CloudDefense.AI Blog

In today’s high-stakes healthcare sector, where healthcare providers leverage various digital solutions, health security has become paramount. Healthcare cybersecurity is always under pressure from data breaches or zero-day exploits, as it holds millions of patients’ data. However, the most severe issue that has plagued the industry

Traditionally, security has always been an obstacle in the development process for engineering teams. The teams are bombarded with an overwhelming number of false positives from different security sources.  It not only leads to high alert fatigue but also causes developers to resort to context switching.

For years, most organizations have been raising one major issue when it comes to scanning in AppSec: false positives. It is a bane to modern AppSec programs that has numerous negative consequences to the development process.  In today’s high-paced software development process, where velocity and accuracy

Modern cybersecurity is evolving rapidly, fueled by AI-driven code generation and an advanced DevOps approach. While AI-based AppSec tools have been helping organizations to bolster their guardrails, it is failing to cope with the high volume of code changes and increasing complexity.  However, the advent of

In today’s modern software development, where speed and security go hand-in-hand, application security (AppSec) is no longer just a feature. AppSec has become a necessary foundation of every organization’s security strategy.  As developers write code, especially through AI-code editors, it creates a possibility of introducing vulnerabilities

The multicloud strategy has become completely mainstream. A recent Cloud Security Alliance report highlighted that around 57% of organizations in the industry are now using a multicloud environment to run their operations. Every CISO and CTO has embraced this model to avoid traditional vendor lock-in and

With the need for speed in the application development process, developers are rapidly turning to AI-powered IDEs like Cursor. It has not only enhanced developer productivity but also enabled organizations to achieve faster release cycles. However, implementing AI-generated code into the codebase introduces new security risks

With applications being the backbone of modern enterprises and a constant threat from attackers, complete security of every application has become a necessity. Moreover, development cycles are accelerating with time, and the addition of dependencies and API is making applications more complex.  Although standard application security

Modern software development commands speed and agility, fueled by AI-assisted coding and an advanced CI/CD pipeline. However, to cater to high-speed application development approaches, organizations need a shift in their application security approach.  AI SAST, or AI-based Static Application Security Testing, serves as a proactive and

Application security in today’s world is constantly under threat from cyber attackers exploiting every possible vulnerability in the application source code. Organizations require advanced and AI security testing methodologies within the SDLC to identify and mitigate vulnerabilities, including zero-day attacks.  Among all, AI SAST and AI