How AI enhances bot protection and anti-automation measures

Bots and automated attacks have become constant issues for organizations across industries, threatening everything from website availability to sensitive customer data. As these attacks become increasingly sophisticated, traditional bot mitigation methods struggle to keep pace. Businesses face a growing need to protect their applications, APIs, and data without diminishing the efficiency of essential automated parts and bots that enhance user experiences.

That’s where AI comes in. AI-enabled WAAP is a game-changing solution that marries the adaptive intelligence of AI with information gleaned from historical data. This means WAAP can detect and neutralize malicious bot and anti-automation activity with unprecedented precision. Read on to discover how.

The bot problem: why automation threats are growing

Just a decade ago, use cases for AI and bots were completely different than they are today. While some modern use cases are benign, such as indexing search engines or helping to monitor website performance, malicious bots account for a large proportion of web traffic. Malicious bots have grown from simple machines that follow scripts to complex creations that can convincingly simulate human behaviors.

What makes bots particularly dangerous is their ability to evade detection by mimicking human-like patterns. Simple measures like CAPTCHA tests or IP blocking no longer suffice. Businesses need more intelligent systems capable of identifying and mitigating these evolving threats without impacting real users.

Defeating automation threats with AI and machine learning

Today’s bots don’t just click on links. They fake human activity convincingly, and defeating them involves a lot more than just simple detection. Battling modern bots requires fighting fire with fire by implementing machine learning and AI to create defensive strategies such as blocking credential stuffing, blocking data scraping, and performing behavioral tagging and profiling.

Blocking credential stuffing

Credential stuffing is a form of attack in which stolen login credentials are used to gain access to user accounts. AI/ML systems can identify such an attack by patterns, including multiple failed logins or logins from unusual locations. These systems learn with each new attempt, strengthening their defenses after every attack attempt.

Data scraping blocking

Scraping bots can harvest everything from pricing data to intellectual property. AI models detect these through the repetitive patterns of requests or abnormally high frequencies of interactions. Unlike basic anti-scraping tools, AI learns new ways that scraping is done, keeping businesses one step ahead.

Behavioral tagging and profiling

AI-powered systems are quite good at analyzing user behavior. They study the tendencies of session parameters, IP addresses, and interaction rates. For instance, most regular users save session data, while bots do not prioritize this action. The AI system flags suspicious behavior and highlights the user in question for review.

These systems also count the recurrence of certain actions, such as clicks or requests. The AI is supposed to build an in-depth profile for every IP or user and find something out of the ordinary to suggest a way to block or throttle the traffic.

IP rescoring for smarter detection

One of the unique capabilities of AI-driven bot protection is Dynamic IP Scoring. Based on external behavior data and threat intelligence, each incoming IP is accorded a risk score. For example, an IP displaying a number of failed login attempts could be suspicious. If it persists, that score worsens, and the system blocks the traffic.

This dynamic scoring system does not focus on mere potential threats. It also allows IPs to “recover” if their behavior normalizes, reducing false positives and helping to ensure that real users are not inadvertently blocked.

Practical insights: operationalizing AI-driven bot protection

Implementing AI/ML-driven bot protection requires an understanding of both the technology and the operational context in which it’s deployed. Businesses can take advantage of several unique features offered by platforms like Gcore WAAP:

• Tagging system synergy: Technology-generated tags, like the Gcore Tagging and Analysis Classification and Tagging (TACT) engine, are used throughout the platform to enforce fine-grained security policies and share conclusions and information between various solution components. Labeling threats allows users to easily track potential threats, provides input for ML analysis, and contributes data to an attacker profile that can be applied and acted on globally. This approach ensures an interlinked approach in which all components interact to mitigate threats effectively.
• Scalable defense mechanisms: With businesses expanding their online footprints, platforms like Gcore scale seamlessly to accommodate new users and applications. The cloud-based architecture makes continuous learning and adaptation possible, which is critical to long-term protection against automation threats.
• Cross-domain knowledge sharing: One of the salient features of Gcore WAAP is cross-domain functionality, which means the platform can draw from a large shared database of user behavior and threat intelligence. Even newly onboarded users immediately benefit from the insights gained by the platform from its historical data and are protected against previously encountered threats.
• Security insights: Gcore WAAP’s Security Insights feature provides visibility into security configurations and policy enforcement, helping users identify disabled policies that may expose them to threats. While the platform’s tagging system, powered by the TACT engine, classifies traffic and identifies potential risks, separate microservices handle policy recommendations and mitigation strategies. This functionality reduces the burden on security teams while enhancing overall protection.
• API discovery and protection: API security is among the most targeted entry points for automated attacks due to APIs’ ability to open up data exchange between applications. Protecting APIs requires advanced capabilities that can accurately identify suspicious activities without disrupting legitimate traffic. Gcore WAAP’s API discovery engine achieves this with a 97–99% accuracy rate, leveraging AI/ML to detect and prevent threats.
• Leveraging collective intelligence: Gcore WAAP’s cross-domain functionality creates a shared database of known threats and behaviors, allowing data from one client to protect the entire customer base. New users benefit immediately from the platform’s historical insights, bypassing lengthy learning curves. For example, a flagged suspicious IP can be automatically blocked across the network for faster, more efficient protection.

Futureproof your security with Gcore’s AI-enabled WAAP

Businesses are constantly battling increasingly sophisticated botnet threats and have to be much more proactive regarding their security mechanisms. AI and machine learning have become integral to fighting bot-driven attacks, providing an unprecedented level of precision and flexibility that no traditional security systems can keep up with. With advanced behavior analysis, adaptive threat models, and cross-domain knowledge sharing, Gcore WAAP establishes new standards of bot protection.

Curious to learn more about WAAP? Check out our ebook for cybersecurity best practices, the most common threats to look out for, and how WAAP can safeguard your businesses’ digital assets. Or, get in touch with our team to learn more about Gcore WAAP.

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