The Indian Army is fast-tracking AI integration following lessons from Operation Sindoor (May 2025), a cross-border operation targeting terror infrastructure in Pakistan and PoK. A detailed roadmap for deploying AI, ML, and Big Data Analytics by 2026-27 has been outlined.

Indian Army’s AI Roadmap 2025–27

• Purpose: To integrate Artificial Intelligence (AI) into the Indian Army’s operations to enhance combat readiness, efficiency, and strategic decision-making.
• Timeline: 2025–27, aligning with India’s broader National AI Strategy and Atmanirbhar Bharat (Self-Reliant India) initiative.
• Vision: Transform the Indian Army into a technologically advanced, AI-driven force capable of addressing modern warfare challenges.

Key Objectives of the AI Roadmap

• Operational Enhancement: Deploy AI for real-time intelligence, surveillance, and reconnaissance (ISR).
  • Enhance situational awareness through AI-based predictive analytics.
• Autonomous Systems: Develop and deploy AI-enabled unmanned systems (drones, robots, vehicles) for combat and non-combat roles.
  • Focus on swarm intelligence for coordinated operations.
• Cyber and Electronic Warfare: Strengthen AI-driven cybersecurity to counter advanced cyber threats.
  • Use AI for electronic warfare, including signal jamming and threat detection.
• Logistics and Maintenance: Optimize supply chains and logistics using AI for predictive analytics.
  • Implement AI for predictive maintenance of critical assets like tanks, aircraft, and weapons.
• Training and Capacity Building: Establish AI-focused training programs for personnel across ranks.
  • Use AI-based simulations for war-gaming and tactical training.

Key Defence AI Projects 

• Combat Information Decision Support System (CIDSS): A command and control tool under development by the Indian Army.
  • Integrates various battlefield inputs for: Situational awareness, Tactical planning, AI-based threat prediction.
  • Core aim: Enable commanders to make data-driven decisions in near-real-time.
• Operational Data Fusion Project: Seeks to consolidate sensor inputs from ground, aerial, and satellite sources.
  • Utilises AI-based fusion algorithms to reduce information overload and improve clarity in high-pressure situations.
  • Particularly relevant for multi-domain operations.
• AI for Surveillance and Image Analysis: Projects under DRDO and BEL (Bharat Electronics Limited):
  • Use of computer vision for UAV/drone footage interpretation.
  • Object recognition, target classification, and automated movement tracking.
  • Supports border security and internal counter-insurgency missions.
• AI-based Logistics Management System: 
  • AI tools to optimise: Troop movement, Ammunition & fuel supply chains, Predictive maintenance for vehicles and aircraft
  • Aimed at reducing dependency on manual updates and delays.
• Cyber Defence AI Projects: CERT-IN & NTRO (National Technical Research Organisation) involved in building:
  • AI-driven intrusion detection systems
  • Threat modelling and attack prediction tools
  • Deep packet inspection for encrypted threat detection
• Speech and Natural Language Processing (NLP) Applications for Military Use: AI-based speech-to-text and multilingual translation tools under trial.
  • Built to assist:
    • Field operations in linguistically diverse regions.
    • Interoperability among defence forces and intelligence units.
• Autonomous Combat Drones (in R&D phase): AI-led drone development by DRDO and private defence startups.
  • Targets include Autonomous navigation, Target recognition and engagement, Swarm coordination (inspired by US/Israeli doctrines)
  • AI Capability in Swarm Drones: Provides an aerial manoeuvre capability during offensive as well as defensive tasks.
  • Project Storm Drone: AI-enabled automated room intervention drone systems with lethal and non-lethal payload are used to carry out building clearance and urban surveillance in GPS denied areas. 
• AI Tools for Operational Efficiency: LLM-based text summarizers for intelligence reports.
  • Chatbots, facial recognition, voice-to-text systems for operational support.
• Simulation and Wargaming Platforms: Use of AI to simulate battlefield conditions for training.

