The growing sophistication of cyber threats, coupled with evolving compliance regulations, has made manual compliance monitoring increasingly time-intensive and challenging. In this paper, the author looks at the feasibility of applying AI-based solutions to increase cybersecurity compliance in organizations. In view of this, we present a framework that employs NLP and Machine Learning techniques, which scan through legal frameworks to extract compliance rules and concurrently scan through the system logs for non-compliance activities. The proposed framework also helps minimize the organizational compliance process suffering from human factor vulnerabilities and improves the security level of the organization. Also exploring how AI-based anomaly, rule-based systems, and automated remediation policies work in identifying and correcting non-compliance situations in real time. The outcomes indicate, therefore, that AI tools like rule extraction from NLP models for use in determining rules for compliance and Machine Learning models for anomaly detection have the potential to enhance the improvement of compliance monitoring through simplicity, efficiency, and flexibility. Nevertheless, some open issues are still of interest: improving the model accuracy, making the models less sensitive to adversarial attacks, and introducing new and stricter regulatory requirements that must be incorporated into the design of black-box models. The paper also explains the main obstacles in deploying AI-based compliance systems and indicates how these difficulties can be addressed. Thus, AI can significantly empower compliance checks and enhance threat identification potential in organizations, which means great potential for the direction of cybersecurity compliance and risk management in the future.