AI generated code security
Secure AI generated code without slowing developers down.
Challenges
Securing AI generated code is not just a tooling problem
It’s a new paradigm. Traditional tools weren’t built for code written by machines, and it’s a growing blind spot.
Unfamiliar coding patterns
AI generated code doesn’t follow human logic, so standard SAST tools often miss subtle but critical security flaws.
Slow legacy scanners
Traditional scanners can’t operate at the speed of AI code generation, break developer flows, and can’t integrate seamlessly with modern coding assistants.
Minimal human review
AI generated code often skips peer review and lacks secure coding practices. Many developers paste it into production without fully understanding it, creating subtle but serious vulnerabilities.
Opportunities
Secure code from the start, without disruption
Stop security risk early by integrating AppSec directly into AI coding assistant development workflows.
Scan at the point of generation
Catch flaws the moment code is suggested, using SAST and SCA engines tuned for AI generated code.
Automate fixes powered by AI
Loop findings back to the AI coding engine to regenerate secure alternatives automatically, before flawed code ever hits a commit.
Apply two-phase scanning
Run rapid, AI code tuned scans at the point of generation, followed by deeper SAST/SCA checks in CI pipelines to reduce risk while boosting productivity.
The solution
Mend AppSec
A purpose-built platform to secure AI generated code—built for today’s speed, scale, automation, and development demands.
Discover Mend AppSec
“One of our most indicative KPIs is the amount of time for us to remediate vulnerabilities and also the amount of time developers spend fixing vulnerabilities in our code base, which has reduced significantly. We’re talking about at least 80% reduction in time.”
“When the product you sell is an application you develop, your teams need to be fast, secure and compliant. These three factors often work in opposite directions. Mend provides the opportunity to align these often competing factors, providing Vonage with an advantage in a very competitive marketplace.”
“The biggest value we get out of Mend is the fast feedback loop, which enables our developers to respond rapidly to any vulnerability or license issues. When a vulnerability or a license is disregarded or blocked, and there is a policy violation, they get the feedback directly.”
FAQs
How does Mend.io secure AI-generated code?
AI coding assistants can use Mend Agentic Integration tools to immediately scan generated code for CWEs and dependencies for known CVEs. The assistant can use Mend.io findings to fix and rescan the code before it is committed, while repository, pull-request, and CI/CD scans provide broader SAST and SCA coverage later in the development workflow.
How does Mend.io scan code at the point of generation?
MCP servers integrated with AI coding assistants let Mend.io trigger SAST and SCA scans as the assistant produces code, catching vulnerabilities and risky dependencies in real time rather than weeks later in review.
What is Mend.io’s two-phase scanning approach?
Phase one: rapid AI-code-tuned scans at generation. Phase two: deeper SAST/SCA checks in CI/CD pipelines. This provides speed where developers work and depth where builds ship.
Does Mend.io’s scanning slow developers down?
No. Scans run in real time at the point of generation, and fixes are regenerated automatically, so developers keep their pace while security keeps coverage.
Which AI coding assistants can I connect to Mend.io’s Agentic Integration tools?
Cursor, GitHub Copilot in VS Code, Windsurf, Claude Code, Antigravity, Amazon Q (Kiro), and Google Gemini (CLI and Code Assist) all have dedicated Mend.io integrations today. Each one connects through an MCP server that Mend.io configures automatically during setup.
Do I need to set up my own MCP server or install anything to secure AI-generated code with Mend.io?
No. Mend.io’s Agentic Integration tools run through an MCP server it configures for you. Setup happens once through your coding assistant’s integrations catalog with your Mend.io account credentials, and your assistant’s config file (Cursor’s mcp.json, for example) is pointed at your Mend.io environment automatically.
What’s the difference between Mend.io’s code scanner and its dependency scanner inside the IDE?
They check different things. Mend SAST scans code an assistant just wrote for CWEs and re-checks it, up to three attempts, as fixes get applied. Mend SCA checks any library the assistant wants to add against known CVEs and asks for your approval before it lands, so a vulnerable package gets flagged the moment an assistant suggests it.
What happens if Mend.io can’t find a safe fix for a vulnerability an AI assistant introduced?
It tells you instead of guessing. Remediation suggestions come from the assistant researching a secure version of the flagged library. When no safe alternative exists, Mend.io explains the risk rather than forcing a fix that might break something.
Does Mend.io only catch problems in code written inside a supported AI assistant, or does pasted AI code get checked too?
Mend.io scans any code that lands in your repository, regardless of where it came from. Point-of-generation scanning is specific to connected AI assistants, but pull-request and CI/CD scans check everything committed, including code pasted in from ChatGPT, Claude.ai, or anywhere else.
Why can’t my existing SAST tool just cover AI-generated code?
Because most legacy scanners weren’t built for how fast AI assistants write code or the patterns they write it in. AI-generated code arrives faster, in unfamiliar patterns, with less human review, so Mend.io scans it at the point of generation with AI-code-tuned checks instead of a generic ruleset.
Stop managing alerts.
Start reducing risk.
Join the teams reducing remediation effort by 75%.
