Autonomous Weapon System (AI in Warfare) | Science & Technology for UPSC CSE PDF Download

Introduction

Autonomous Weapon Systems (AWS), often termed "lethal autonomous weapons" (LAWs), integrate artificial intelligence (AI) to independently identify, track, and engage targets without human intervention. These systems leverage advanced algorithms, sensors, and machine learning to perform tasks traditionally requiring human decision-making. As of August 2025, AWS are at the forefront of global defence innovation, driven by AI advancements and geopolitical tensions. For India, AWS align with the Atmanirbhar Bharat initiative, enhancing capabilities against regional threats from China and Pakistan. This topic is critical for UPSC, intersecting Science and Technology, national security, ethics, and international relations in the General Studies syllabus.

Fundamentals of Autonomous Weapon Systems

• Core Principles:

  • AWS use AI for autonomy in three functions: perception (sensing environments via radar, cameras, or LIDAR), decision-making (processing data to select targets), and action (executing strikes or defensive measures).

  • Levels of autonomy: Human-in-the-loop (human approves actions), human-on-the-loop (human oversees but can override), and fully autonomous (no human intervention).

  • Key technologies: Machine learning (ML), computer vision, neural networks, and real-time data processing.

• Advantages:

  • Rapid response to threats, outpacing human reaction times (e.g., milliseconds for missile defence).

  • Reduced risk to human soldiers by deploying AWS in high-threat zones.

  • Scalability and cost-efficiency in repetitive or high-volume tasks (e.g., drone swarm defence).

• Limitations:

  • Dependence on reliable AI algorithms, vulnerable to errors or adversarial attacks (e.g., spoofed sensor data).

  • High development costs and need for robust cybersecurity.

  • Limited situational awareness in complex, dynamic environments.

Types of Autonomous Weapon Systems

• Unmanned Aerial Vehicles (UAVs): AI-enabled drones like Turkey’s Kargu-2, capable of autonomous target selection and kamikaze strikes, used in Libya (2020).

• Unmanned Ground Vehicles (UGVs): Robotic platforms like Russia’s Uran-9, equipped with AI for reconnaissance and combat in urban warfare.

• Unmanned Maritime Systems: Autonomous ships or submarines, such as the US Navy’s Sea Hunter, for mine detection and anti-submarine warfare.

• Loitering Munitions: "Suicide drones" like Israel’s Harop, which autonomously patrol and strike targets upon detection.

• Swarm Systems: Coordinated groups of AI-driven drones, such as China’s CH-901 swarm, overwhelming defences through numbers and adaptability.

• Defensive Systems: AI-based missile defence like Israel’s Aegis or India’s Akash, autonomously intercepting incoming threats.

Applications in Warfare

• Offensive Operations:

  • Precision strikes on high-value targets (e.g., command centres, missile launchers) with minimal collateral damage.

  • Swarm attacks to overwhelm enemy air defences or saturate battlefields.

• Defensive Operations:

  • Countering drones, missiles, or cyberattacks in real time. Example: US Phalanx CIWS autonomously tracks and destroys incoming projectiles.

  • Protecting critical infrastructure (e.g., airbases, naval ports) from asymmetric threats.

• Intelligence, Surveillance, and Reconnaissance (ISR):

  • AI processes vast sensor data for real-time battlefield analysis, enhancing situational awareness.

  • Autonomous drones map enemy positions or detect stealth assets.

• Logistics and Support:

  • Autonomous vehicles deliver supplies in contested zones, reducing human exposure.

  • AI-driven predictive maintenance for military equipment.

Global Developments in AWS

• United States:

  • Leads with programs like Project Maven (AI for drone imagery analysis) and Joint All-Domain Command and Control (JADC2) for AI-integrated warfare.

  • DARPA’s Collaborative Operations in Denied Environment (CODE) tested autonomous drone swarms in 2024.

  • Budget: Over $3 billion in FY2025 for AI and autonomy in defence.

• China:

  • Advances in AI-driven drones (e.g., CH-901) and autonomous naval vessels.

  • PLA’s “intelligentized warfare” doctrine emphasizes AWS for Indo-Pacific dominance.

  • Demonstrated swarm drones (100+ units) in 2025 exercises.

• Russia:

  • Deploys Uran-9 UGVs and Su-57 jets with AI-assisted targeting.

  • Used AI-guided loitering munitions in Ukraine (2022–2024).

• Other Players:

  • Israel: Harop and Iron Dome integrate AI for autonomous operations.

  • UK: Taranis stealth drone with semi-autonomous capabilities.

  • Turkey: Kargu-2 drones, noted for autonomous strikes in Libya.

