Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Generative AI is the big trending topic right now, and understandably is featuring prominently in the news. The popularity of platforms such as Open AI’s ChatGPT, which set a record for the fastest-growing user base by reaching 100 million monthly active users just two months after launching is unquestionably on the minds of businesses globally.

While perhaps new to AI researchers, supply chain attacks are nothing new to the world of cybersecurity. For those in the know, it has been best practice to verify the source and authenticity of downloads, package repositories, and containers. But human nature usually wins. As developers, our desire to move quickly to improve ease of use for users and customers can cause us to delay efforts to validate the software supply chain until we are forced to by our peers in compliance or security organizations.

For nearly a decade, Egnyte has been applying AI to help customers protect and manage large volumes of unstructured data. The outputs of these models were historically focused on a relatively narrow set of IT security, privacy, and compliance applications. Today, we’re announcing the next generation of AI-powered solutions at Egnyte, unleashing content intelligence for every user on our platform!

The 2021 Log4Shell incident cast a bright light on open source software security — and especially on supply chain security. The 18 months following the incident brought a greater focus on open source software security than at any time in history. Organizations like the OpenSSF, AlphaOmega, and large technology companies are putting considerable resources towards tooling and education. But is open source software security actually improving? And where are efforts still falling short?

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.

As attackers get more creative in their malicious tradecraft, cloud security teams must be able to keep up with detections that provide adequate coverage against the diverse threats to their cloud environments. Threat emulation enables cloud security teams to leverage their understanding of threat actor behaviors as a feedback loop for developing cloud-based detections and validating their resilience.

Managed Service Providers (MSPs) play a crucial role in delivering reliable and secure IT services to organizations of all sizes and across various industries. With the rise in cyber threats, especially ransomware attacks targeting public sector organizations, the need for robust cybersecurity tools has become paramount.

The rapid and widespread adoption of artificial intelligence (AI) has ushered in a new era of technological advancement, revolutionizing various industries and becoming immensely popular worldwide. AI-driven applications and solutions have streamlined processes, improved efficiency, and enhanced the overall user experience. However, this surge in AI’s popularity also comes with a dark side.