Why API Security-by-Design outweighs API Behavior Monitoring Why using Machine Learning Alone Falls Short for API Security and Why 'Secure by Design' is Essential

API security is complex and presents real challenges for API behavior monitoring and ML-based anomaly detection tools. Vendors such as Traceable.ai, Salt Security, Akamai (Noname), and Cequence are responding with anomaly detection technologies that promise to identify abnormal behavior in API traffic.

While these tools can play a role in identifying long-term patterns, fraud, and advanced persistent threats, they are reactive by nature and that’s a fatal flaw in the API space. Behavioral analytics systems are notoriously difficult to install and maintain, require time to establish a baseline of “normal” behavior, time to retrain after API changes, and often suffer from generating false positives and blind spots due the dynamic nature of APIs.

Most API attacks aren’t slow or predictable, they’re fast, targeted, and exploit flaws tied to specific API logic or data structures, as outlined in the OWASP API Top 10. In fast-paced, largescale API environments, relying on traffic patterns alone adds noise, delays response, and undermines confidence in automated defenses.

In this paper we advance why a “Secure-by-Design” approach, widely endorsed by global cybersecurity agencies, is proving to be the essential best practice approach to securing APIs.

Learn how to:

• Establish an accurate baseline of secure API behavior based on design intent
• Prevent vulnerabilities before deployment and reduce risk at scale
• Deliver a more complete and accurate real-time protection for APIs
• Scale and adapt to changes with the speed of DevOps