India’s Initiatives in AWS

India is accelerating AWS development under DRDO and private sector collaboration, aligning with the 2023 National AI Strategy:

• Key Projects:

  • Rakshak Drone Swarm: DRDO’s 2025 project for AI-driven surveillance and attack drones, tested in Rajasthan.

  • Nag Autonomous UGV: A robotic platform for border patrolling, integrated with Bharat Forge’s AI systems.

  • Tejas Mk-1A AI Integration: AI-assisted targeting and threat prioritization, operational by 2026.

  • Swarm Intelligence Program: DRDO’s 2024 trials of 50-drone swarms for coordinated ISR and strikes.

• Developments in 2025:

  • Successful tests of AI-based missile defence systems integrated with Akash and QRSAM.

  • Private sector (Tata, L&T) developing AI modules for autonomous logistics.

• Strategic Role:

  • Counter China’s AI-driven systems along LAC, including drone incursions.

  • Enhance maritime security in the Indian Ocean against Pakistan and China.

  • Support counter-terrorism operations in Jammu & Kashmir with autonomous ISR.

• Challenges:

  • Limited indigenous AI expertise and high-performance computing infrastructure.

  • Cybersecurity risks to AI systems, requiring robust encryption.

  • Funding constraints: ₹10,000 crore allocated for AI-defence R&D (2023–2028).

Ethical and Regulatory Issues

• Ethical Concerns:

  • Accountability: Who is responsible for AWS errors causing civilian casualties? Fully autonomous systems challenge the principle of human accountability.

  • Discrimination and Bias: AI algorithms may misidentify targets due to biased training data, risking disproportionate harm.

  • Escalation Risks: Autonomous systems may misinterpret threats, triggering unintended conflicts.

  • Moral Implications: Removing humans from lethal decisions raises questions about the ethics of “killer robots.”

• Regulatory Frameworks:

  • International: No binding treaties govern AWS. The UN’s Convention on Certain Conventional Weapons (CCW) debates LAWs, but consensus is elusive. Over 30 countries, including India, support a ban on fully autonomous weapons.

  • India: Governed by MoD’s AI Task Force (2018) and Defence AI Council. Guidelines emphasize human-in-the-loop for lethal actions, but lack specific AWS laws.

  • Challenges: Balancing innovation with ethical constraints; harmonizing global standards.

Challenges in AWS Development

• Technical:

  • AI reliability in chaotic battlefield conditions (e.g., fog of war).

  • Vulnerability to adversarial AI attacks, such as data poisoning or sensor spoofing.

  • Integration with legacy defence systems.

• Operational:

  • High costs of development and deployment (e.g., $100M+ for advanced drone swarms).

  • Need for real-time decision-making in dynamic environments.

• Geopolitical:

  • Arms race dynamics, with US, China, and Russia competing for dominance.

  • Risk of proliferation to non-state actors, increasing terrorism threats.

India’s Strategic Context

• Regional Threats:

  • China’s AI-driven drones and cyber-physical systems along the LAC necessitate robust AWS.

  • Pakistan’s use of low-cost drones for cross-border terrorism requires autonomous countermeasures.

• Policy Initiatives:

  • National AI Strategy (2023) prioritizes defence applications, targeting 100 AI-based systems by 2030.

  • iCET (India-US Critical and Emerging Technology) collaboration enhances AI and autonomy R&D.

• Opportunities:

  • Leverage India’s IT sector for AI algorithm development.

  • Cost-effective AWS for regional deterrence and asymmetric warfare.

Future Outlook

• Short-Term (5–10 Years):

  • Semi-autonomous systems (human-on-the-loop) dominate, with India deploying Rakshak swarms and AI-enhanced Tejas by 2027.

  • Global focus on defensive AWS (e.g., missile interception, cyber defence).

• Long-Term (10–15 Years):

  • Fully autonomous systems may emerge, pending ethical and regulatory resolutions.

  • Integration with quantum computing for faster AI processing.

• Strategic Implications:

  • AWS will redefine warfare, emphasizing speed and precision.

  • India must balance innovation with ethical governance to maintain global credibility.

Autonomous Weapon Systems, powered by AI, are transforming warfare by enabling rapid, precise, and scalable operations. India’s advancements, like Rakshak swarms and AI-integrated Tejas, position it to counter regional threats and assert technological prowess. However, ethical concerns (accountability, bias) and technical challenges (reliability, cybersecurity) demand robust governance. As of August 2025, global debates on AWS regulation and India’s strategic investments underscore the need for a balanced approach. For UPSC aspirants, this topic highlights the intersection of technology, ethics, and security, requiring nuanced policy perspectives to ensure responsible innovation.