Image for representation.
Credit: iStock Photo
Intelligence failures have long challenged India’s national security apparatus. The Kargil War, the IC-814 hijacking, the 2001 Parliament attack, the 26/11 Mumbai attacks, the Pulwama bombing, and the 2020 Chinese intrusion in Ladakh and the subsequent Galwan Valley clash — each of these events reveal the dire consequences of fragmented intelligence and poor interagency coordination. Despite the failures, India’s intelligence operations remain fragmented, hindered by siloed information and bureaucratic inertia.
India must embrace an advanced, AI-enabled intelligence-sharing network that integrates information from all security agencies. Such a platform, driven by Artificial Intelligence (AI) and Machine Learning (ML), can revolutionise how intelligence is gathered, processed, and disseminated, offering real-time updates, improving threat assessments, and ensuring faster, more coordinated responses.
The classical intelligence cycle is at the heart of any intelligence operation: identifying critical intelligence requirements, followed by collection, collation, processing, and dissemination. In India’s current model, this cycle is often delayed by manual processing, lack of coordination, and overwhelming volumes of data.
AI and ML can streamline this process, dynamically prioritising intelligence requirements and allocating resources more efficiently. AI’s ability to handle vast amounts of data enables it to analyse and process information faster than a human team can. This would allow intelligence agencies to cut through the clutter and focus on high-priority, actionable intelligence, ensuring that time-sensitive information reaches decision-makers immediately. For example, in border situations like the Chinese intrusion, AI could integrate satellite and drone imagery with on-ground intelligence to detect troop movements earlier, providing a clearer picture of developing threats.
Modern intelligence is no longer limited to traditional means like HUMINT (human intelligence) or SIGINT (signals intelligence). The vast data generated through open-source intelligence (OSINT), particularly social media, cyber intelligence, and satellite imagery, must be harnessed more effectively. AI-powered systems can analyse and collate this data to identify emerging threats that might go unnoticed.
For example, AI could continuously monitor social media platforms for keywords, behavioural patterns, or suspicious communications, providing early warnings of potential attacks. Similarly, AI can analyse satellite imagery to detect unusual activities, such as military build-ups or infrastructure developments in sensitive areas.
These technologies would enable security forces to correlate data from various sources, generating comprehensive intelligence that is immediately actionable.
One of AI’s primary benefits is its ability to declutter the vast flow of information that intelligence agencies must sift through. By prioritising and filtering intelligence, AI can prevent the problem of “too much information,” ensuring that decision-makers at all levels receive clear, concise, and relevant updates. This decluttering is particularly important at the tactical level, where excessive data can delay responses.
An AI-enabled platform would ensure real-time updates, allowing operational units to act immediately. The system can generate automated, actionable alerts by feeding raw intelligence into a central repository, enabling swift defensive manoeuvres, intelligence operations, or pre-emptive strikes. This real-time capability is essential for responding to threats such as infiltration attempts, terror plots, or cyber-attacks with minimal lag time.
AI would also facilitate lateral information-sharing, allowing agencies to quickly disseminate critical intelligence without compromising ongoing operations. This is crucial in situations where time is of the essence, such as terror attacks or border skirmishes, where delays in communication between agencies have previously resulted in missed opportunities to pre-empt or mitigate threats.
While the benefits of such an AI-enabled system are clear, significant challenges exist, especially in building the backbone communication architecture. India’s security forces operate across diverse and often remote terrains, from the deserts of Rajasthan to the icy Himalayan border regions. Developing a secure, high-bandwidth network capable of real-time data sharing across these varied landscapes is essential.
Furthermore, protocols for data-sharing must be carefully designed to ensure security and trust among agencies. Information must flow freely, but safeguards must be in place to prevent sensitive data from being compromised or misused. Clear dissemination protocols governed by AI would ensure that only relevant and time-critical intelligence reaches those who need it without overwhelming field units.
India’s intelligence failures—from Kargil to Pulwama—have highlighted the urgent need for reform in how information is gathered, processed, and shared. An AI-enabled intelligence-sharing platform offers a solution that breaks down the silos between agencies, ensuring that information flows seamlessly and in real-time. By integrating modern intelligence sources like social media, satellite imagery, and cyber intelligence, this system would provide a more comprehensive and dynamic approach to national security.
To secure India’s future, investment in AI-driven intelligence capabilities is no longer optional—it is imperative. The stakes are too high to continue with a fragmented system that reacts to threats after they materialise. The time has come to build a proactive, coordinated, and tech-driven security apparatus to prevent the next attack.
(The writer is adjunct scholar, strategic studies, at the Takshashila Institution)