Digital arrest in India: Driven cybersecurity for national digital security

India's rapid digital transformation has led to a significant increase in cybercrimes, including deepfake-enabled scams such as Digital Arrest fraud, highlighting the urgent need for advanced security solutions. This paper presents a novel Artificial Intelligence (AI)-driven cybersecurity framework specifically designed for real-time deepfake detection and anomaly analysis. The system employs advanced machine learning (ML) and deep learning (DL) techniques to identify inconsistencies in multimedia content, such as facial discrepancies, and detect unusual user behaviors, like suspicious financial transactions. Hypothetical results demonstrate that this AI approach yields superior threat detection rates, such as >95%, and significantly reduced false positives and response times, thereby minimizing financial losses and data breach costs. To address privacy concerns, the study emphasizes privacy-preserving AI methods. Future research will focus on enhancing AI model interpretability and exploring hybrid human-AI systems to contribute to safer digital environments and support sustainable digital transformation.