Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defenses

TL;DR

This survey reviews AI-driven security defenses at the web edge, highlighting their effectiveness in detection and mitigation, while also discussing new risks and a future research agenda.

Contribution

It provides a systematic survey method, threat taxonomy, evaluation metrics, deployment playbooks, and governance guidance for AI-enhanced web security defenses at the edge.

Findings

Edge AI improves detection and mitigation speed

Reduces data movement and enhances compliance

Introduces new risks like model abuse and poisoning

Abstract

The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where automated, AI-assisted attacks evolve continuously. Content Delivery Networks (CDNs) and edge computing place programmable defenses closest to users and bots, making them natural enforcement points for machine-learning (ML) driven inspection, throttling, and isolation. This survey synthesizes the landscape of AI-enhanced defenses deployed at the edge: (i) anomaly- and behavior-based Web Application Firewalls (WAFs) within broader Web Application and API Protection (WAAP), (ii) adaptive DDoS detection and mitigation, (iii) bot management that resists human-mimicry, and (iv) API discovery, positive security modeling, and encrypted-traffic anomaly analysis. We add a systematic survey method, a threat taxonomy mapped to edge-observable signals, evaluation metrics, deployment playbooks, and governance guidance. We conclude with a research agenda spanning XAI, adversarial robustness, and autonomous multi-agent defense. Our findings indicate that edge-centric AI measurably improves time-to-detect and time-to-mitigate while reducing data movement and enhancing compliance, yet introduces new risks around model abuse, poisoning, and governance.