Atmanirbharta in national security: The inevitable reform for Viksit Bharat

This article was first published in The Week.in on June 05 2026. Please click here to read the article.

India’s national security architecture is transitioning through a decisive juncture. As artificial intelligence begins to dominate decision support systems which were once solely within the realm of human judgement, the idea of Atmanirbharta demands a more rigorous understanding. Self‑reliance, especially within the national security architecture, cannot remain a mere technology acquisition strategy. The time is ripe when we must evolve it into a doctrinal reform to address who and what controls state decision‑making systems, how authority is distributed within them, what governance safeguards govern their use and where are the sources of origins and the point of impact of such decisions in a clearly explainable manner.

The general articulation of Atmanirbharta has mostly remained rooted in economic resilience and industrial capacity. While important, this framing often make little of what is at stake when it comes to national security. Dependence here goes much beyond the hardware of national defence, it is as critically inconvenient in the decision support systems that powers the targeting and firing frameworks and guidelines that adds the lethality which we need as a national when it comes to national security. When core facets like intelligence, policing, and justice-delivery are facilitated by systems that the India does not fully own or at the very least, control, then underlying sovereignty becomes provisional.

When it comes to our national security in the AI era, the challenge is not access to technology, but loss of judgement. AI models trained on external datasets, designed for foreign operational contexts, and hosted on foreign infrastructure embed assumptions that may be misaligned with India’s legal, cultural, and threat environments. The resultant output is often contextually irrelevant, and the decision-tree is also opaque and beyond the scrutiny of India agencies.

The risk for the nation is further accentuated in today’s geopolitical environ where dependence is often a leverage. Reliance on foreign hyperscalers may lead to a critical chokepoint that cannot be mitigated through contractual assurances alone. National security cannot be outsourced to systems whose continuity, reliability and auditability lies beyond our sovereign control. Yet, at times, there is a degree of normalisation of this dependence by prioritising rapid adoption.

To achieve sovereignty, different nations and blocs have followed different pathways. For instance, the European Union is putting a regulation regime in place through its AI Act which was passed in 2024 and key provisions of it will come into force this August¹. This is a rights-based regulation focusing on safety and explainability. The US, on the other hand, is building and collaborating with hyperscalers and exporting these models globally. Predictably, the Chinese on the other hand are focusing on state control.

For India, the larger pathway for this Atmanirbharta has been laid out in the five-layer framework defined by the government when it comes to AI, sovereignty in applications, models, compute, infrastructure and energy. In other words, India defines Sovereign AI as full-stack national capability across applications, models, compute, infrastructure, and energy, enabling the country to build and run AI entirely on its own terms. This has been well articulated in the recently concluded India AI Impact Summit that India hosted. India’s most impactful decision has been its policy on democratising compute and decentralising innovation. Enabling advanced compute accessible beyond metropolitan and corporate enclaves is a structurally sound reform that aligns with India’s scale and diversity. Making it affordable at INR 65 per hour is the true genius².

From a national security perspective, this could mean operational and targeting support for a Corp Commander responding to enemy aggression, analytical augmentation capabilities for and analyst in IB who is trying to make sense of an intercept in foreign language, assistance in following the due process of law to a freshly minted constable posted in the hinterlands with limited access to legal knowhow, or truly democratising the justice delivery process by keeping citizen rights at the forefront with solutions like Nyaya Setu.

The other key strength that we possess is the elephantine database that our citizens produce, and which is key to training, contextualising and deploying our own models. Looking through national security lens, this could mean that erstwhile siloed data is brought together in a conjoint data lake. For instance, the world’s largest justice system ERP is India’s CCTNS-ICJS platform. The richness of this humongous dataset can be appreciated when we realise that an entry in this system acts as a single source of truth for the entire criminal justice system of the nation since over last two decades. For instance, data entered in registering a case in a village in Mizoram becomes an enabler for policymaking when it is aggregated and analysed nationally at the Ministry of Home Affairs. When we add to that the NATGrid data or the enormous public-safety net of CCTVs deployed through India’s Smart City project, or the petabytes of social media chatters we produce daily, this could forms a data lake which can be leveraged to generate substantial predictive capabilities.

Decentralised capability without central governance only defuses risk. While privacy preservation governance and legal safeguards are being put in place with laws like DPDP Act or the revision of IT Rules, given the speed of development in these technologies, there needs to be legally mandated explainability embedded into applications and models to root out biases quickly. As the country comes together to respond to this national mission, it is equally important that we work towards achieving a certain degree of sovereignty in compute through indigenisation and diversification of supply chains. Unless we put these guardrails in place, the stride of innovation could potentially outpace the administrative controls in place.

Additionally, while in theory, our sovereign datasets present unprecedented analytical potential, these data could potentially usher in dramatic improvements in predictive policing, threat assessment, and institutional learning. In practice, however, they are limited by institutional silos and governance opacity. Data integration has progressed faster than clarity on purpose limitation, access controls, and administrative oversight. Without enforceable governance structures, data consolidation could potentially become a tool that would fall short of delivering strategic effectiveness.

The reform challenge, therefore, is less of a technical issue than an institutional and administrative one. India needs legally mandated data privacy audit regimes that demonstrates at a predefined periodicity as to who is accessing what, for what purpose, and under whose supervision. Thus, reform must move towards mandatory explainability for all state‑deployed AI systems, especially those influencing national security decision-making.

Compute sovereignty remains another critical challenge. While full indigenisation in this area remains aspirational in the near term, diversification of supply chains and reduced exposure to single points of failure should be carefully guarded against especially within our national security doctrines.