Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Large language model (LLM) security refers to the strategies and practices that protect the confidentiality, integrity, and availability of AI systems that use large language models. These models, such as OpenAI’s GPT series, are trained on vast datasets and can generate, translate, summarize, and analyze text. However, like any complex software component, LLMs present unique attack surfaces because they can be influenced by the data they process and the prompts they receive from users.

SAST (Static Application Security Testing) tools are crucial for DevSecOps, enabling automated code analysis to identify vulnerabilities early in the development lifecycle. They analyze source code without execution, detecting issues like SQL injection, XSS, and buffer overflows. Popular SAST tools used by DevSecOps teams include Mend, Checkmarx, Snyk, Veracode, BlackDuck, SonarQube, and Semgrep. Integrating SAST into CI/CD pipelines ensures continuous security checks as code is developed.

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the development lifecycle and deliver high-quality software continuously. It emphasizes collaboration, automation, and integration between previously siloed teams. DevOps aims to improve deployment frequency, reduce failure rates, and accelerate recovery times.

AI code review leverages artificial intelligence models and machine learning techniques to analyze and provide feedback on source code, automating and improving the traditional code review process. It is crucial for software development workflows, offering significant advantages to developers and teams. AI code review can scan for bugs, style violations, security vulnerabilities, and other issues.

Most dashboards are like a busy beach with one lifeguard watching the entire shoreline. They keep an eye on everything, but the sheer scope means that critical issues—like risks in AI applications—can get lost in the crowd. Mend.io’s AI Security Dashboard changes that. It’s like a lifeguard tower posted directly at the AI section of the beach, keeping a sharp, dedicated watch on AI specific risks that other tools overlook.