Exploring Zero Trust Artificial Intelligence-Based Frameworks in Large-scale Dynamic Networks for Enhancing Cybersecurity

Zero Trust AI-based models play a pivotal role in providing security for large-scale dynamic networks by continuously authenticating and verifying every network entity. Such models banish implicit trust, lowering cyber threat and unapproved access chances. APTs and other complex attacks pose a major challenge, especially when utilizing models that primarily utilize fixed techniques such as signature based intrusion detection. In regards to these limitations, this paper proposes a model called RL-AD, or Reinforcement Learningbased Anomaly Detection, which autonomously modifies the behaviors of networks to detect outlier events in real time. Our proposed RL-AD model has been found to be beneficial in protecting high scale dynamic environments such as enterprise cloud infrastructure, IoT networks, and even 5G communication systems by embedding Zero Trust principles that AI powered policy enforcement systems evaluate access requests constantly. The experimental evidence clearly shows RL-AD strengthens the effectiveness of anomaly detection, reduces false positives, improves real-time threat mitigation, forming a truly effective cybersecurity approach. This study illustrates the efficacy of utilizing Zero Trust AI based models to secure essential infrastructure against modern cybersecurity threats.