Source: TH
Subject: Science and Tech/National Security
Context: The U.S. military has reportedly integrated Anthropic’s Claude AI into its kill chain for real-time target identification and legal approval during strikes in Iran.
About AI and the National Security Calculus:
What it is?
- The national security calculus refers to the strategic assessment of how AI—a dual-use technology—alters the balance of power between nations. Unlike nuclear technology, which is government-controlled and scarce, AI is driven by the private sector and defined by mathematical models and ubiquitous semiconductors.
Data/Stats on AI and National Security:
- Defense Speed: In the first 24 hours of the 2026 Iran conflict, the U.S. military leveraged AI targeting tools to strike over 1,000 targets, prioritizing them quicker than the speed of thought.
- Industrial Distillation: Anthropic reported 16 million unauthorized exchanges targeting its Claude model from approximately 24,000 fraudulent accounts linked to Chinese labs.
- Indian Cybersecurity Spending: India’s information security spending is projected to reach $3.4 billion in 2026, an 11.7% increase from 2025, driven by sophisticated AI-led threats.
- Compute Power: Under the IndiaAI Mission, India has onboarded over 38,000 GPUs (targeting 100,000) to provide subsidized compute for national security and innovation.
Role of AI in National Security:
- Surveillance and Border Monitoring: AI-enabled drones and satellite imagery provide real-time reconnaissance of difficult terrains.
Example: In early 2026, the Indian Army integrated AI-driven swarm drones for automated reconnaissance along the Line of Actual Control (LAC).
- Predictive Threat Analysis: Using machine learning to identify patterns in terrorist communication and movement.
Example: The National Security Council Secretariat (NSCS) uses AI models to conduct national security impact assessments and scenario-based risk exercises.
- Cyber Defense and Anomaly Detection: Protecting critical infrastructure from polymorphic malware and deepfake-enabled fraud.
Example: The CyberGuard AI Hackathon (2025) led to the deployment of AI-driven SOCs (Security Operation Centres) across India’s power grids to detect intrusions.
- Internal Security and Crowd Control: Real-time facial recognition and behavioral analytics to maintain order during mass gatherings.
Example: During the Maha Kumbh 2025, police used 2,700 AI-enhanced CCTV cameras to monitor crowd density and flag individuals with criminal records.
- Logistics and Autonomous Systems: Streamlining military supply chains and reducing human risk in hazardous zones.
Example: The iDEX (Innovations for Defence Excellence) program has funded startups building AI-powered autonomous underwater vehicles for the Indian Navy.
Initiatives Taken So Far:
- IndiaAI Mission: A ₹10,372 crore flagship program focused on building sovereign compute, foundation models, and Safe and Trusted AI frameworks.
- BharatGen: The world’s first government-funded multimodal large language model, supporting 22 Indian languages to ensure Cognitive Sovereignty.
- U.S.-India iCET (initiative on Critical and Emerging Technology): A bilateral partnership to co-develop defense AI and secure semiconductor supply chains.
- India AI Governance Guidelines (2026): A principle-based framework released at the New Delhi Summit to regulate autonomous weapons and surveillance tools.
Challenges Associated:
- The Black Box Strategic Problem: Difficulty in explaining AI’s decision-making process during lethal operations.
Example: If an AI-powered missile guidance system fails during a border skirmish, determining whether it was a software bug or a hack is nearly impossible.
- Dependence on Foreign Stacks: Relying on proprietary U.S. or open-source Chinese models risks kill switches or covert surveillance.
Example: Analysts at the India AI Impact Summit 2026 warned that using imported models for policing creates an illusion of control that could collapse during a crisis.
- AI-Driven Disinformation: The use of deepfakes to manipulate public sentiment or destabilize the democratic process.
Example: In 2025, security agencies flagged multiple AI-generated deepfake videos designed to incite communal tension during regional elections.
- Evasion of Export Controls: Sophisticated actors can bypass semiconductor restrictions through proxy services or model distillation.
Example: Reports in early 2026 indicated that restricted Nvidia Blackwell chips were being used in Inner Mongolia to train models that rival top U.S. systems.
- Ethical and Human Control Dilemma: The risk of decision compression reducing human legal review to a mere rubber-stamping of machine decisions.
Way Ahead:
- Sovereign AI Infrastructure: India must control its own cognitive infrastructure by training models on locally relevant, diverse Indian datasets.
- Plurilateral Commitments: States must agree on universal red lines, such as maintaining meaningful human control over lethal autonomous weapons.
- Model-Level Safeguards: Developing technical fingerprinting to detect unauthorized model distillation and prevent IP theft.
- AI Red-Teaming: Establishing dedicated units within the Armed Forces to stress-test AI systems against adversarial machine learning attacks.
- Ethical Auditing: Moving toward Responsible AI 2.0, which involves continuous, auditable assurance of AI systems used in public and military sectors.
Conclusion:
The integration of AI into national security marks the end of traditional warfare and the beginning of algorithmic competition. For a nation like India, the challenge lies in balancing the tactical speed of AI with the ethical accountability of human judgment. Ultimately, true security will depend on achieving technological sovereignty and a robust, indigenous AI ecosystem that cannot be overridden by foreign interests.