Applications of AI in Defence

• Intelligence, Surveillance, and Reconnaissance (ISR)
  • Real-Time Data Analysis: AI processes satellite imagery, drone feeds, and signals intelligence for real-time threat detection and situational awareness.
    • Example: AI-based analysis of border surveillance data to identify potential threats along India’s borders.
  • Predictive Analytics: Uses machine learning to predict enemy movements, assess threats, and enhance situational awareness.
    • Example: Indian Army’s AI-driven ISR systems under the C4ISR framework for real-time battlefield intelligence.
  • Automated Target Recognition: AI algorithms identify and classify targets from visual or sensor data, reducing human error.
    • Example: Integration in drone surveillance systems like DRDO’s Netra UAV.
• Autonomous Systems
  • Unmanned Aerial Vehicles (UAVs): AI-powered drones for reconnaissance, surveillance, and targeted operations.
  • Unmanned Ground Vehicles (UGVs) and Naval Systems: AI-driven robotic vehicles for logistics, mine detection, and combat support in hazardous areas.
    • Example: Jaguar robot, a semi-autonomous unmanned ground vehicle (UGV), is primarily used by the Israel Defense Forces (IDF) for border patrol missions along the Gaza Strip border.
  • Swarm Intelligence: Coordinated operations of multiple autonomous units using AI for tasks like area domination or attack.
    • Swarm drones have proven successful in conflicts such as the Russian-Ukraine conflict.
• Cybersecurity
  • Intrusion Detection and Response: AI systems detect and neutralize cyber threats, including malware and hacking attempts, in real time.
    • Example: Indian Army’s “Cyber Shield” initiative to protect military networks from AI-driven cyberattacks.
  • Countering Adversarial AI: AI tools to identify and mitigate enemy cyberattacks that leverage AI technologies.
  • Electronic Warfare: AI for signal jamming, interception, and countering enemy electronic systems.
• Logistics and Supply Chain Management
  • Supply Chain Optimization: AI optimizes resource allocation, inventory management, and supply chain logistics for efficient operations.
    • Example: Smart logistics systems under the Indian Army’s AI Roadmap for real-time supply tracking.
  • Predictive Maintenance: AI predicts equipment failures (e.g., tanks, aircraft) to reduce downtime and maintenance costs.
    • Example: US Air Force’s Condition-Based Maintenance Plus (CBM+) program to optimize fleet maintenance, increase aircraft availability, and minimize downtime.
  • Resource Allocation: AI-driven systems ensure optimal distribution of resources in combat and non-combat scenarios.
• Decision Support Systems
  • Real-Time Decision-Making: AI provides commanders with actionable insights for rapid decision-making in dynamic combat environments.
    • Example: AI-based C4ISR systems for real-time battlefield strategy formulation.
  • Risk Assessment and Modeling: AI models simulate scenarios to assess risks and plan strategies.
  • Command and Control Enhancement: AI integrates data from multiple sources to support centralized command and control.
• Training and Simulation
  • Immersive Training: AI-powered virtual reality (VR) and augmented reality (AR) for realistic training environments.
  • War-Gaming Simulations: AI creates complex scenarios for strategic and tactical training, improving preparedness.
  • Personalized Training: AI tailors training programs based on individual soldier performance and skill gaps.
• Health Monitoring and Medical Support
  • Soldier Health Monitoring: AI-driven systems monitor soldiers’ health in real time, especially in remote or combat zones.
    • Example: Project BHISHM and BHISHM 2.0 for AI-based health diagnostics and monitoring.
  • Medical Decision Support: AI assists in diagnosing injuries and recommending treatments in field hospitals.
    • AI tools for triage and medical logistics in high-intensity conflict zones.

