This systematic review synthesizes 45 peer-reviewed studies (2019–2024) on AI-driven Network Intrusion Detection Systems (NIDS) for enterprise cybersecurity. Advanced cyber threats, including zero-day exploits, adversarial AI, and ransomware, render traditional signature-based methods inadequate. AI-based NIDS, particularly hybrid models combining Machine Learning (ML) and Deep Learning (DL), exhibit superior detection accuracy, adaptability, and real-time responsiveness. Employing a PRISMA-guided methodology, this study evaluates hybrid ML-DL systems, zero-day detection techniques, adversarial countermeasures, and Explainable AI (XAI) frameworks. The meta-analysis indicates hybrid models achieve a mean accuracy of 96.2%, an F1-score of 0.94, and a 2.1% false positive rate, outperforming standalone ML (88.7% accuracy) and DL (92.5% accuracy) models by 10–15%. Real-world case studies in healthcare and smart cities, alongside cost-benefit analyses, demonstrate practical applicability. Standardized benchmarking protocols address dataset bias and adversarial vulnerabilities, validated in financial and healthcare sectors. The review proposes ethical AI frameworks, a future research roadmap, and deployment guidelines for enterprise Security Operations Centers (SOCs).

AI Cybersecurity Hybrid Models Zero-Day Detection Network Intrusion Detection System Explainable AI