Threat Intelligence Platform Empowered by Generative Ai with Quantum-Security

Abstract

The rapid evolution in types of cyber threats and anticipated threats through quantum computing requires a change in cybersecurity strategy. The traditional Threat Intelligence Platforms (TIPs) usually use static rule-based systems and reactive measures to provide real-time solutions to address attacks that are either emerging or sophisticated. This paper proposes a next-generation Threat Intelligence Platform (TIP) incorporating Generative Artificial Intelligence (GenAI) to enable predictive threat scenario modelling, anomaly detection, and autonomous mitigation strategies using quantum-security mechanisms to provide very-long-term cryptographic resilience. The GenAI engine provides live threat simulation, adequate situational awareness, and improved anomaly detection accuracy by large-scale cyber datasets. At the same time, the Quantum-Security Module consists of post-quantum cryptographic (PQC) algorithms, such as lattice-based, hash-based, and multivariate cryptosystems. All of these seek to plug holes caused by quantum adversaries. The suggested TIP architecture is a multi-layered one that possesses a data ingestion layer, an AI-driven threat analytics, a quantum-resilient cryptographic framework, and an interactive visualization dashboard. It delineates an architectural framework, important points of innovation, and potential applications across the main sectors of finance, healthcare, defence, and enterprise cybersecurity. Additionally, the research presents a high-level strategic roadmap for the deployment of GenAI-intensified TIPs with quantum-resilient security, scalable and adaptable for future-proof data integrity. Addressing AI-enabled predictive security features and post-quantum cryptographic resilience, the research offers future-proof cybersecurity as an answer to continuous changes in digital threats.