MASP: A Modular AI Security Protocol Framework for Ensuring Robustness, Privacy, and Compliance in AI Systems
Harshith Madhavaram · 2025
As artificial intelligence systems proliferate across sensitive and mission-critical domains, their susceptibility to cyber threats demands a unified, lifecycle-aware security strategy. Current approaches often address isolated stages or attack surfaces, lacking modularity and adaptability across diverse AI models and use cases. This paper proposes MASP, a Modular AI Security Protocol Framework designed to secure AI systems comprehensively across training, inference, and deployment stages. MASP is composed of five independently configurable modules: Data Security, Model Integrity and Robustness, Inference Privacy, API and Deployment Security, and Explainability and Trust. Each module incorporates advanced techniques such as differential privacy, backdoor detection, federated learning, runtime sandboxing, and audit logging. The framework introduces a Security Maturity Score to evaluate an AI system's theoretical robustness and compliance level. MASP is designed to integrate seamlessly with existing AI pipelines and aligns with international standards such as NIST AI RMF, ISO/IEC 23894, and IEEE 7000, ensuring applicability in regulated environments. While the framework is currently proposed as a theoretical construct, it offers a foundational blueprint for building adaptive, end-to-end AI security systems capable of mitigating evolving adversarial threats. This work aims to guide future research and development toward creating secure, transparent, and trustworthy AI deployments.