Col (Dr) Anupam Tiwari
This article discusses the importance of Indigenous Transformer-based models for enhancing the country’s National Defense systems. It analyzes the vulnerabilities associated with the use of foreign models for AI systems, including Biasing, Alignment issues, Adversarial attacks, and Issues with data sovereignty. These methods for developing models more comprehensively, including fine-tuning, federated learning, and secure model development, will be discussed in depth. Although utmost care has been taken to ensure the content is easily comprehensible, certain technical concepts may not be familiar to all readers. Indeed, the defence and aerospace sectors are witnessing rapid evolution, with a blurred line separating “Technical” and “Non-technical” aspects, enabling informed decision-making at all leadership levels without necessarily delving into the technicalities of these concepts. The reader is not required to be familiar with the technicalities involved, but must be conversant with the fundamental concepts of Transformers, Federated Learning, and Differential Privacy to effectively navigate the defence sector’s technology-driven future.
The New Battlespace is Cognitive
Modern warfare is not just taking place in the physical realms of Land, Sea, Air, Space, and Cyberspace, but is now occurring in a new battlespace: the Cognitive Domain. In the Cognitive Domain, the outcome is not based on who has more platforms but on who can interpret faster, more accurately, and more reliably.
The defence forces are currently flooded with massive amounts of information from sensors, communications intercepts, intelligence, open-source information, and social media, which may be in multiple languages, unstructured, and time-sensitive. This is beyond the capacity of human brains to handle. Artificial Intelligence (AI), in the form of the “Transformer” language model, is increasingly being placed at the heart of military intelligence analysis and command and control systems.
The implications here are deeply significant. Where AI systems are used to sift intelligence, make recommendations, or identify threats, they do not simply support a commander; they enable a judgment. Where the “thinking layer” of defence systems is built on foreign-trained black-box models, the sovereignty of defence-related judgement is compromised. The question facing today’s military is not one of Adoption, but Ownership.
This is an inflection point for India. Defence is increasingly becoming an algorithmic domain, and the basis of those algorithms, including Data, Models, Assumptions, and Security, needs to be considered vital national resources, not luxury imports.
Why Transformers Matter to Defence Operations
Transformers are quickly becoming the foundation for future AI applications because they are highly effective at performing what future defence systems will require: understanding, correlating, and reasoning about vast volumes of unstructured data in real time. Unlike previous AI models, which were limited by sequence and working memory capacity, Transformers can examine relationships across entire datasets thanks to their attention mechanisms.
This capability offers tremendous benefit to defence operations. Transformer models can correlate information from disparate sources such as Signals Intelligence, Image Metadata, Mission Data, Operational Reports, and publicly available information to make sense of the situation. They can analyze communications from various languages in near real-time to assist the intelligence community with the analysis of intercepted communications that use dialects, slang, and code, which would otherwise be difficult for human analysts to understand.
However, what is perhaps even more significant is that Transformers will increasingly be employed in decision support systems in the future. They will effectively shorten decision time by providing alternatives during decision-making through their ability to identify anomalies in intelligence summaries. This is a vital ingredient in a fast-moving world of aeronautics and defence.
It is the same power that drives the application of Transformers from analytical tools to cognitive infrastructure. As models analyze data, predict, and influence decision-making, their assumptions, biases, and security considerations directly affect their effective functioning. This is what makes model provenance, design, and management of interest to strategy and not preference.
The Hidden Risk of Imported AI Brains
With AI systems increasingly integral to military decision-making, the use of foreign-developed or pre-trained Transformer models is fraught with deeply problematic strategic implications. Such models may look and feel competent and trustworthy, but beneath their polished surface lurk problematic vulnerabilities.
- Opaqueness and False Trust: “Black boxes” in the AI context are mostly pre-trained models. Commanders and analysts can view outputs but are unable to grasp the rationale for those outputs. “Explainable AI (XAI)” solutions are only approximations to transparency. In critical operations, unexamined trust in opaque AI may lead to threats being misestimated, resources being misallocated, or decisions being operationally wrong.
