Secure Design Principles for AI-Driven Learning Platforms

Abstract

Artificial intelligence is increasingly integrated into educational tools such as learning management systems, analytics dashboards, adaptive assessment services, and generative AI assistants. These platforms support personalized learning and institutional decision-making, but they create risks linked to digital identity, learner profiling, data misuse, model leakage, prompt injection, and weak governance. This chapter analyzes cybersecurity governance in AI-driven learning platforms from the perspective of identity security and data privacy. It examines AI-specific privacy threats and safeguards such as zero trust, decentralized identifiers, verifiable credentials, encryption, federated learning, differential privacy, secure aggregation, and accountable oversight. The chapter proposes a layered cybersecurity framework integrating identity assurance, privacy-preserving processing, AI model protection, secure platform operations, institutional governance, and continuous risk monitoring. The contribution supports trustworthy, privacy-aware, and secure AI-enabled education ecosystems.