Survey of AI-Driven Threat Intelligence Systems: Techniques for Real-Time Cyber Attack Prediction

Cyber risks increased rapidly due to the fast digitization of global infrastructure. When faced with sophisticated, ever-changing cyberattacks, traditional security measures are falling short. The advent of AI has been a game-changer in the cybersecurity industry, paving the way for adaptive protection mechanisms, proactive threat prediction, and automated detection. As such, traditional ways to cyber defense in the current climate. This article is therefore aimed at studying threat intelligence systems that are artificial intelligence (AI) powered, in the context of real-time prediction of cyber-attacks. Plus, we'll go over the function and advantages of ML, DL, and NLP (natural language processing). Moreover, this paper analyzes various threat intelligence cyber frameworks and data sources, emphasizing their success in recognizing complex patterns of attacks and providing valuable insights into this problem. Moreover, a conceptual framework that would help integrate multiple datasets and hybrid AI models and threat intelligence platforms were introduced. Lastly, a review of benefits, obstacles and implementation on a scale of such AI-powered solutions is presented.