Widi Aribowo
The rapid advancement of autonomous AI systems has introduced unprecedented capabilities in decision-making, data analysis, and operational efficiency. However, these systems also pose significant privacy challenges, particularly when handling sensitive personal or organizational data. This chapter explores the principles, methods, and frameworks for designing privacy-preserving autonomous AI systems, emphasizing techniques that balance functionality, security, and compliance. Key approaches such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation are analyzed in the context of autonomous agents. The chapter also discusses regulatory and ethical considerations, highlighting the need for transparency, accountability, and adaptive privacy mechanisms. By integrating privacy-preserving strategies into autonomous AI systems, organizations can mitigate risks while harnessing the transformative potential of intelligent agents. ©2026, IGI Global Scientific Publishing. All rights reserved.
Universitas Negeri Surabaya, Indonesia
