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Artificial intelligence is becoming a force multiplier in military operations, but it does not simply replace soldiers or independently “run” a war. Its most established roles are helping people process information, detect patterns, plan and maintain equipment, and coordinate systems. The greatest legal and ethical concerns arise when AI recommendations are treated as decisions—or when a weapon can select and engage targets without further human intervention.
India has built institutions and programmes to develop defence AI and introduced a trustworthy-AI framework for the armed forces. Public announcements show active capability-building, not proof that India has fielded fully autonomous warfare. Understanding that distinction is essential to judging both the technology’s promise and its risks.
What “military AI” means—and what it does not
Artificial intelligence (AI) is a broad term for systems that perform tasks associated with human intelligence, such as recognising images, classifying objects, translating language, predicting outcomes or recommending options. Machine learning is one way to build AI: a model learns patterns from data rather than relying only on rules written by a programmer.
Automation is different. An automated system follows predefined rules or procedures; it need not learn or make a judgment. Autonomy describes how much a system can do after activation without further human input. A platform may navigate, maintain its position or avoid obstacles autonomously without deciding to use force.
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An autonomous weapon system is generally understood as a weapon that can select and engage targets without further human intervention after activation, but states do not agree on one universal definition. The crucial distinction is between a drone that autonomously follows a route and a system that autonomously chooses whom to attack. The International Committee of the Red Cross (ICRC) identifies autonomous weapons, AI decision-support systems and AI-enabled cyber or information operations as especially consequential areas of concern.
How AI fits into a military system
AI is not a standalone capability. It is one component in a chain that includes sensors, data, communications, computing hardware, software models, operators and command authority. A model may identify a possible object in an image, but its usefulness depends on whether the image is reliable, whether the model has encountered similar conditions, whether the information reaches the right person in time, and what authority that person has.
In practice, AI may search and filter large volumes of information, identify patterns or propose options. People may assess the context, authorise action and monitor the system. This human-machine arrangement can speed up work, but only if people have enough time, information and authority to challenge or stop the system.
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Where militaries may use AI
Intelligence, surveillance and reconnaissance
AI can help analyse satellite and drone imagery, detect changes, classify objects, track movement, process video, translate documents and summarise large collections of text. It can also combine information from radar, satellites, signals intelligence, unmanned platforms, cyber sensors, human reports and open sources into a shared operational picture.
The benefit is faster synthesis; the danger is faster propagation of error. A false, misleading or contaminated data stream can be carried through a system and presented as if it were corroborated intelligence. Publicly listed DRDO AI/ML technology areas include image and video analytics, satellite-sensor data processing, object detection, explainable AI, document summarisation and machine translation (DRDO’s AI/ML technology overview).
Command and control
Decision-support tools can sort incoming reports, flag patterns and help commanders compare options. They may reduce the burden of monitoring many feeds, but they do not remove the need to judge uncertain or contradictory evidence. If several systems rely on the same flawed input, apparent agreement between them may not be independent confirmation.
Air and missile defence
AI may assist in detecting and classifying incoming objects, tracking multiple threats, predicting trajectories, identifying possible decoys and coordinating sensors or interceptors. These functions do not necessarily mean a system has authority to fire. Tracking, recommending a response and authorising a lethal engagement are separate steps; a system can automate or accelerate some while reserving authorisation to a human.
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Unmanned and autonomous platforms
AI can support navigation, obstacle avoidance, formation flight, maritime patrol, border surveillance, mine detection and route planning for ground vehicles. It may also help coordinate groups of platforms, including in communications-limited settings. Autonomy here describes a range of tasks, not a single level of independence. DRDO identifies autonomous unmanned surface and ground-vehicle patrolling and vision-based navigation among its development areas (DRDO’s autonomous-systems overview).
Logistics and predictive maintenance
AI can forecast component failures, maintenance needs, fuel use, spare-parts demand and supply-chain disruption, and help select routes. These applications are generally less contentious than autonomous targeting, but they still need validation. Incomplete records or conditions unlike those in the training data can produce misleading predictions about equipment availability or supply needs.
Cyber and information operations
In cyber defence, AI may help detect anomalies, classify malware, find vulnerabilities and analyse threat intelligence. In information operations, generative AI can produce synthetic images, cloned voices or persuasive text at scale; other tools may help identify manipulated media. These capabilities are dual-use: they can support defence and verification, but also deception and influence campaigns. The ICRC warns that AI could increase the speed and scale of cyber operations, with potential effects on civilian infrastructure.
DRDO lists deepfake detection and synthetic-media generation among its AI/ML technology areas. Their presence in a technology overview indicates areas of interest, not by itself an operational deployment or a particular use in conflict.
Training and simulation
AI can create adaptive opposing forces, synthetic environments and mission scenarios for training. A simulation can help personnel practise more situations, but it can also create false confidence if it does not reflect civilian presence, bad weather, sensor degradation, electronic warfare, deceptive adversaries, data scarcity or communications failure.
