Developers are increasingly using AI to generate software-writing less code themselves and relying on tools that can suggest functions, scaffold services, and even build entire features. That speed is powerful. But it also comes with a real risk: if we treat AI like an all-knowing oracle, we'll end up shipping insecure code faster than ever.
So, what does secure vibe coding look like?
This post outlines a practical framework for using AI in software development without compromising security fundamentals that keep teams safe.
In this context, vibe coding means using AI to write software, where the developer no longer has to type every line manually. Instead, the developer guides, prompts, reviews, and ships code generated by AI.
The problem isn't the concept. The problem is how quickly "generate" can turn into "merge," especially when the output looks correct.
Secure vibe coding starts with one key mindset shift.
AI can produce code that looks polished, but that doesn't mean it's correct, secure, or maintainable. A better mental model is to treat AI like a junior developer.
It's fast. It's confident. It's helpful
And it needs oversight
That means reviewing AI-generated code with the same rigor you'd apply to someone fresh out of school writing production code for the first time.
You should ask questions like:
AI agents may still require significant human intervention. That's not a weakness-it's the point. Humans own the responsibility.
One of the fastest ways AI-assisted development becomes dangerous is when it generates too much at once.
Large changes create the worst-case scenario:
Even if the reviewer spends hours reading, the final outcome often becomes: "This is broken. Undo it." But by then, it may already be merged-or it may be painful to reverse.
That's why secure vibe coding needs SRR.
This is how we want AI to write code:
AI makes it tempting to move faster, but secure teams don't abandon good engineering practices just because code can be produced more quickly.
Secure vibe coding doesn't happen by accident. It happens when security requirements are part of the prompt itself.
That means prompts shouldn't be "write a login feature."
They should include security constraints like:
These are not "nice to have" details-they define whether the output is safe.
One of the most important ideas here is avoiding reinvention.
Security teams don't reinvent secure patterns every time they implement auth, validation, or logging. They use playbooks. Tested patterns. Proven constraints.
AI-assisted development should work the same way.
Instead of starting from scratch every time, teams should create reusable prompt templates-essentially "security paragraphs"-that can be inserted into prompts for common tasks.
This creates consistency and reduces the chance that a developer forgets critical security requirements during a fast-moving build.
To make this approach repeatable, you can think of secure vibe coding through the lens of an acronym:
Each part plays a role in keeping AI output aligned with secure engineering.
Vision means defining what "success" looks like before AI generates anything.
This includes:
Secure vibe coding is not: "Let's start prompting and see where we end up." It's: "We know where we need to end, and we'll build toward that outcome."
This is where prompt engineering becomes a security control, not just a productivity trick.
Interfaces means defining trust boundaries and system constraints up front-and forcing AI to work within them.
That can include:
It should be a layered approach with gates, so the AI clearly understands:
A Common Example: Third-Party Library Creep
AI often introduces dependencies casually.
A secure interface constraint should be: Do not add third-party libraries unless explicitly recommended and approved. This prevents hidden risk from unvetted packages entering your build because an AI thought it was "the easiest way.
Build loops are the steps your team follows to ship software-and secure vibe coding requires a loop that includes validation.
A secure AI build loop looks like:
The key idea: We are not doing "generate to push."
AI doesn't remove the need for review and testing. It increases the need for them.
Secure teams should:
Enforcement is how you ensure AI actually follows the rules you set.
This includes two layers:
1. Use Existing Security Tooling
Run the same controls you already rely on for human-written code:
AI should be held to the same standards as developers.
2. Add Guardrails for AI-Generated Code
Secure vibe coding also benefits from visibility and process controls, such as:
Checklists that confirm required steps were followed
For example:
When security is built into the way your team uses AI – through small changes, strong constraints, clear interfaces, disciplined build loops, and enforceable guardrails – vibe coding becomes more than a productivity trend.
It becomes a way to:
Vibe coding is powerful. The goal isn't to stop it. The goal is to learn how to do it securely.
Security Journey helps organizations train developers to write secure code, reduce vulnerabilities, and build security into the software development lifecycle-whether code is written by humans, AI, or both. Schedule a demo to learn more!