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The best practices for vibe coding come down to one principle: the faster an AI agent writes code for you, the more deliberately you must plan, constrain, test and review it. Vibe coding can produce working software quickly, but a working demo is not the same as secure, maintainable code. The ten practices below set out how to keep control of an AI-assisted project, and how to scale the effort to the risk of what you are building.
What vibe coding means, and what it does not guarantee
Vibe coding describes the high-autonomy end of a wider spectrum of AI-assisted development. At one end, an AI tool offers autocomplete while the developer stays firmly in control. Further along come test-driven or module-level generation, and at the far end the agent generates most of the code with limited review. The National Cyber Security Centre (NCSC) describes this range in its article of 18 June 2026. The term is not a badge of quality, and it does not on its own define a safe process.
The useful goal is not a prompt that produces production-ready code. It is a workflow that lets a person move quickly while still answering for how the software behaves, how secure it is and whether anyone can maintain it later.
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The NCSC’s central point is that oversight should follow consequence. Its headline statement reads: “Different code deserves different levels of oversight, so calibrate your approach to ‘vibe coding’ accordingly.” A throwaway prototype that never touches real user data needs a different level of scrutiny from code that handles logins, personal records or credentials.
#1 Best Overall
| Factor | Prototype or limited internal tool | Authentication, sensitive data, credentials or public-facing service |
|---|---|---|
| Data involved | Dummy or non-sensitive data | Personal, financial, confidential or otherwise restricted data |
| Who is exposed | A small internal group | Customers, the public or other organisations |
| Consequence of a flaw | Inconvenience, rework | Account takeover, data exposure, operational or safety harm |
| Reversibility | Easy to discard or rebuild | Hard to undo once data has leaked or users have relied on it |
| Review expected | Lighter, focused on whether it works | Full code review, security checks and qualified sign-off before release |
The NCSC presents a proof-of-concept or limited-risk internal tool as one that may justify less oversight than systems handling authentication or sensitive data. Decide which column your project falls into before you write the first prompt, because it sets the rest of the process.
Before the first prompt
1. Define the user outcome and acceptance criteria
Write down who the feature is for, what it must do and how someone will know it works. A sentence such as “a signed-in user can export their own invoices as CSV, and no one else’s” gives the agent and the reviewer the same target. Google’s guidance on coding-agent workflows recommends preparing product requirements and design before production implementation begins. Without a stated outcome, you have no reliable way to judge whether the generated code is finished.
2. Ask for a plan before any implementation
Have the agent describe the intended behaviour and system design before it changes files. Google’s lifecycle guidance suggests keeping product requirements separate from architectural specifications and building against those documents. Read the plan critically: check that it names the files it will touch, the data it will read and write, and the external services it will call. A plan you do not understand is a warning sign, not a formality.
While the agent works
3. Give the agent bounded tasks and enough context
Ask for one feature or one module at a time, not a complete application in a single request. Google warns that zero-shot prompts, where a large system is requested in one go, can lead to technical debt. Small, well-described changes are also easier to test and to roll back. Supply the context the agent needs, such as the existing project structure, naming conventions and the parts of the codebase it must not alter.
4. Put constraints and security expectations in the request
State the access rules, input validation requirements, data-handling expectations and project conventions that apply to the change. OpenSSF’s guidance on security-focused instructions for AI coding assistants, published on 16 September 2025, finds that clear, careful instructions improve the chance of correct and secure output. The same guidance is equally clear that assistants can still make mistakes. Treat a well-written prompt as a useful control, not as proof that the result is secure.
5. Keep sensitive data and credentials out of reach
Do not give the tool sensitive, personal, classified or otherwise restricted information unless its use has been approved. Before you paste logs, database dumps or configuration files into a prompt, consider what context the tool sends to its provider. Check the integrations as well: an agent connected to a repository, a CI/CD pipeline or a cloud account may be able to reach secrets and deployment credentials that you never meant it to see. The UK Home Office engineering standard for AI-assisted development states the data restriction directly. OWASP’s 2026 Secure Coding with AI cheat sheet maps the trust boundaries between developer, agent, repository content, model provider, tools, credentials and CI/CD systems.
