Cloud-based Platform-agnostic Adversarial AI Defence framework

The exponential growth of Artificial Intelligence (AI) technologies has significantly enhanced various domains, from smart cities to medical devices. However, this rapid advancement has also broadened the attack surface, leading to increased exposure to adversarial threats such as poisoning, evasion, inference, and extraction attacks. To address these challenges, we propose cPAID, a Cloud-based Platform-Agnostic Adversarial AI Defense framework designed to fortify AI systems against a wide range of adversarial attacks. The proposed framework integrates multiple defensive mechanisms, including Generative Adversarial AI, AI-assisted Intrusion Detection and Prevention Systems (AIPS), Risk Management for AI (RIMA), Data Fabric, Meta-SIEM (mSIEM), and Adversarial AI Cyber Range. Furthermore, the cPAID platform employs the MLPrivSecOps methodology, embedding security-, privacy-, and trust-by-design principles throughout the AI lifecycle. This paper presents the architecture, methodologies, and components of cPAID, emphasizing its scalability, robustness, and compliance with ethical AI principles.