Challenges in Defence AI Deployment 

• Lack of Defence-Specific Data Ecosystem: AI systems require large, clean, and mission-oriented datasets, but military data in India is fragmented, often classified, and lacks standardisation.
  • This limits AI training for object recognition, threat prediction, or battlefield simulations, especially in Indian terrain and conflict conditions.
• Integration Challenges with Legacy Platforms: Most Indian defence platforms were not built with AI in mind, making retrofitting complex and costly. 
  • Legacy systems like tanks, aircraft, and artillery face challenges in embedding AI modules for predictive maintenance or sensor fusion.
• Ethical and Legal Ambiguity in Battlefield Use: There are no clear legal frameworks or accountability norms for autonomous systems using lethal force. 
  • Use of AI for targeting raises concerns under international humanitarian law, especially if non-combatants are misidentified or collateral damage occurs.
• Lack of Transparency and Explainability: Many AI systems, especially deep learning models, lack explainability and transparency in decision-making.
  • This creates hesitation among commanders to rely on AI during critical operations where human judgment and traceability are essential.
• Vulnerabilities to Cyber and Adversarial Attacks: AI can be manipulated through spoofing, data poisoning, and adversarial inputs, compromising its reliability.
  • Drones or surveillance systems may be misled by altered visual inputs or hacked to misclassify threats or misfire targeting protocols.
• Shortage of Skilled AI-Ready Defence Workforce: There is a critical gap in personnel trained to develop, interpret, or deploy AI tools in military settings.
  • Operational commanders often lack familiarity with algorithms, reducing effective deployment and oversight of AI-enabled systems.
• Dependence on Foreign Hardware and Software: India relies heavily on imported AI chips, processors, and proprietary algorithms, risking strategic vulnerabilities.
  • In scenarios of geopolitical tension, access to key AI infrastructure may be restricted or compromised by supply chain disruptions or embedded malware.

Government Support and Initiatives for AI in Defence

• Innovations for Defence Excellence (iDEX 2018): A flagship initiative under the Ministry of Defence to foster innovation in defence by engaging startups, MSMEs, and innovators.
  • Role in AI:
    • Supports development of AI-based defence solutions, including autonomous systems, cybersecurity, and ISR technologies.
    • Provides funding, mentorship, and market access to startups working on AI projects.
• Defence AI Council (2019): An advisory body established by the Ministry of Defence to guide the adoption of AI in defence operations.
  • Functions:
    • Formulates policies for AI integration in military applications.
    • Coordinates between the Indian Army, DRDO, and private sector for roadmap implementation.
• AI Task Force (2018): Formed by the Government of India to recommend strategies for AI adoption in defence and national security.
  • Key Recommendations:
    • Integration of AI in ISR, cybersecurity, and autonomous systems.
    • Establishment of AI research centers and training programs for defence personnel.
    • Promotion of public-private collaborations for AI innovation.
• Defence AI Project Agency (DAIPA 2019):  DAIPA was established to provide the necessary guidance and implementation framework for AI adoption in defense organizations.  
  • It serves as the central execution body for AI projects and initiatives across the Indian military. 
• AI Research and Development in DRDO:
  • Centre for Artificial Intelligence and Robotics (CAIR), Bengaluru (1986): Conducts workshops to train DRDO scientists in AI for defense systems and supports start-ups by nurturing innovation and collaboration.
  • DRDO Young Scientist Laboratories (DYSL):
    • DYSL-AI: Focused on AI-related research and applications.
    • DYSL-CT (Cognitive Technology): Concentrates on cognitive technology advancements for defense.
  • Defence Institute of Advanced Technology (DIAT): Offers certified courses in AI and machine learning.