What AI can improve—and what it cannot guarantee
AI can process more data than a person can review unaided, maintain attention across multiple feeds and perform some tasks in dangerous or remote settings. It may reduce workload and help identify relevant information sooner. In selected contexts, better information could support safer decisions or help mitigate civilian harm.
None of those benefits is automatic. AI does not inherently make a decision more accurate, lawful or humane. A model can be wrong with confidence, miss an unfamiliar threat or classify a civilian object as military. Whether a tool improves performance depends on data quality, system design, operating conditions, training, doctrine, communications, cybersecurity and human oversight.
Why military AI can fail
Data gaps and unfamiliar conditions
Military systems may encounter terrain, weather, camouflage, equipment and adversary tactics unlike the conditions represented in their training data. Rare events are particularly difficult to predict. A model that performs well in tests may fail after a change in geography, sensors or operating conditions—a problem known as distribution shift. Poorly labelled data, incomplete civilian-location information and historical intelligence errors can compound the problem.
Cyberattack, spoofing and supply-chain risk
AI systems can be attacked through poisoned data, manipulated sensor inputs, compromised software updates, model theft or weaknesses in suppliers’ hardware and software. GPS, radar, imagery and communications can be spoofed. A system must therefore be tested against deliberate manipulation, not just ordinary mistakes.
Military AI may also have to work offline or at the tactical edge, with limited bandwidth, power and computing resources. Cloud computing can offer greater capacity but depends on communications and raises questions of security and data sovereignty. A tool that cannot function safely when the network is jammed or lost needs a defined degraded mode, not an assumption that connectivity will return.
Opacity and automation bias
Operators and commanders may need to know what information a system used, which model version was deployed, how uncertain its result was, whether the input fell outside its validated conditions and what alternatives it considered. Logs should allow a decision to be reconstructed later. Without that traceability, it may be hard to understand why a system produced a recommendation—or to investigate an incident.
Automation bias is the tendency to accept a machine’s recommendation because it appears objective or technically sophisticated. Under time pressure, a person may approve a result without meaningful scrutiny. A human approval step on paper is not sufficient if the operator lacks the time, information or authority to reject the recommendation.
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AI may shorten the time available to interpret a threat and respond. That can matter when a threat moves quickly, but compressed timelines can also reduce deliberation, encourage pre-emption and make human review nominal. False warnings or misread behaviour could contribute to escalation, particularly when decision-support systems interact with systems responsible for nuclear command and control. The ICRC has warned against AI use in nuclear command and control.
Repeated reliance on machine classifications or scores may also weaken professional judgment and attention to human consequences. A numerical confidence score cannot settle a moral or legal question about using force.
Ethics, law and human control
Military AI remains subject to international humanitarian law (IHL), including the rules requiring distinction between civilians and combatants, proportionality in attacks and precautions to protect civilians. The use of a machine does not create a legal exemption or transfer responsibility away from a state, commander or operator. The UN Secretary-General’s report on AI in the military domain emphasises compliance with international law throughout the life cycle of military AI and the preservation of human judgment, intervention, oversight and control (UN report).
The difficult question is not simply whether a person pressed a button. Meaningful human control requires an identifiable chain of command; knowledge of the system’s capabilities and limitations; a defined mission context; sufficiently predictable behaviour; time and ability to intervene; rules limiting targets, geography and duration; and records that support review and investigation. A UN working paper argues that those authorising force must be able to explain and predict its effects, and that systems unable to comply with IHL or meaningful human control should be prohibited (CCW working paper).
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Accountability becomes difficult when a system recommends a target, a human approves the recommendation quickly, the system behaves unexpectedly and key technical information is held by a vendor. Responsibility may be distributed among developers, operators and commanders, but that does not make it disappear. Technical logs, clear authorisation rules and post-operation review are necessary to establish what happened and who was responsible.
Bias is another serious risk. Unrepresentative datasets, proxies for race or ethnicity, unequal surveillance coverage, weak performance across faces or languages, and historic intelligence errors can all influence model outputs. In a military setting, a mistaken classification may lead to surveillance, detention or lethal consequences.
The ICRC takes a restrictive position: it recommends prohibiting unpredictable autonomous weapons and weapons designed or used to apply force against people, while placing strict limits on other autonomous systems (ICRC position on autonomous weapons). This is a humanitarian policy position; it should not be confused with a settled, universal treaty prohibition. Existing IHL obligations apply, while states continue to debate whether additional rules are needed. The UN Secretary-General has called for a legally binding instrument on lethal autonomous weapons and said machines should not make life-and-death decisions without human control (UN statement).
India’s defence-AI programme
India’s position is best described as active capability-building alongside an emerging responsible-AI framework. Following recommendations from a 2018 task force, the Ministry of Defence established the Defence Artificial Intelligence Council (DAIC) and Defence AI Project Agency (DAIPA) in 2019. Their intended roles include policy support, coordination, data management, testing infrastructure, training and industry engagement. They are part of a broader ecosystem involving the armed services, DRDO, defence public-sector undertakings, start-ups, universities and private companies—not a single “AI command” (Ministry of Defence announcement).
