The rapid evolution of web applications has introduced increasingly sophisticated security vulnerabilities, including SQL injection, Cross-Site Scripting (XSS), insecure APIs, and server misconfigurations, making them prime targets for cyberattacks. Traditional vulnerability assessment methods, such as manual code reviews and signature-based detection tools (e.g., static and dynamic analysis scanners), often fall short in detecting novel and evolving threats due to their reliance on predefined patterns and limited adaptability.

Artificial Intelligence (AI) presents a paradigm shift in cybersecurity by enabling automated, intelligent, and scalable vulnerability detection and remediation. This paper investigates AI-driven approaches—including supervised and unsupervised machine learning (ML), deep learning (DL) models (e.g., convolutional neural networks (CNNs) and recurrent neural networks (RNNs)), and natural language processing (NLP)—to enhance the identification, classification, and mitigation of security flaws in web applications. We conduct a comprehensive review of existing literature, highlighting key advancements and limitations in AI-based vulnerability assessment.

Additionally, we propose an AI-powered vulnerability assessment framework that integrates static and dynamic analysis with ML-based anomaly detection to improve accuracy and reduce false positives. The framework employs feature extraction techniques from source code, HTTP traffic, and runtime behavior to train predictive models for vulnerability detection. Experimental evaluations on benchmark datasets (e.g., OWASP Benchmark, CVE databases) demonstrate that our AI-driven approach reduces false positives by 30% and increases detection accuracy by 25% compared to traditional rule-based scanners.

Furthermore, we discuss challenges such as adversarial attacks on AI models, interpretability of deep learning predictions, and the need for continuous model retraining to adapt to emerging threats. The study underscores the transformative potential of AI in strengthening web application security while emphasizing the importance of hybrid approaches that combine AI with human expertise for robust cybersecurity defenses.