Generative AI, the transformative technology causing a stir in the global tech sphere, is akin to an enthralling narrative with its charming allure and consequential dark underbelly. Its most notable impact is forecasted in the realm of identity proofing, creating ripples of change that demand our immediate attention.

In the modern, cloud-first era, traditional data protection technology approaches struggle to keep up. Data is rapidly growing in volume, variety, and velocity. It is becoming more and more unstructured, and therefore, harder to detect, and consequently, to protect.

This is the third part of a blog series on AI-powered application security. Following the first two parts that presented concerns associated with AI technology, this part covers suggested approaches to cope with AI concerns and challenges. In my previous blog posts, I presented major implications of AI use on application security, and examined why a new approach to application security may be required to cope with these challenges.

For anyone who follows industry trends and related news I am certain you have been absolutely inundated by the torrent of articles and headlines about ChatGPT, Google’s Bard, and AI in general. Let me apologize up front for adding yet another article to the pile. I promise this one is worth a read, especially for anyone looking for ways to safely, securely, and ethically begin introducing AI to their business.

A few years back, data was constrained to the on-premise infrastructure. Data management, governance, and protection were fairly uncomplicated in this enclosed environment. The emergence of cloud computing and multi-cloud infrastructures has not only introduced more complexity in data management and governance, but it has also increased security risks significantly.