AI-Driven Approaches for Developing Effective Cybersecurity Policies and Procedures

The increasing complexity of cyber threats, coupled with the rapidly increasing pace of digital transformation fueled by cloud computing, IoT, and interconnected enterprise systems, has exposed the limitations of traditional, static cybersecurity policies and procedures. Traditional approaches are largely manual and compliance-oriented, and are not dynamic enough to keep up with the ever-changing nature of cyber threats such as zero-day attacks, advanced persistent threats, and AI-powered social engineering. Our objectives are to design an AI framework for automated generation of cybersecurity policies and procedures, enabling intelligent, data-driven, and dynamic policy design. The proposed system employs Natural Language Processing (NLP) for policy knowledge extraction and a Support Vector Machine (SVM) classifier for policy component classification and validation based on relevance, compliance level, and organizational context. The system design has four layers: input and knowledge acquisition, feature engineering, AI-based decision modeling, and automated policy generation with version control. The experimental results on the NSL-KDD dataset demonstrated that the SVM classifier outperforms Logistic Regression, XGBoost, and Random Forest in terms of accuracy, achieving 99-100%, validating the effectiveness of the proposed system in detecting threat patterns that can serve as a basis for policy formulation. The analysis using the confusion matrix and ROC curve shows that the model is stable, with low false-negative rates, which is significant for proactive cybersecurity governance. The research also considers governance, ethics, and compliance by aligning AI-based policy mechanisms with existing standards such as NIST CSF and ISO. The findings show that AI can play an important role in making policies more responsive, reducing human error, improving compliance, and making policies evolve.