Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

The rollercoaster ride of Elon Musk’s Twitter buyout has taken another twist, as the social media platform looks poised to be rebranded “X”. Should we be worried about the security and privacy implications of Musk’s “everything” app?

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.

In our increasingly digital world, global communications and financial interactions are nigh inescapable for anyone in any industry or walk of life. The infrastructure in place for international transactions is complex and layered, containing moving parts that work in tandem to make things like transferring money nearly seamless.

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.