Securing AI-Driven APIs: Authentication and Abuse Prevention
Pavan Paidy, Krishna Chiatanya Chaganti · International Journal of Emerging Research in Engineering and Technology · 2024
AI-driven APIs are quickly taking front stage in many industries, including healthcare, banking, retail & also entertainment, as AI becomes more & more important in modern applications. Often driving NLP, recommendation systems & also decision-making applications, these intelligent endpoints provide great value even if they also raise the latest set of security concerns. Unlike traditional APIs, AI-driven interfaces might show greater opacity, dynamism & abuse sensitivity, which would attract targets for attackers looking to take advantage of weaknesses, change model behavior or gather more critical information. Emphasizing the requirement of strong authentication & more comprehensive abuse prevention techniques, this paper investigates the evolving security environment related with AI-based APIs. To guard against unlawful access & exploitation, we investigate fundamental methods like rate limiting, behavioral analytics, token-based authentication & also anomaly detection. Moreover, we underline the growing demand of AI-aware security systems that fit the complexity of ML models and their application strategies. The paper uses an actual world case study of a production-level artificial intelligence API that intentionally underwent abuse to effectively contextualize these ideas. The exact assault paths, the put in place mitigating strategies, and the long-term effects are investigated in this instance. This paper aims to provide developers, architects, and security professionals useful concepts to improve the security of AI-driven APIs within a more intelligent digital world