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In July 2022, the government announced 75 AI products and technologies developed by the services, DRDO, defence public-sector undertakings, iDEX start-ups and private industry. Listed areas included radio-frequency spectrum management, underwater-domain awareness, satellite-image analysis and friend-or-foe identification (Ministry announcement). The wording matters: “developed” or “launched” does not automatically mean a system was inducted or is operationally deployed.
DRDO’s published AI/ML and autonomy priorities include image and video analytics, satellite-data processing, object detection, anti-tank missile image recognition, explainable AI, language technologies, synthetic data, autonomous navigation and unmanned ground and surface vehicles. These are evidence of publicly stated technology areas, not proof that each system is in service.
In October 2024, India introduced the Evaluating Trustworthy Artificial Intelligence (ETAI) framework and guidelines for critical defence operations. The government described goals including reliability, robustness, transparency and safety, including resilience to adversarial attacks (government announcement). The public announcement does not settle every implementation question: whether evaluation is mandatory for every defence AI system, who conducts it, whether independent audits are possible, how classified systems are assessed, what incident reporting is required, or how human control is tested in realistic conditions.
India’s iDEX programme gives start-ups, small businesses, innovators, research institutions and academia a route to work on defence challenges. Its areas include AI, autonomous systems, cybersecurity, secure communications, simulation, navigation and predictive maintenance (iDEX programme announcement). Wider participation can speed innovation, but moving from prototype to procurement and service use still requires testing, integration, secure data access and long-term support.
India’s strengths and constraints
India has potential advantages: a large technical workforce, space, missile, radar and telecommunications institutions, a domestic defence market, a growing start-up and university ecosystem, and operational needs across land, maritime and air borders. These factors create opportunities; they do not establish parity with any other military or prove that a capability has been validated in combat.
Likely structural challenges include fragmented data ownership, limited access to high-quality military datasets, reliance on imported advanced chips and specialised hardware, uneven connectivity in remote theatres, a shortage of personnel trained in both military operations and AI assurance, lengthy procurement and testing cycles, and barriers to transferring technology between civilian and military sectors. Classification can also make independent assessment difficult. These are relevant issues for evaluating the programme, not proof that any one system has failed.
Claims about AI in recent operations require particular care. A DRDO press-clipping compilation includes media reporting about AI-enabled integration in Operation Sindoor and claims of 129 AI-based defence projects, 77 completed by 2026. It is not a detailed operational after-action report, so it cannot establish which AI functions were used, how they affected decisions or whether they were independently verified (DRDO compilation). Publicly available evidence supports describing India’s programme and technology priorities; it does not support claiming that AI independently determined the outcome of a named conflict.
International approaches: principles are not proof of performance
NATO’s revised AI strategy sets out six responsible-use principles: lawfulness, responsibility and accountability, explainability and traceability, reliability, governability and bias mitigation (NATO summary). They offer a useful governance benchmark, but a published framework does not prove that every member state or system implements the principles identically.
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At the UN, General Assembly Resolution 79/239, adopted in December 2024, addressed AI in the military domain and its implications for international peace and security. The UN process continues to consider legal, humanitarian and security questions (UN overview). The ICRC argues for prohibitions and strict restrictions on some autonomous weapons, while states continue to debate the scope and form of future rules.
A practical test for responsible deployment
A defence organisation evaluating an AI system should ask more than “How accurate is it?” A serious review should consider:
- Mission and limits: What exact task is the system authorised to perform, in which area, for how long and against what target classes?
- Reliability: How does it perform with incomplete data, unfamiliar terrain, degraded sensors, electronic warfare and communications loss?
- Legal review: Can the intended use comply with distinction, proportionality and precautions in attack?
- Human control: Do operators understand the system, have enough time and information to challenge it, and retain a real ability to intervene or abort?
- Security: Has the system been tested against spoofing, poisoned data, compromised updates and supply-chain vulnerabilities?
- Traceability: Are model versions, inputs, uncertainty, approvals and interventions logged so that an incident can be investigated?
- Bias and civilian harm: Has performance been assessed across relevant populations, languages and environments, with procedures for uncertain results?
- Lifecycle governance: Do updates require re-testing and re-certification, and are incidents reported and reviewed?
- Strategic dependence: Who controls the data, hardware, model updates, maintenance and long-term sustainment?
Failure planning matters as much as expected performance. A false positive needs verification and an abort path; a false negative needs uncertainty review and independent sensing. Communications loss should trigger defined behaviours such as mission limits or return-to-base logic, not unrestricted continuation. Operators should be trained to challenge recommendations, and realistic exercises should test whether human control survives time pressure and system malfunction.
The central question for India
The important question is not simply whether India should use AI in defence. AI can help with sensing, logistics, maintenance, cyber defence and decision support, as well as more contentious autonomous functions. The harder questions are which missions should use it, what limits apply, how its performance is tested in real conditions, and who remains accountable when it is wrong. India’s public record shows growing institutions, projects and a responsible-AI framework; the credibility of that effort will depend on transparent governance, rigorous assurance and human authority that is meaningful in practice.
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