Rising geopolitical volatility, multi-domain threats, and the convergence of physical and digital risks demand governance systems that are not only faster and smarter, but also trusted, accountable, and resilient. In this conversation, Dr. Rajendra Kumar, Secretary, Department of Border Management, Ministry of Home Affairs, Government of India, outlines to Muskan Jaiswal of Elets News Network how India is operationalising Artificial Intelligence as a strategic enabler for border security. He details the use of AI-driven intelligence, surveillance and reconnaissance, predictive analytics, and integrated command platforms to enhance situational awareness, optimise resources, and strengthen border dominance across diverse terrains.

What are the Department of Border Management’s key AI priorities for governance over the next five years, particularly in the context of national security and public safety?

In view of the current geo-political situation and the likelihood of future multi-theatre engagements, the Department of Border Management (DoBM) has envisioned to operationalise Artificial Intelligence (AI) to strengthen ISR (Intelligence, surveillance & reconnaissance), threat detection, border domination and resource optimisation. The strategies to achieve this vision include deploying AI-powered surveillance systems, implementing predictive data analytics to identify patterns and hotspots and to inform domination plans, improving workforce and manpower management using AI-driven resource allocation and logistics prediction, and establishing unified data-integration platforms and real-time intelligence dashboards. These align with our technology deployment objectives and governance processes.

How can AI be deployed in sensitive domains such as border management in a manner that balances innovation with trust, accountability, and human oversight?

AI deployment in border management is being implemented within the existing scheme framework so that innovation is balanced with trust and accountability. AI initiatives will pass through feasibility studies and Proof-of-Concept (PoC) within the existing institutional mechanisms. Initially, AI systems will operate as decision-support tools with mandatory human-in-the-loop review for all critical actions. Deployments will be preceded by data-privacy, cybersecurity and operational safety assessments, and supported by Standard Operating Procedures (SOPs), role-based access and audit trails. Capacity building and training of the personnel within the organisations will be crucial for ensuring the successful deployment and use of these technologies.

Could you highlight one flagship AI use case from your Department that demonstrates clear governance impact and has strong potential for inter-State peer learning?

A governance-impacting, AI-enabled use case under implementation as PoC is technology deployment for surveillance and sensor integration across vulnerable stretches along the border. These include the roll- out of AI analytics on camera feeds, sensor (smart-fence) telemetry, and retrospective satellite / Unmanned Aerial Vehicle (UAV) imagery analysis, etc.

What governance safeguards and institutional frameworks are most critical when scaling AI solutions across borders, agencies, and jurisdictions?

Key governance safeguards and institutional frameworks for scaling AI solutions include strict adherence to the overall institutional architecture of our border infrastructure and management initiatives. These include:

• Mandatory feasibility studies and Detailed Project Reports (DPRs) with statutory clearances before sanction.
• Robust quality assurance through Third-Party Quality Assurance (TPQA) and Project Monitoring Unit (PMU) oversight.
• Financial due diligence through milestone-based funding and conditional releases via the Public Financial Management System (PFMS).
• Technical scalability requires standardised data schemas and interoperability to enable seamless integration of enterprise GIS/ERP systems, secure APIs and unified dashboards across various applications.

How do you see collaboration between the Centre and States shaping India’s AI ecosystem, especially in areas like data sharing, capacity building, and responsible adoption?

Successful AI deployment and adoption are predicated on sound Centre-State collaborations in border infrastructure and management initiatives. These include:

• The Centre (DoBM/MHA) provides funding, technical standards, project appraisals and platform- level capabilities while States facilitate land acquisition, statutory clearances, local execution, etc.
• Joint preparatory steps (joint inspections, feasibility inputs, c omm on DPR t empla t e s), establishment of AI & Machine Learning cells and digital forensics/ training labs within each border guarding force, and shared databases (integration of ERP data for operations, logistics, etc.) will enable responsible data sharing, harmonised capacity building and replicable best practices across all the border guarding forces.

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As AI capabilities advance, what opportunities and risks should policymakers remain most alert to while shaping the next phase of AI-driven governance in India?

India is rapidly developing its AI ecosystem for enhancing indigenous capabilities in AI technologies and fostering local innovations. In the context of border management, AI presents clear opportunities for force multiplication through wider and smarter surveillance coverage, predictive hotspot identification from legacy operations data to prioritise scarce assets, AI-driven manpower optimisation and automated scenario simulation for contingency planning. However, it also needs to address challenges such as data scarcity and data quality (especially in remote border segments), infrastructure and connectivity constraints, need for specialised human resources and sustained training, capital- intensive hardware/software investments, cybersecurity threats, etc.

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