The Role of De-identification in AI-Powered Zero Trust Architectures for Data Privacy Compliance

The fast adoption of the artificial intelligence (AI) in the enterprise setting has been the main factor that has changed the way companies handle, process, and protect sensitive information. However, the new acceleration has brought new risks that are related to privacy, compliance, and cybersecurity. The established perimeter-based security models have become less effective to mitigate the advanced cyber threats and insider risks, therefore, leading to the rise of Zero Trust Architectures (ZTA) as a security paradigm. Meanwhile, strict regulatory policies like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA) emphasize de-identification as a key tool of safety of sensitive data. Anonymization, pseudonymization, and differential privacy are collectively referred to as de-identification, which is a crucial element in supporting secure data processing without affecting analytical utility. In this paper, the author analyzes how de-identification can be used in AI-based Zero Trust systems as a tool to reach the compliance with international data privacy laws. Based on a review of retrieved literature and industry publications, as well as regulatory standards, the paper presents a conceptual framework of incorporating de-identification methods into ZTA settings to reduce risks of data leakage, adversarial attacks, and non-observance. The results show that de-identification does not just enhance the compliance but also enhances AI-based monitoring and detection functions in Zero Trust ecosystems. This work provides a new viewpoint in developing resilient, compliance-oriented, and ethically based data security architectures by merging the privacy engineering with AI-enabled ZTA