The metaverse seamlessly integrates physical and digital spaces, enabling AI-driven innovations in virtual interactions, autonomous avatars, and real-time experiences. However, increased reliance on AI brings sweeping cybersecurity challenges, such as adversarial attacks, deep fake impersonation, and AI-driven phishing campaigns. The security of the metaverse is vital for the sustainability of user trust and system integrity. As AI assumes a larger role in virtual environments, proactive cybersecurity measures must be taken to counter emerging threats. This paper introduces DAI-TIRS, a holistic security framework designed to proactively secure the metaverse. DAI-TIRS is the integration of machine learning-based anomaly detection, dynamic honeypots, and predictive threat modeling that detect, classify, and mitigate AI-driven threats in real-time. By utilizing MITRE ATT&CK and the PyTM framework, it constantly learns new emerging threats through advanced behavioral analytics and keeps pace with the adversarial AI model’s evolution. The experimental results from a simulated metaverse environment demonstrate that DAI-TIRS achieves 93% accuracy in threat detection, 90% precision in classifying the severity, and a 36.9% faster threat mitigation response time than the average performance of baseline models, as detailed in the paper. These findings underscore the critical need for adaptive AI-based cybersecurity solutions that will enhance the resilience, trust, and integrity of metaverse ecosystems. This research establishes DAI-TIRS as an advanced cybersecurity framework that has demonstrated its adaptability and effectiveness in countering AI-driven threats across multiple sectors. The source code for DAI-TIRS is available on GitHub: [https://github.com/sharmamohini762/DAI-TIRS-Code]