Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

What Are Application Security Testing Tools? Application security testing (AST) tools identify vulnerabilities and weaknesses in software applications. These tools assess code, application behavior, or its environment to detect potential security risks. They help developers and security teams prevent cyberattacks by addressing security issues during the development and deployment phases.

Best SAST Solutions: How to Choose Between the Top 11 Tools in 2025 Static Application Security Testing (SAST) is a proactive approach to identifying security vulnerabilities in source code during development. This article delves into the core features of SAST tools, reviews leading solutions, and provides guidance on selecting the right tool to enhance your software’s security posture.

AI is reshaping software development quickly. From boilerplate generation to test automation and refactoring, LLMs like the one behind Cursor are transforming how developers build. But with great power comes a new generation of vulnerabilities. At Mend.io, we’re excited to announce a native integration with Cursor, the IDE taking the dev world by storm.

AI security is where traditional cybersecurity meets the chaotic brilliance of machine learning. It’s the discipline focused on protecting AI systems—not just the code, but the training data, model logic, and output—from manipulation, theft, and misuse. Because these systems learn from data, not just logic, they open up fresh attack surfaces like data poisoning, model inversion, and prompt injection.

Shadow AI refers to the unauthorized or unmanaged use of AI tools, models, frameworks, APIs or platforms within an organization, operating outside established governance frameworks. While employees may adopt these AI tools with good intentions, seeking to enhance productivity or solve problems more efficiently, the lack of oversight creates significant security, compliance, and operational risks.

Kubernetes security refers to practices, tools, and configurations that protect Kubernetes clusters and workloads from unauthorized access, vulnerabilities, and runtime threats. It involves securing all components of the Kubernetes environment—including the control plane, worker nodes, pods, container images, networking, and storage.

MCP adoption is surging across industries, fundamentally reshaping how systems connect to AI models. By establishing a universal protocol for data exchange, MCP simplifies integration complexity, empowering developers to build sophisticated AI capabilities in a fraction of the traditional development time. However, this streamlined connectivity to AI tools introduces significant security risks.

AI red teaming is the process of simulating adversarial behavior to test the safety, security, and robustness of artificial intelligence systems. It draws inspiration from traditional cybersecurity red teaming (where ethical hackers emulate real attackers to expose flaws) but applies that mindset to machine learning models, data pipelines, and the broader AI stack.

The OWASP Top 10 is a security research project that outlines the ten most critical security risks to web applications. Published by the Open Worldwide Application Security Project (OWASP), it serves as a widely recognized benchmark for web application security. The list is compiled from data gathered by security experts and organizations worldwide, based on the prevalence, detectability, and impact of various vulnerabilities.

Application security testing (AST) is the process of identifying and fixing security vulnerabilities in software applications. It ensures that applications are protected against threats such as unauthorized access, data breaches, and code manipulation. The application layer continues to be the most attacked and hardest to defend in the enterprise software stack.