- AI Hardware: It’s not just the software; foreign AI chips come with operations ‘Locked’ to verified uses, unmodifiable, and destroying data if messed with. This translates to ‘under the radar’ risks, ‘non-flexible’ use, and vulnerability to dangers beyond our control, if we are talking about our National Defence. What we need is our very own hardware and models for our AI. In defence, this would entail a lack of flexibility, latent weaknesses, and foreign control. Real sovereignty would require developing AI from scratch, hardware, models, and data exclusively indigenous, but at the same time, it would be difficult to realize.
- Bias, Alignment Faking, and Cultural Misfit: AI systems inherit bias from the data they are trained on. For the military, bias could be built into the system over the years through hierarchy, regions, regimentation, or gender. Additionally, Alignment Faking, in which the model performs well in testing but poorly in real-world applications, can lead to disastrous classification, such as flagging a harmless ship as an enemy or downplaying risks in critical situations.
- Threats from Adversaries & the Supply Chain: Adversaries could use models in a number of ways, including adversarial examples, data poisoning attacks, or back-door attacks. Even hardware or software libraries used in the supply chain could be vulnerable to attacks. Model extraction attacks could be used to copy the “Decision Brains” used in AI.
- Cryptography and the Quantum Horizon: Today, the imported encryption standards are used in most of the existing AI systems. However, the upcoming threat of quantum computing may breach the existing cryptography, making the defence models vulnerable to new threats. The incorporation of post-quantum cryptography is essential, but India is yet to catch up here, too. It is also important to note that a large amount of the existing as well as planned quantum technology induction and roadmaps ride on internationally defined standards, such as the NIST-based cryptographic standards, as well as imported critical hardware components, like photonic detector technology, lasers, high-speed electronics, etc., with wafer-scale fabrication being a major hurdle.
- Operational Ramifications: These weaknesses are not merely hypothetical. A hostile entity skilled in the art of subtle manipulation or reconnaissance of a system that utilizes a Transformer could lead troops astray, reveal secrets, or spread disinformation. In the context of space and defence, a millisecond of incorrect computation could lead to catastrophic results.
The OS Lesson India Cannot Repeat
History provides a grim example of what could go wrong in India’s AI plans. In the ’90s, when Windows OS and other foreign operating systems swept the market, India missed an opportunity to dive into developing a completely indigenous OS instead of making an opportunity out of it. Today, millions of PCs and mobile phones in India run foreign operating systems, which compromise their sensitive information and create a long-term dependence on foreign technology. While a lot of work has been done to give BOSS and MAYA an indigenous OS identity, their cores and many other libraries depend heavily on foreign open-source software.
The takeaway is simple: Basic Technologies Count. It is not enough to have good applications, user interfaces, and other peripheral technologies if you are not good at basic technologies such as operating systems, microprocessor architecture, encryption algorithms, and AI algorithms. This is because basic technologies have invisible vulnerabilities, whether it is data theft or supply chain risks, and AI, including algorithms based on the ‘Transformer’ architecture, is no exception.
The act of trying to replicate foreign models with advanced technology is a repetition of the same mistake made with OS dependence. To not repeat the same mistake in history, it is imperative for India to focus on developing its basic Transformer models indigenously. It is not only a question of capability but a question of sovereignty in decision-making for the future.
Building Indigenous Transformers: Opportunities and Challenges
It is not only necessary but also imperative to develop indigenous Transformer models in the defence sector. The magnitude of the effort is enormous, but the payoff in terms of its benefits is many times the cost. This is because indigenous models will be customized to the languages of India, the country’s defence psychology and vocabulary, and the nation’s culture.
Opportunities in Indigenous Development
- Data Sovereignty: By using local data for models, it is ensured that key military intelligence is kept inside the country’s boundaries and is therefore at a lower risk of leaking out or coming under foreign regulation. But even if the basic model is kept offline and its updates are managed within the country’s borders, it is only a matter of time before hardware upgrades require rebooting the overall offline design and reconsidering the architecture.
