Preserving an individual privacy in the Artificial Intelligence(AI)era is a pressing challenge, as traditional anonymization techniques often fall short againstAI-driven threats. WhileAIhas enabled transformative advancements across sectors such as healthcare, finance, aviation, smart cities, and the Internet of Things (IoT), it also introduces new privacy challenges. In particular,AI-driven background knowledge attacks occur when adversaries combine auxiliary information from external sources such as public records, social media, or previously leaked datasets with advancedAItechniques to re-identify individuals or infer sensitive details from anonymized datasets, rendering conventional defenses less effective. In this study, we presentPrivacyGuard, an automated framework that integrates machine learning with traditional anonymization to address these risks.PrivacyGuardemploys K-Nearest Neighbor and Random Forest classifiers to identify and evaluate privacy risk of each data tuple inAI-driven attack scenarios. Following risk assessment, Laplace-based differential privacy is applied to mitigate identified risks, while fuzzy logic forms equivalence groups that reflect varying risk levels. Experimental validation on the 1:M Adult dataset (an individual having multiple records) demonstrates thatPrivacyGuardachieves 96.47% accuracy and mitigates approximately 42.21% of the identified risks, confirming its effectiveness in balancing privacy and data utility against emergingAI-driven threats.