6. Limit the agent’s permissions to the task
A coding agent does more than suggest text. OWASP’s cheat sheet describes agents that can run shell commands, install packages, edit files, access networks and push branches. Each of those capabilities is a security decision. Restrict the agent to the directories and commands the task needs, and require confirmation before consequential actions such as deleting files, installing new packages, changing infrastructure or pushing to a shared branch. Take particular care with automated workflows that can read secrets or deployment credentials, because a mistake there can reach production without anyone looking at it.
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Before anything merges or ships
7. Test after each meaningful change
Run the project’s normal test suite, plus the type checks, build and relevant security checks, before you accept the next change. For a Node.js project, that might mean npm test and npm run build, with a linter or static analysis tool added where your team already uses one. The UK Home Office standard requires AI-assisted changes to be tested under the same engineering standards as any other change before they are merged or deployed. Google recommends repeating its plan-and-build loop for each feature you add, so testing is part of the loop, not a final step.
8. Review the code and understand what will run
A demo that works on screen does not establish that the code is correct, secure or maintainable. The NCSC advises reviewing and understanding generated code, checking it for vulnerabilities and verifying that it behaves as expected, with more rigour as the risk rises. If you cannot explain what a function does, why a dependency is needed or where user input goes, ask the agent to explain it, then check the explanation against the code and the documentation. The accountability stays with your team, not with the tool.
Rank #4
9. Verify every suggested dependency and version
Before you accept a new package, confirm that its name is correct, the version exists, the licence is acceptable, the project is still maintained and it fits your existing policy. UK government guidance on AI coding assistants warns that these tools may hallucinate package versions, so check them against trusted registries and sources rather than trusting the suggestion. The Home Office standard requires teams to manage the risks that AI-introduced dependencies create. Dedicated application-security scanners, such as Snyk Code and Aikido, are examples of third-party tools that UK government guidance names as complements to coding assistants.
10. Gate production behind human approval and scale scrutiny to risk
Keep changes traceable through small commits and clear pull-request descriptions, and use peer review with branch protection so that no change reaches the main branch without a second person looking at it. The Home Office engineering standard sets the rule plainly: “AI-assisted outputs MUST be reviewed and approved by a human before reaching production.” Increase scrutiny for authentication, sensitive data, credentials, public-facing services and safety-critical systems. Those are the areas where a missed flaw costs the most.
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A checklist for reviewing agent-written code
Use this list before merging AI-generated changes. Scale it up for the high-risk column in the table above.
Best Value
- Does the change match the acceptance criteria and the approved plan, with no unrequested features?
- Can the reviewer explain what each changed file does and why?
- Are inputs validated, and are access controls enforced on the server rather than only in the interface?
- Does any code log, print or transmit secrets, personal data or tokens?
- Have all new dependencies and versions been verified against trusted sources and policy?
- Have the tests, build and security checks passed, and do they cover the new behaviour?
- Has a qualified human approved the change, and is the commit history traceable?
What the exposure figures show, and what they do not
ISACA published an article on 29 July 2026 reporting an analysis by RedAccess. It found more than 5,000 applications with little or no security controls or authentication, and nearly 40% exposing sensitive information. The analysis covered applications built on popular vibe-coding platforms. These figures are a useful warning about what happens when prototypes reach the public without review. They are not a measured rate of insecurity across all vibe-coded software, and they should be read as ISACA’s account of RedAccess’s findings rather than as an independently verified prevalence figure.
The lesson is practical rather than statistical. Authentication and data exposure are the failure points most worth checking, which is why the review steps above focus on them.
Reliable practice, not a shortcut
None of these ten practices removes the need to inspect and validate what an agent produces. Planning, clear security-focused instructions, iterative building, testing and dependency checks make the workflow safer and faster to review. They do not replace the judgement of the people who answer for the software once it runs.
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