This article was written by Deb Bond, Consulting Manager.
Data Privacy can be best defined as the protection of personal data from those who should not have access to it and the ability to individuals to determine who can access their personal information.
AI’s Impact on Informational Privacy
The use of Artificial Intelligence (AI) has become widespread and almost invisible in our daily lives. One concern in AI is ‘informational privacy’ — the protection of personal data collected, processed, and stored by these systems. The granular, continuous, and ubiquitous data collection and analysis by AI can potentially lead to the exposure of sensitive information.
AI in Financial Services
AI is used for tasks such as spotting potentially suspicious financial activities, reducing the risk of financial theft or loss. Credit bureaus use AI to better calculate creditworthiness through the use of traditional and non-traditional data. Tasks which were at one time required to be performed in person (such as opening an account or obtaining information on services) are automated and completed very quickly.
AI in Cybersecurity
AI is used in many cybersecurity network protection and workstation protection utilities to help organizations spot potentially malicious activity and thwart attackers before they can cause damage. The use of these utilities for real-time protection and analysis enables businesses to protect sensitive data and maintain a state of high vigilance to prevent the loss of theft of the sensitive data they have been entrusted with.
Generative AI and Privacy Concerns
Generative AI can affect data privacy, especially when its models’ training involves massive datasets that typically contain Personally Identifiable Information (PII). Integrating AI into operational processes can unintentionally cause operational failures which may lead to data corruption or loss. Errors may include AI decision making errors, bias or discrimination in AI algorithms, failure of AI systems during unanticipated conditions, and challenges which may arise during an organization’s attempts to integrate AI into legacy systems.
The Risk of Inadequate Data Privacy Measures
Inadequate data privacy measures and information systems management can expose sensitive information, resulting in serious privacy concerns. For example, organizations in the financial services and healthcare sectors collect vast amounts of sensitive personal and financial data. The use of AI or other similar technologies in processing and analyzing this data introduces significant privacy concerns, particularly if this data is mishandled, misused, inadequately protected, or stolen.
Cybersecurity Threats from AI
AI systems introduce new vulnerabilities and can be used to conduct cyber-attacks such as AI-driven phishing, malware attacks, and social engineering. These sophisticated cyber-attacks are more difficult to detect and defend against. Implement robust security measures to protect your AI systems, as they are vulnerable to cybersecurity attacks and data breaches, just like any other system.
Privacy-by-Design in AI Development
As you develop AI systems or integrate AI into your systems, adopt a privacy-by-design approach and make sure this is documented in software development policies and other general security policies which govern development and integration. This ensures that data privacy and data security concerns are a consideration from the beginning. Adhere to best practices such as data anonymization, encryption, and secure storage.
Trustworthy AI and Accountability
As more organizations work to adopt and implement the use of AI within their systems and processes, it is important to ensure that the use of trustworthy AI is incorporated into policies, procedures, and processes and that accountability is fully documented. Documented policies, procedures and standards which include the use of AI are central to effective AI risk management. Without effective AI risk management, the result could be unintentional harm to the organization, presenting additional risk and potential regulatory or legal action.
GAO-21-519SP: AI Accountability Framework for Federal Agencies &Other Entities. https://www.gao.gov/products/gao-21-519sp