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Description

Check Point WAF is an AI-driven web application firewall and API security solution designed to protect web applications, APIs, and generative AI services from sophisticated cyber threats. The platform leverages advanced machine learning and contextual AI technologies to analyze incoming traffic in real time, identifying malicious activity without depending on static signatures or manually maintained rule sets. By combining supervised and unsupervised AI models, Check Point WAF can detect and block both known and previously unseen threats, including zero-day attacks, OWASP Top 10 vulnerabilities, bot-driven attacks, distributed denial-of-service attempts, malicious file uploads, and API-specific security risks. The solution continuously adapts to the unique behavior of protected applications, reducing false positives and minimizing administrative burden. Built as a cloud-native platform, it supports infrastructure-as-code deployments, DevOps automation, and CI/CD integration, allowing organizations to deploy security controls rapidly and efficiently across modern application environments. Additional capabilities include API discovery and protection, GenAI application security, intrusion prevention, file reputation analysis, and advanced DDoS mitigation. With its automated security model and low-maintenance architecture, Check Point WAF enables organizations to secure critical digital assets, improve operational efficiency, and maintain a strong security posture in dynamic cloud and hybrid environments.

Description

open-appsec is an open-source initiative that builds on machine learning to provide pre-emptive web app & API threat protection against OWASP-Top-10 and zero-day attacks. It can be deployed as add-on to Kubernetes Ingress, NGINX, Envoy and API Gateways. The open-appsec engine learns how users normally interact with your web application. It then uses this information to automatically detect requests that fall outside of normal operations, and sends those requests for further analysis to decide whether the request is malicious or not. open-appsec uses two machine learning models: 1. A supervised model that was trained offline based on millions of requests, both malicious and benign. 2. An unsupervised model that is being built in real time in the protected environment. This model uses traffic patterns specific to the environment. open-oppsec simplifies maintenance as there is no threat signature upkeep and exception handling, like common in many WAF solutions.