- Contextual Precision: The Indigenous Transformers will be able to understand and incorporate operational language, idioms, and even communication protocols specific to their respective units or organizations.
- Ethical and Safe AI: National norms regarding privacy, adversarial robustness, and alignment can be baked in, making it less likely for biased, aligned, or toxic outputs to emerge.
- Integration with Defence Ecosystems: Indigenous models will have smooth interfaces with existing command-control, surveillance, and intelligence systems, preventing expensive retrofits or security loopholes.
Challenges
Developing local models based on Transformer architectures for the military is not easy. The process is very resource-intensive and requires sophisticated hardware and highly skilled personnel. The training data used for military applications has existed for many years. Still, it has been isolated on islands and is prone to inconsistencies and vulnerabilities that need careful consideration and mitigation. The models also need to be robust and secure against adversarial attacks such as data poisoning and backdoor attacks to be useful and effective in real-world applications. Added to these are the dynamic development and improvement of AI technology, and the need to incorporate robust safeguards, such as Retrieval Augmented Generation and Differential Privacy, to mitigate potential dangers, such as quantum attacks.
Strategic Approaches:
Until such time that fully indigenous models are developed, the following short-term approaches can be employed as stopgap measures to fill capability gaps:
- Fine-Tuning Qualified Pre-Trained Models on Specific Defence Applications,
- Federated Learning Among Distributed Units
- Open-Source Architectures With Full Provenance.
Ultimately, the task of developing Indigenous Transformers is a matter of more than just technology; rather, it is a decades-long endeavour that seeks to ensure India’s defence cognition. This is a task that requires vision, focus, and patience. Still, when properly implemented, it would create a secure and independent AI environment able to guide defence decisions for decades to come.
Urgency of Adoption
The Transformer is no longer merely an analysis engine; it is soon to become an intrinsic part of operational-level decision-making in aerospace and defence applications. By integrating analysis of intelligence reports, radar data, satellite imagery, and communications, it can provide insights into threat irregularities and adversary movement patterns at speeds that are simply unmatchable by human analysts. The imperative to develop its own Transformers domestically is driven by one basic truth: Foreign-developed Transformers simply cannot understand India’s operational context, languages, and defence environment psyche. Their use would entail risks of data leakage, bias, and adversarial manipulation, even if trained on Indian datasets, because of inherent foundational flaws that would persist and not be remediated by retraining on new datasets.
Call for Technological Sovereignty
During the journey towards this indigenous move, new developments are inevitable. Today’s technology will get obsolete at a faster rate than technology has ever been in recent times and the past. Integrating and developing upon rugged guardrails, such as Retrieval Augmented Generation and Differential Privacy, needs to be emphasized to address privacy and security challenges.
The need for Technological Sovereignty of our nation has never been as necessary as it is today. We need to understand, as a nation, the weight and responsibilities of the word “AATMNIRBHAR”, for it is not just today and Tomorrow that we need to envision, but also at least 100 years and beyond. We should look for a future wherein Aadhar and UPI apps are not run on foreign operating systems but on our own indigenous OSs. We have to have a vision wherein India has its own operating system (Desktop/Mobiles), own niche lithography machines, own ASICS-GPUs-TPUs, own encryption standards, own browsers, own storage manufacturers, own cloud OS, own AI models, etc., to mention a few. The list is long; the responsibilities are arduous enough to keep us much engaged as a nation for the next few decades, and with our abundant population, we have enough expertise, too. The only need is Right Vision, Right Focus, Right Speed, Right Skills, True Indigenous digitization, and unfeigned R&D.
Col Anupam Tiwari, PhD, the author, formerly serving as Advisor (Cyber) in the Office of the Principal Advisor, Ministry of Defence, draws upon firsthand experience in defence technology strategy.
