Prevent AI endpoint scraping

Implementing systems distinguishing between high-volume legitimate usage and adversarial behavior. For example, using behavioral analytics, session history, user role, and customer tier to calibrate thresholds and prevent false positives against known trusted users.

Implementing rate limiting and query restrictions. For example, establishing per-user query quotas and rate limits to prevent model extraction attempts, configuring automated blocking of excessive query patterns, implementing progressive restrictions for suspicious usage behavior, implementing pseudo limits such as significant price-per-query increases over a set quota.

Conducting simulated external attack testing. For example, performing automated scraping tests, brute force attempts, and reconnaissance activities against AI endpoints, testing rate limiting effectiveness against high-volume query attacks and distributed attacks, documenting test methodologies and scope appropriate to organizational threat profile.

Maintaining endpoint security through remediation. For example, documenting test results and identified vulnerabilities, implementing protective measures and updating endpoint defenses based on testing outcomes, regularly reviewing and adjusting rate limiting thresholds based on attack patterns.