, urging discourse beyond large language models (LLMs) and graphics processing units (GPUs).
Organizations leveraging public foundational models are not merely processing queries; they are also unwittingly divulging operational insights. “Sensitive sectors expose intelligence when sharing data with foundational models and cloud-based AI services,” Gopalakrishnan emphasizes.
Conversely, agentic AI implementations must grapple with the dual specters of security and privacy threats. Gopalakrishnan proclaims, “Information transforms everything. The moment you contextualize data, you impart intelligence.”
A study titled “AI Threat Landscape 2025,” conducted by the security platform HiddenLayer, reveals that 88% of enterprises harbor concerns regarding vulnerabilities introduced through external AI integrations, including widely adopted tools like OpenAI’s ChatGPT, Microsoft Copilot, and Google Gemini.
In August of the previous year, a report from MIT highlighted that 95% of generative AI pilot programs in the corporate world failed to materialize successfully, with privacy concerns cited as a significant factor.
Framework and Cost Perspectives
There are four fundamental layers integral to a private AI-in-a-box solution: first, custom hardware from Nvidia; second, KOGO’s agentic operating system, which encompasses an Enterprise Agent Suite boasting over 500 connectors tailored for enterprise workflows, while utilizing open-source models to ensure sovereign AI capabilities.
Variants include Nvidia’s Jetson Orin-class edge systems optimized for field applications, DGX Spark for compact on-premises development, and configurations suitable for enterprise data centers featuring Nvidia RTX Pro 6000 Blackwell Server Edition graphics.
“This box is engineered to navigate the complexities of hardware, software, and application layers, which enterprises would otherwise need to orchestrate independently. It facilitates focused workloads, repeatable tasks, and scalability to large clusters covering entire workflows,” explains Angad Ahluwalia, chief spokesperson for Arinox AI.
Scalability is attainable through the interconnection of multiple units. Enterprises can currently select from three model configurations, with more iterations anticipated in the upcoming months, as noted by Ahluwalia. Pricing commences at ₹10 lakh.
The small CommandCORE option operates models ranging from 1 billion to 7 billion parameters, making it suitable for enterprises deploying a limited number of agents for tasks such as batch processing or human resources onboarding.
The medium model accommodates parameters between 20 billion and 30 billion, catering to complex agents with advanced inference capabilities.
“As AI adoption proliferates in regulated and sensitive fields, entities require accelerated computing platforms that function entirely on-premises while adhering to stringent security protocols,” states Vishal Dhupar, Managing Director of Nvidia India.
“The largest models, akin to Nvidia’s DGX clusters based on the Grace Blackwell series, serve as formidable engines capable of driving enterprise-wide transformation,” Ahluwalia remarks. For context, Nvidia documentation suggests that interconnecting two such DGX units can manage models reaching up to 405 billion parameters.
The Significance of a Private, Secure, Local AI System
Beyond the argument for sovereignty, why is a private, secure, and localized AI system essential? For Gopalakrishnan, the rationale extends into economic domains. He cites the example of commercial electric vehicle charging and battery swap stations, each capable of generating approximately 30TB of data daily.
“If an organization owns 1,000 such stations and is compelled to transmit all this data to the cloud, the costs become astronomical,” he observes.
The alternative lies in edge processing. “A compact device stationed at each site, requiring no internet, can transmit merely 200GB of data to the cloud for processing.”
In essence, this approach advocates for local filtering and processing, selective transmission, and a notable reduction in both bandwidth and cloud computing expenses.
Arinox and KOGO aspire to garner traction, particularly within sensitive sectors such as finance, banking, government services, and defense.
Source link: Hindustantimes.com.
