INDIA Trends and Developments Contributed by: Sandra Lisa Philip, Shri Gayathri and Prashanth Shivadass, Shivadass & Shivadass
extracting key information, summarising documents, drafting and interpreting legal clauses, obtaining a nuanced understanding of language and context, applying business logic, sending automated emails, linking information, etc. While the adoption of AI across key business sectors offers significant potential for advancement, it is also accompanied by a number of drawbacks. High-Risk AI Applications in Business Practices AI-related privacy risks can be understood better when examined through real-world business use cases. Certain AI applications warrant closer scrutiny as they are both widely used by businesses and inher- ently high-risk from a privacy perspective. These risks Generative AI possesses significant privacy risks across business sectors, as it relies on vast datasets that may contain sensitive or personal information. This increases the risk of data leakage or unintended disclosure through generated outputs. In business environments, generative AI can inad - vertently reproduce confidential data, trade secrets or personally identifiable information if the training module is not properly governed. Prompts pertaining to proprietary or customer data given by employees in order to obtain business ideas/logic may continue to be reflected in the chat history of other users, thereby exposing confidential information. Additionally, limited transparency around how generative AI models store, learn from and retain data makes privacy compliance more complex. Facial recognition technology Businesses that adopt facial recognition – ie, because it reduces friction, lowers dependency on passwords and enables rapid identification at scale – face privacy risks with the nature of data itself. Facial data is deeply personal, permanently linked to an individual and can - can be divided between: • technological risks; and • sectoral risks by the use of AI. Technological risks Generative AI
not be replaced if compromised. Once collected, mis - use or leakage can carry long-term consequences. Most privacy issues in facial recognition arise from unclear purpose definition and over-collection of data. Risks are significantly reduced when organisations clearly specify where and why facial data is used, restrict its application to narrowly defined functions and avoid repurposing it across unrelated systems. Meaningful opt-in mechanisms, rather than default or passive consent, are critical. From a technical standpoint, privacy risks can be reduced by converting facial images into encrypted biometric templates, keeping the data only for as long as necessary and avoiding centralised storage. Regu - lar testing for accuracy and bias is equally important. However, most reliance is placed on internal policies. These should clearly define acceptable and prohib - ited users and prevent the gradual expansion of facial recognition into areas where it was never intended. Banks and NBFCs’ reliance on AI, if effected without precautions, can have long-term disastrous conse - quences for customers, since it deals with some of their most sensitive data and even a slight overlook can put them in the way of cyberthreats. AI’s help in accelerating banking decisions and credits is a huge attraction for these institutions as well as their cus - tomers. Most privacy issues in this sector emanate from similar concerns surrounding hoarding and use of data, espe - cially with credit and banking details. Use of these datasets after the purpose of retention, if used to train AI models, can either result in bias or the worse con - sequences of such data falling into the wrong hands. Data minimisation and purpose limitation – the two core principles of data privacy – must be applied here too, to ensure that such risks are nipped in the bud. Healthcare and pharmaceuticals AI tools used in the healthcare sector for processing data/information pertaining to patients rely on high - ly sensitive data and directly affect individuals. As already discussed, the benefits of AI in the healthcare Sectoral risks by the use of AI Lending and banking operations
152 CHAMBERS.COM
Powered by FlippingBook