Think of SurakshaNetra as an AI-powered early warning radar for cyberattacks on Indian networks. It constantly scans digital traffic, learns what “normal” looks like, spots suspicious activity in real time, and alerts defenders before small issues turn into major breaches.
Traditional cyber defenses in India are mostly reactive, siloed, and signature-based, making it hard to detect new, fast-evolving threats and coordinate responses at national scale. SurakshaNetra aims to provide continuous, AI-driven, real-time threat intelligence so organizations can detect, prioritize, and respond to cyber threats proactively.
If successfully deployed, defensibility would come from access to large-scale, Indian-specific network telemetry and incident data, integration with local ISPs/critical infrastructure, and domain-specific threat models tuned for regional threat actors and languages.
Classical-ML (Scikit/XGBoost)
Vector Search
High (Custom Models/Infra)
High-volume streaming data ingestion and real-time inference across many networks can create bottlenecks in storage throughput, feature computation, and model latency; data privacy and cross-organization data sharing are additional constraints.
Early Adopters
Positioned as a national or India-focused real-time threat intelligence platform, emphasizing local context (regulators, infrastructure, threat actors) rather than a generic global enterprise security product.