The growing prevalence of artificial intelligence (AI) across industries has revolutionized how organizations process, analyze, and leverage data. This increased use of AI also introduces significant risks to the protection of personally identifiable information (PII) regarding vulnerable sectors such as healthcare, fintech, and retail. This presentation examines the critical threats posed by AI to PII data in these industries and explores practical strategies to mitigate such risks.

Healthcare data is particularly vulnerable due to the highly sensitive nature of medical records, patient histories, and genetic information. In fintech, financial transactions and customer data are attractive targets for cybercriminals exploiting AI-driven predictive modeling. Similarly, retail organizations face challenges in protecting customer data, including purchase histories and payment information.

This session will delve into how AI models can unintentionally expose PII through data leaks, re-identification risks, and adversarial attacks. Additionally, the presentation will cover the implications of AI-driven automated decision-making and data model poisoning that can amplify existing biases or lead to incorrect and even discriminatory outcomes.

To address these challenges, we will outline a multifaceted approach, encompassing secure data handling practices, advanced data extract through cloning techniques, and differential privacy. We will discuss implementing privacy-preserving AI models and frameworks that minimize the exposure of PII. Regulatory compliance measures, such as GDPR and CCPA, will be highlighted to ensure legal safeguards are integrated into AI data processing.

By the end of this presentation, participants will have a comprehensive understanding of the risks AI poses to PII in healthcare, fintech, and retail, as well as a set of actionable recommendations to enhance data security and privacy, fostering responsible AI usage in these critical sectors.