Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI has had dramatic impacts on almost every facet of every industry. API security is no exception. Up until recently, defending APIs meant guarding against well-understood threats. But as AI proliferates, automated adversaries, AI-crafted exploits, and business logic abuse have complicated matters. It’s no longer enough to merely patch known flaws; security teams must now contend with intelligent, adaptive attacks that evolve as fast as the technology they target.

Many tech professionals see integrating large language models (LLMs) as a simple process -just connect an API and let it run. At Wallarm, our experience has proved otherwise. Through rigorous testing and iteration, our engineering team uncovered several critical insights about deploying LLMs securely and effectively. This blog shares our journey of integrating cutting-edge AI into a security product.

Nestled in a log cabin high in the Rocky Mountains, Rick Bohm starts his day the same way he’s approached his career: intentionally, with a quiet commitment to learning and action. Boasting more than three decades of cybersecurity experience, Rick has watched tech evolve from dial-up ISPs to advanced AI-driven security architectures – and through it all, he’s focused on one enduring mission: protecting data, organizations, and people.

According to the Wallarm Q1 2025 ThreatStats report, 70% of all application attacks target APIs. The industry can no longer treat API security as a sidenote; it’s time to treat it as the main event. NIST seems to be on board with this view, releasing the initial public draft of NIST SP 800-228, a set of recommendations for securing APIs.