Global Use of AI in Military and Defence

• United States: AI for Autonomous Systems & Decision Dominance
  • The US Department of Defense (DoD) is spearheading AI integration via its Joint Artificial Intelligence Center (JAIC) and Project Maven.
  • AI is used for drone surveillance (e.g., in Afghanistan), battlefield data fusion, autonomous naval vessels, and predictive logistics.
  • DARPA’s OFFSET program enables drone swarming for urban warfare simulations.
• China: Military–Civil Fusion & AI-Driven Strategic Ambitions
  • China’s 2017 AI Strategy aims to make it the global AI leader by 2030, including military superiority through intelligentised warfare.
  • PLA uses AI for facial recognition, satellite tracking, cyber operations, and decision-making tools.
  • Collaborations with tech giants like Baidu and Alibaba aid military-grade AI applications under “military–civil fusion” doctrine.

• Russia: AI in Electronic Warfare & Unmanned Systems
  • Russia uses AI for electronic countermeasures, battlefield simulations, and autonomous ground vehicles like Uran-9.
  • AI is embedded in cyber operations, disinformation campaigns, and hypersonic weapons decision-support systems.
  • Russia also tests AI-based command-and-control in joint exercises.
• Israel: AI-Integrated Precision Warfare
  • Israel leads in battlefield AI, notably through its use in the Iron Dome and Harpy autonomous loitering munitions.
  • AI is used in image processing, surveillance drones, and predictive border monitoring.
  • In Operation Guardian of the Walls (2021), Israel reportedly deployed AI to generate rapid targeting databases.
• United Kingdom: AI in Strategic Command and ISR
  • The UK’s Strategic Command is experimenting with AI to enhance Intelligence, Surveillance and Reconnaissance (ISR) and threat assessment.
  • AI-driven cyber defence systems are being tested for use in NATO environments.
  • The UK is also investing in ethical frameworks for AI in lethal systems.

Way Forwards

• Develop Clear Legal Frameworks: Formulate national and international regulations for AI-driven defence systems, defining accountability for autonomous actions.
  • Advocate for binding agreements in UNCCW (Convention on Certain Conventional Weapons) discussions to regulate Lethal Autonomous Weapons Systems (LAWS), ensuring compliance with International Humanitarian Law. 
• Enhance AI Explainability: Invest in explainable AI (XAI) to make decision-making processes transparent, increasing commander trust and accountability.
  • DRDO’s Centre for Artificial Intelligence and Robotics (CAIR) to prioritize XAI research for systems like Command Information and Decision Support System (CIDSS) and surveillance tools.
• Mitigate Algorithmic Bias: Use diverse, standardized datasets for AI training, incorporating India-specific terrains and conflict scenarios.
  • Develop a centralized defence data ecosystem under the Operational Data Fusion Project to ensure high-quality, unbiased data.
• Strengthen IHL Compliance: Embed IHL principles into AI algorithms, ensuring systems prioritize distinction, proportionality, and necessity.
  • Align AI projects (e.g., autonomous drones, C4ISR) with IHL through mandatory human-in-the-loop protocols.
• Promote Human Oversight:  Mandate human-in-the-loop mechanisms for all AI-driven lethal systems to ensure ethical decision-making.
  • Incorporate human oversight protocols in projects like Project Storm Drone and swarm drones.
• Secure Data Privacy: Build secure data lakes and implement strict access controls for AI-processed military data.
  • Strengthen the Operational Data Fusion Project with cybersecurity measures to protect sensitive data (e.g., ISR, logistics).
• Capacity Building for Ethical AI:  Train personnel on ethical AI use and legal implications to bridge skill gaps and build trust.
  • Leverage AI Centers of Excellence and Defence Institute of Advanced Technology (DIAT) for ethical AI training programs.
• Global Collaboration and Advocacy: Engage in international forums like UN CCAC to shape ethical AI norms and prevent an AI arms race.
  • Propose human-centric AI guidelines, reinforcing India’s commitment to responsible AI in defence.

Conclusion

Artificial Intelligence is transforming defence by enabling faster decision-making, autonomous operations, and enhanced situational awareness. India’s 2025–27 AI roadmap positions it to leverage cutting-edge technologies while addressing ethical, infrastructural, and strategic challenges. Its success hinges on indigenisation, interoperability, and capacity-building.

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