AI coding assistants have become a normal part of how developers work because the benefits are so valuable. They speed up development and reduce repetitive work, but this also brings more risk. Teams need targeted training to ensure that AI-generated code is not only functional but also secure.
SecureFlag is leading the way in training developers for this new reality. Today, we’re releasing a preview of our new hands-on AI-Assisted Development Labs for developers working with AI coding assistants and need the skills to do so safely and securely.
The Shift from Writing Code to Guiding AI
Now that AI coding assistants, such as GitHub Copilot, Claude, and ChatGPT, are in the picture, developers are using them daily, and their role is changing.
Teams have to:
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Turn product requirements into prompts or instructions for AI assistants.
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Review and validate code generated by AI.
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Write fixes for security defects identified by other tools (e.g., SAST)
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Create custom skills, agents, and MCP integrations to safely automate tasks.
The focus is more on how to direct an AI to produce secure code, find vulnerabilities, and, of course, how to mitigate them when something gets through.
The Security Risks of AI-Generated Code
While AI coding assistants can generate large amounts of code quickly, the more code they generate, the greater the risk of security issues. Research has shown that AI can reproduce insecure patterns, and because it generates much more code than a developer would manually, those vulnerabilities can appear across a larger surface area.
Context-specific risks are often missed, or the assistant produces output that looks correct while still containing security issues. Yet, in many cases, developers have been given these tools without guidance on how to use them safely.
Teams are also adding integrations to automate development tasks, which further increases the potential attack surface. Security tools can catch some of these issues, but they don’t solve the underlying problem. Developers need to understand how to guide AI assistants and review their output effectively, and then fix issues properly.
What Developers Will Learn
These practical labs are built around the tools and technology your developers already use. They teach developers how to interact with AI assistants to produce secure code and how to detect problems if vulnerabilities get through.
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Produce secure code with AI assistants: Learn how to structure prompts and guide AI assistants so that security is considered from the start.
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Review AI-generated code for security issues: Identify security issues in AI-generated output and apply structured review methods to catch problems before production.
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Fix vulnerabilities identified by security tools: Interpret findings from scanners such as SAST and Claude Code Security, then write secure remediation code.
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Create secure skills, agents, and MCP integrations: Consider security implications when building automations to reduce risk.
For security leaders, this is a practical way to get ahead of a risk already in the pipeline. Seeing as developers will continue using AI tools, these labs ensure they do so safely.
Training for How Development Works Today
SecureFlag has always delivered developer-focused training based on realistic attack scenarios in hands-on environments. AI-Assisted Development Labs are the next step, giving teams the skills they need to work securely with AI code assistants.
Organizations that start encouraging these skills now will be in a much stronger position as AI becomes further embedded in the software development lifecycle.
The first labs are available, and new content is already on the way.
Want to see the new AI-Assisted Development Labs in action?
