Bot & Abuse Protection

Bot detection is the process of identifying automated traffic across web, mobile, and API endpoints using application telemetry and API signals like behavior, device fingerprinting, and network reputation to stop malicious automation. Distinguishing "good bots" from "bad bots" is often a moving target, varying per industry and business. Continuous signal gathering and intent analysis is needed to fully address the problem.

Bot management, also sometimes referred to as bot protection, combines bot detection and bot mitigation techniques such as explicit block, step-up authentication, dynamic rate limits, or bot challenges while still allowing legitimate users and approved bots like search crawlers or trusted partner integrations.

Business logic abuse protection prevents automated abuse that targets how an application works, primarily through APIs. Frequently targeted functions include login, password resets, change password, account signups, checkout, search, and digital promotion API flows. Attackers exploit APIs and API flows even when no traditional vulnerabilities and misconfigurations are present, making business logic abuse protection is so crucial.

Bots, or attackers using automation, perpetuate account takeover by automating other techniques like credential stuffing, password spraying, and bruteforcing scale. In the case of credentials, attackers often use stolen credentials that have been harvested from other breaches, or they may use other intelligence as a starting point such as social media data. Bots and attacker automations also rotate IPs to evade traditional, basic security controls like IP address block lists and static rate limits.

Credential stuffing is an automated abuse technique that targets login flows using leaked or breached authentication material, typically username (or email) and password pairs, across sites. Stopping credential stuffing typically requires bot detection, anomaly-based login protection, and step-up authentication for high-risk or privileged sessions.

CAPTCHAs alone are not enough because advanced bots and attackers can bypass CAPTCHAs, outsource CAPTCHA solving to human (mechanical turk) services, or mimic real user behavior. CAPTCHAs can damage user experience if not implemented properly, or they rely on client fingerprinting and tracking that raises privacy concerns. Interactive or gamified CAPTCHAs have also emerged, but they can be expensive and still result in abandoned user journeys and transactions. Effective bot protection uses layered detection and adaptive mitigation to preserve user experience and still combat bots & abuse.

API bot & abuse protection pairs API abuse detection with rate limiting, token integrity, anomaly detection, intent analysis, and automated attack fingerprinting to stop scraping, credential attacks, transaction fraud, and high-volume API abuse.

Reducing false positives requires accurate classification of "good" bots (or behaviors/intent) vs "bad" bots, tuning protection policies by API endpoint, and using adaptive bot mitigations so legitimate users aren’t blocked during traffic spikes that may occur normally, such as with market seasonality or product launches. Traditional "false positive" metrics sometimes need to be scrutinized, and organizations may prefer other ROI or fraud measurements. Example metrics include transaction fraud rates, chargeback percentages, infrastructure costs incurred, conversion rates, and support incident rates from account compromises or abuse.

Web scraping or content scraping is automated data extraction from websites commonly but also APIs that serve data. Scraping prevention uses bot detection, intent analysis & controls, and adaptive challenges to block undesirable scraping while still allowing legitimate access such as search engine crawling, AI/LLM crawling, or trusted partner/supplier automations. Organizations in specific industries or regions may also want to block even these legitimate use cases or specific providers to preserve intellectual property or privacy, making the good vs. bad bot argument more complex than it seems.