Bot attacks pose significant risks to websites and online applications, with these automated programs capable of overwhelming servers, scraping content, and attempting unauthorized data access. Traditional client-side measures like CAPTCHA challenges are becoming less effective due to advancements in machine learning, while server-side defenses like Web Application Firewalls (WAFs) offer only basic protection by monitoring and filtering HTTP traffic. Fingerprint Bot Detection provides a more advanced client-side solution, distinguishing real users from bots by analyzing browser data to detect inconsistencies and automation tools. By integrating Fingerprint Bot Detection with a WAF, websites can dynamically block IP addresses associated with bots, combining the strengths of both solutions to protect against sophisticated attacks. This integration involves saving detected bot IPs to a database, using a dashboard to monitor and manage these IPs, and updating firewall rules through a service like Cloudflare to block malicious traffic effectively. The process enhances security by blocking bots before they can load web pages, leveraging both client-side and server-side capabilities.