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"Vibe coding" is here to stay.
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.
What Is Vibe Coding?
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.
Principle #1: Treat AI Like a Junior Developer (Not an Oracle)
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:
- Why did you do it this way?
- What assumptions are you making?
- Where is input validated?
- How does authentication work?
- What happens when something fails?
AI agents may still require significant human intervention. That's not a weakness-it's the point. Humans own the responsibility.
Principle #2: Use SRR - Small, Reversible, Reviewable
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:
- 20 files modified
- New integrations added
- Sweeping refactors introduced
- Dependencies pulled in
- No reviewer can confidently validate what's happening
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.
SRR = Small, Reversible, Reviewable
This is how we want AI to write code:
- Small changes in bite-sized commits
- Reversible work that can be rolled back cleanly
- Reviewable code that a human can actually validate
AI makes it tempting to move faster, but secure teams don't abandon good engineering practices just because code can be produced more quickly.
Principle #3: Use Security as a Prompt Constraint
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:
- Authentication expectations
- Input validation rules
- Logging requirements
- Data sensitivity handling
- Authorization boundaries
These are not "nice to have" details-they define whether the output is safe.
Don't Go Wild West with Prompts
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.
The VIBE Framework: A Practical Model for Secure AI Development
To make this approach repeatable, you can think of secure vibe coding through the lens of an acronym:
VIBE = Vision, Interfaces, Build Loops, Enforcement
Each part plays a role in keeping AI output aligned with secure engineering.
V = Vision
Vision means defining what "success" looks like before AI generates anything.
This includes:
- The desired behavior
- The expected outcomes
- What must not break
- What constraints must be honored
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.
I = Interfaces
Interfaces means defining trust boundaries and system constraints up front-and forcing AI to work within them.
That can include:
- Repository-level constraints (which files AI can touch)
- Architectural boundaries (what services can call what)
- Rules for data access and authentication
- Explicit "do not modify" zones
It should be a layered approach with gates, so the AI clearly understands:
- Where it is allowed to work
- Where it must not work
- What it can't change without human approval
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.
B = Build Loops
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:
- Clarify what you want
- Generate the code
- Review the code
- Test the code
- Ship the code
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:
- Review generated code before anything else
- Generate and review tests as part of the process
- Validate behavior before shipping
E = Enforcement
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:
- SAST
- Dependency scanning
- Secrets scanning
- Other security checks in CI/CD
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:
- PR labels that indicate AI-generated code is present
- Tags or annotations in code blocks
Checklists that confirm required steps were followed
For example:
- Did you do a quick threat model for a critical feature?
- Did you evaluate risk and trust boundaries?
- Did you review the code and tests before production?
- Enforcement is what makes secure AI development repeatable across teams.
Secure Vibe Coding Can Make Teams Faster and Safer
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:
- Reduce time spent on repetitive tasks
- Move faster without losing control
- Focus developer effort on the "critical thinking" work
- Avoid shipping vulnerabilities at machine speed
Vibe coding is powerful. The goal isn't to stop it. The goal is to learn how to do it securely.
Want to Build Secure Code (Even in the Age of AI)?
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!
