Artificial intelligence (AI) refers to systems designed to imitate human intelligence, thus allowing them to learn, tackle their challenges, make decisions, and improve over time. Whether as virtual assistants or generative AI tools, these technologies are becoming increasingly indispensable to modern life. Despite so, with the expansion of AI’s reach come concerns about privacy and security. The capacity of AI to handle and analyze vast quantities of data raises crucial questions about the ways personal information is employed, saved, and safeguarded within these systems.

More Data, More Privacy Risks

AI systems thrive on vast amounts of data, and this dependency makes more complex long-standing privacy concerns, especially around the collection and processing of personal information. While issues around transparency, security, and unauthorized data collection are not new, the scale and impact of AI’s capabilities, enabled by vast datasets and powerful computational resources, present more significant challenges.

Deleting Personal Data

Under some certain rules, organizations using AI models must guarantee compliance with the right to erasure when individuals ask for the deletion of their personal data, thus resulting in unique challenges for AI models, particularly large language models (LLMs), where personal information has been used for training or has been embedded into sophisticated datasets. Once  incorporated into an AI model, data becomes deeply embedded, making complete deletion nearly impossible. Retraining models with updated datasets can reduce the influence of older data, but achieving full compliance with deletion requests remains a major concern. It is organizations with robust data governance practices that can reduce this risk through carrying out structured processes to govern where and how data will be used. These systems help organizations locate, manage, and securely delete personal data while reducing to minimum the impact to AI model integrity.

Transparency in Decision-Making

AI tools are increasingly used to profile and make automated decisions about people, such as for customer identity verification or recruitment purposes. Several privacy laws around the world require transparency around such processes, while the rule provides one of the most comprehensive regulations for this type of decision-making. Under another rule, individuals have the right to opt out of decisions made solely through automated processing, specifically when such decisions could result in legal consequences or significantly affect their rights and freedoms. Under this rule, data controllers are obligated to inform individuals about automated decision-making, clarify the underlying logic of these systems, and describe the potential outcomes. In spite of so, providing transparency becomes challenging when working with machine learning algorithms, as their decision-making may be too complex to explain in simple terms or self-evolve over time. It is noteworthy that individuals’ right to opt out from automated decision-making does not apply in all cases though. For example, if the decision-making is necessary for entering into or performing a contract between the individual and the data controller or if the individual provides their explicit consent.

Repurposing of Personal Data

AI raises concerns about the repurposing of personal data. This occurs when data collected for one purpose is later used for an entirely different, often unforeseen, purpose. Such misuse of data can lead to violations of data protection laws. Despite the fact that regulations provide some protection, they may not fully account for the complexities of AI systems. Organizations must ensure that they have a lawful basis for processing personal data, and that the data is used in alignment with its original purpose or with the informed consent of individuals.

AI Security and Data Storage Risks

AI systems face unique security challenges, including vulnerabilities to cyberattacks, model manipulation, and data breaches, which can compromise personal data. Securing AI systems is critical for safeguarding individuals’ privacy. Beyond traditional IT security measures, organizations must adopt AI-specific safeguards, such as securing training datasets, monitoring model integrity, and implementing access controls tailored to AI systems. Regularly updating systems to address emerging risks, conducting thorough security audits, and incorporating encryption and anonymization techniques are also essential to mitigate risk. It is through integrating these measures together with established IT security practices that organizations can better protect personal information and guarantee the resilience of their AI systems.

Preparing Your Organization for the Future

With AI going on transforming industries, organizations must balance innovation with privacy protection. The vast datasets required by AI systems challenge individuals’ ability to understand or control the use of their personal information. Staying informed about regulatory developments, ensuring governance and accountability, and addressing potential abuses are essential. The risk of bias, as well as the ability to generate deceptive or manipulative content, also underscores the need for ethical implementation of these systems.