Safeguarding Personal Information Privacy in AI-Driven Data Engineering: Challenges and Protection Strategies

Abstract

The interation of data engineering processes with artificial intelligence has essentially transformed the handling of personal information by organizations, posing an enormous privacy risk in real-time data processing and automated workflow configurations. Ordinary security cannot be used to address the special issues that systems based on AI that continuously process streaming data and are distributed across architectures present. In the case of machine learning models operating on high-velocity data streams, they introduce points of exposure to the data lifecycle, specifically during ingestion and model inference. This implies that privacy protection strategies should be algorithmic and operate with strict time and computational constraints. Privacy-preserving technologies, such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation, among others, can be used to safeguard sensitive data and enable AI applications to work with the required data simultaneously. Privacy-by-design principles and privacy engineering methodology are organizational frameworks that provide systematic means of integrating privacy protections into every phase of system development. Legal requirements, including technical controls and governance processes, are outlined by the GDPR, CCPA, and new AI-specific rules to ensure data engineers comply with them. Privacy technologies do not only fulfill the legal requirements, but their impact on the economy and society is far more drastic. They radically alter the competitive landscape of the business, the level of trust that customers have in one another, and the operation of digital rights in societies that are becoming more data-driven. The decisions that organizations make about privacy protection must be balanced against their sustainability commitments, since privacy systems with high consumption of computing resources do have environmental impacts. Active privacy engineering strategies assist companies in achieving their innovation as well as privacy objectives simultaneously. This will provide them with a competitive advantage through greater trust of stakeholders and regulatory strength, as well as promote the ethical application of AI in accordance with democratic principles and individual autonomy.