Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

SAST (Static Application Security Testing) tools analyze an application’s source code to identify potential security vulnerabilities without executing the code. They are crucial for finding security flaws early in the development lifecycle, helping developers address issues before they become more costly and difficult to fix. Unlike dynamic analysis techniques, SAST operates without executing the program, focusing entirely on the static codebase.

Today, we’re introducing our Risk Reduction Dashboard. This is a new way for security leaders to quantify their AppSec program’s impact, prioritize high-value fixes, and prove ROI with data-backed insights that go beyond raw vulnerability counts.

“72% of organizations use AI in business functions — but only 13% feel ready to secure it.” That gap, between adoption and preparedness, explains why traditional AppSec approaches aren’t enough.

Over this last year, we’ve seen significant attacks like the Shai-Hulud worm, the Nx build system compromise, and secrets being leaked to public GitHub Actions logs via the tj-actions/changed-files compromise, but I could spend the entirety of this article only listing different attacks, let alone talking about them.

Application Security Testing (AST) services use automated tools and manual techniques to find and fix security vulnerabilities in software, integrating security into the entire development lifecycle (SDLC) to prevent threats and protect applications from attacks. Key services include Static Application Security Testing (SAST) for code-level analysis, Dynamic Application Security Testing (DAST) for runtime testing, and Interactive Application Security Testing (IAST) which combines both.

Unlike closed-source code or proprietary applications, open source software (OSS) exposes its source code, allowing anyone to view, modify, or contribute to it. This transparency delivers both opportunities and unique threats; developer communities can uncover flaws faster, but attackers can also examine code for weaknesses and even easily leverage known reported open source vulnerabilities.

Today, Mend.io is expanding its AppSec capabilities to secure the five most popular agentic IDEs — including Windsurf, CoPilot, Claude Code, Amazon Q Developer, and Cursor — ensuring that developers can move at AI speed without compromising security.

Containers have transformed how modern applications are built and deployed. They’re lightweight, portable, and allow teams to move software from development to production faster than ever before. But as adoption has accelerated, so have security concerns. From vulnerable base images to exposed Kubernetes clusters, container security has become a top priority for AppSec and DevSecOps professionals.

Code scanning is the process of automatically analyzing source code to identify potential security vulnerabilities, bugs, and other code quality issues. It’s a crucial part of secure application development, helping teams detect and fix problems early in the software development lifecycle. Code scanning tools mainly use static analysis methods (examining code without running it), in contrast to dynamic analysis tools which analyze applications while they are running.

The software security landscape is evolving faster than ever, and AI is accelerating this change. As generative and embedded AI become core to how software is developed, tested, and deployed, security must adapt to protect an entirely new layer of risk. At Mend.io, we’ve spent the past year reimagining what Application Security Testing (AST) looks like in this new reality.