Adaptive Deception: Real-Time, AI-Powered Cybersecurity for Modern Threat Landscapes

INTRODUCTION: The current volume and sophistication of cyber threats are beyond overshadowing the security capabilities of traditional reactive security approaches. Herein, we present a new cybersecurity framework that incorporates real-time threat intelligence with adaptive deception technologies for the proactive defense of digital infrastructures. OBJECTIVES: The objectives of this research include: (1) develop an AI-driven cybersecurity framework, (2) incorporate real-time threat intelligence and deception-based active defense approaches, and (3) assess performance in simulated and real-world cyber-attack scenarios. METHODS: The proposed cyber-defense framework uses machine learning approaches, automated deception technologies (e.g., honeypots, moving target defense), and real-time threat intelligence feeds. The framework is constructed in a modular architecture and tested in simulation environments with real-time attack emulation. RESULTS: The framework performed with over 93% of threats visible, an adaptive response time < 2 seconds, and < 12% overhead imposed on the system. The framework achieved > 85% threat prevention, measured long recovery time, and measured system integrity improvements. CONCLUSION: The conclusion of this work illustrates that a proactive cybersecurity framework can be achieved through the integration of AI-enabled adaptive response with real-time threat intelligence. This work represents an advancement toward intelligent, self-learning systems capable of anticipating and responding to developing cyber threats with minimal human intervention.