Growing data privacy regulations and data breaches are increasing the cost of privacy compliance, protection monitoring, and management. Advances in data-centric services have fueled the demand for better data privacy. As sentience and intelligence are increasingly embedded almost everywhere, enterprise and consumer advocacy groups have been asking for clearer rules to protect personal data and individual privacy. Firms are increasingly measured on how well they enable users to take back control of their data, with efficient systems building data empowerment and protection into the technology architecture itself. Hence, advances in confidential computing are important. This paradigm guarantees users that their data is only being used for the purpose intended and is not open to phishing or malware.
A health-care company partnered with Infosys to build a comprehensive framework for building and managing a health-care privacy program to comply with the Health Insurance Portability and Accountability Act through the Infosys Data privacy Suite.
Cloud transformation and modernization offer significant opportunities for privacy-first app development. Organizations are looking to deliver high-quality applications at minimum cost. They need a test data management (TDM) strategy that supports waterfall and agile delivery models. With the rapid adoption of DevOps and increased focus on automation, the need for data privacy has grown immensely. These transformations are providing opportunities for early adoption of concepts such as responsible AI and developing unbiased data sets for AI models.
A U.S.-based retailer wanted to build and roll out a predictive algorithm to forecast demand from customers in specific geographic locations. The company did not have the accurate data to test and optimize the predictive analytics engine due to a lack of user consent. In partnership with Infosys, the client used data augmentation to build data sets, which were completely anonymized with high data utility. Following this, the client performed a detailed analysis of reliability, validity, and privacy consequences, effectively scaling its data-centric services and sharing data with partners.