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APIs (Application Programming Interfaces) have become a key component in the development of modern digital products and the transformation of cloud-based services. Its ability to provide structured access to data and enable cross-platform integration makes APIs at the core of the enterprise's digital architecture. However, the high level of API openness poses increasingly complex security challenges, including potential data exploitation, injection attacks, credential misuse, and exploitation of business logic loopholes. This article examines the strategic role of APIs in the digital ecosystem, analyzes the operational risks that arise from API exposure, and evaluates the effectiveness of basic defense mechanisms such as API Gateways and Web Application Firewalls (WAFs). The findings of the study show that while both solutions play an important role in controlling access, filtering, and mitigating attacks at the surface layer, they have not been able to provide comprehensive protection against modern API threats that are dynamic, distributed, and often exploit weaknesses at the application and business logic levels. Therefore, a more holistic, layered, and sustainable API security approach is needed, including anomalous behavior detection, API abuse protection, and real-time monitoring to maintain the integrity and reliability of digital services.  

The rapid growth of web technologies and online services has increased the exposure of web applications to cyber threats such as Cross-Site Scripting (XSS) and SQL Injection (SQLi). Conventional rule-based mechanisms, such as Web Application Firewalls (WAFs), often fail to detect emerging attack patterns. To address this, Machine Learning (ML) and Deep Learning (DL) have emerged as adaptive approaches for enhancing web attack detection. This study performs a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to analyze recent ML/DL-based detection methods. Of the 263 retrieved studies, 15 met the inclusion criteria for detailed review. The findings reveal that Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) are the most applied algorithms. At the same time, recent works emphasize Transformer-based and hybrid ML–DL models. These approaches achieved robust performance (accuracy 85–97%, F1-score >90%) but still face challenges in dataset representativeness, class imbalance, and computational cost. This review highlights future research directions in Explainable Artificial Intelligence (XAI), Federated Learning (FL), and adversarial robustness to develop more efficient and trustworthy web attack detection systems.