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Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B. and Swami, A. (2017). Practical Black-Box Attacks against Machine Learning. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, Abu Dhabi, 2-6 April 2017, 506-519.
https://doi.org/10.1145/3052973.3053009

has been cited by the following article:

• TITLE: Bridging the Gap: Improving Agentic AI with Strong and Safe Data Practices

  AUTHORS: Anil Kumar Soni, Ravinder Kumar

  KEYWORDS: Agentic AI, Data Integrity, Secure Data Pipelines, Anomaly Detection, AI Robustness, Explainable AI, Autonomous Data Integrity Layer (ADIL)

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.17 No.4, November 5, 2025

  ABSTRACT: Agentic AI represents a significant advancement in artificial intelligence, enabling proactive agents that can set goals, make decisions, and adapt to changing situations. However, the performance of these systems is heavily dependent on the quality and relevance of the data they process. This research highlights the critical risk posed by faulty, insecure, or contextually inappropriate input data in modern Agentic AI systems. To address this challenge, this study proposes the Autonomous Data Integrity Layer (ADIL). This flexible architecture integrates best practices from security engineering and data science to ensure that Agentic AI systems operate with clean, validated, and contextually relevant data. By focusing on data integrity, ADIL enhances the reliability, accountability, and effectiveness of Agentic AI systems, leading to more trustworthy and robust intelligent agents.