The Sprawling India AI Mission
Ever since AI began permeating into various aspects of everyday life, India has strived to use it in a way that benefits every citizen and leads to collective well-being across the country. To that end, the IndiaAI mission got over ₹10,300 crore in funding back in 2024 to develop indigenous AI capabilities across various industries – some of which have already started having tangible impact:
- Agriculture is being transformed with AI-powered advisory tools improving sowing decisions, crop yields and input efficiency – with state-level deployments in Maharashtra and AP reporting productivity gains of up to 30-50%.
- Healthcare is becoming more preventive, with AI apps enabling early detection of tuberculosis, cancer, neurological disorders & other conditions.
- Education is integrating AI learning into many programs, such as the CBSE curriculum, DIKSHA platforms and initiatives like YUVAi.
- Courts are delivering speedier justice, with eCourts Phase III deploying AI & ML for translation, case management and scheduling.
- Weather & Disaster Management is getting more forward-thinking, with the IMD using AI for advanced forecasting of rainfall, cyclones, fog, lightning and fire.
It is an all-encompassing movement – NASSCOM reports that 87% of Indian enterprises today are actively using AI solutions. Yet, while adoption is high, maturity is low – only 26% of Indian companies have achieved AI maturity at scale, according to a recent BCG survey. How does your enterprise reach that level?
Blending Infrastructure, Intelligence & Security
Due to AI being a relatively new technology, many organizational initiatives across industries are seeing low rates of success. The truth is, enterprise AI success demands a multi-disciplinary approach that requires you to invest in resilient digital foundations, trustable AI tech stacks and proactive cyber risk management. To optimize AI, you need a solid data infrastructure that enables your programs to thrive. AI also brings about a higher degree of risk, and that requires additional levels of fortification when it comes to your cybersecurity programs.
Essentially, infrastructure, intelligence and security all have to work in sync for your AI initiatives to scale up towards enterprise success:
Thankfully, recent national policies paired with the presence of domain experts (like iValue) in the market ensure that your enterprise has access to all the resources required to build successful AI programs. Let’s now look at how you can build digital confidence in your enterprise across each of these 3 interlinked elements.
The Dual Architectures Required To Optimize Underlying AI Infrastructure
To establish optimal foundations for AI programs, your enterprise has to look at the underlying infrastructure involved in two different architectural layers:
- The physical architectures that determine whether models can run at scale & speed
- The data architectures that determine whether models can produce reliable outcomes
AI programs, due to their distinct complexities and requirements, demand the best of both worlds:
AI-Ready Physical Architectures | AI-Ready Data Architectures |
|
|
Both these layers have to work in sync to lay down an effective base that you can have full confidence building AI on. Several recent industry developments have helped streamline this:
- In the recent Budget 2026, the Indian government announced a 20-year tax holiday for foreign giants to build AI-ready data centres and give enterprises across the country the proper physical architectures to scale up their AI programs.
- iValue has best-in-class stacks to optimize the underlying data architectures that support your AI programs.
AI Intelligence Has Immense Applicability… And Risk
Once the underlying infrastructure is set, your enterprise can begin developing AI models that fit your specific organizational goals. India’s AI ecosystem now has multiple initiatives to accelerate model development and deployment:
- IndiaAIKosh serves as a national repository for datasets, models and tools. As of December 2025, it hosts 5,722 datasets and 251 AI models, with contributions from 54 entities across 20 sectors.
- Managed tech providers like iValue can help you create customized AI models that specifically prime your organization for success.
Yet, despite all the applications, AI models also introduce a series of novel risks into your organization that you may not have been subjected to before:
Types Of Novel Risks | What It Entails |
Decision Opacity Risk | Complex models may produce outputs that are difficult to explain, creating compliance and governance challenges. |
Data Poisoning Risk | Attackers can insert malicious or corrupted data into your training pipelines. These attacks manipulate model behaviour and outcomes. |
Prompt Injection Risk | If you create GenAI models, attackers can manipulate or extract your data using crafted inputs. |
Model Leakage Risk | Sensitive information embedded in training data may be exposed anyways through outputs, creating privacy risks. |
Supply-Chain Model Risk | If you depend on third-party models and APIs, vulnerabilities in these dependencies can cascade into your enterprise systems. |
Shadow AI Rusk | Your employees may use unapproved AI tools that create uncontrolled data exposure. |
Embedding Security Into Every Stage Of AI Development And Deployment
To successfully combat these novel risks, your enterprise must incorporate security measures across the entire AI lifecycle. AI security cannot be layered on after deployment. It must be embedded across the entire lifecycle, starting with governed data ingestion and extending through secure model development, hardened production environments, continuous monitoring, and strong governance controls.
When security is designed into every stage, enterprises reduce systemic risk, maintain regulatory trust, and create AI systems that can scale with confidence.
Additionally, to inculcate responsible AI usage among all your employees, you must conduct periodic training programs that guide them on the policies and restrictions involved when they use your enterprise AI models.
Yet, the beauty of AI is that even though it introduces novel risks, it also has the versatility to improve your cybersecurity posture in a variety of ways. Automation can identify and respond to threats much faster and more effectively, allowing your teams to focus on more higher-level security initiatives to further secure your enterprise.
In short, when you focus on these three elements (infrastructure, intelligence and security) as a whole rather than as disparate parts, you create a force multiplier that can transform your enterprise in many ways.
In that goal, iValue Group – with the experience of deploying many successful AI programs across multiple industries – can help you create fully optimized AI programs that push your organization towards exciting new directions.
