AI Coding Assistants and Your Code: Security, IP and Quality in Outsourced Projects
Most development teams now use AI coding assistants every day. When you outsource development, that means your source code, specifications and sometimes even production data may pass through tools you never chose yourself. This does not have to be a problem — but it should be a conscious decision, not a surprise. Here are the main risks and how a responsible team handles them.
1. Where Does Your Code Go?
Many AI assistants send code to a cloud service to generate suggestions. The questions that matter are simple: which provider, under which contract, is your code used to train models, and how long is it stored?
Good practice: business or enterprise plans with a contractual no-training guarantee and limited data retention, a list of approved tools agreed with the client, and the option to use self-hosted models for especially sensitive projects.
2. Secrets and Personal Data
The most common leak is not exotic: a developer pastes a configuration file with passwords or a log with customer e-mails into a chat with an AI assistant.
Good practice: secrets never live in the code (they are kept in a vault or environment variables), real personal data is not used in development at all, and automated secret scanning runs on every commit. If your product handles personal data, this is also a matter of GDPR compliance — see our article on what GDPR means for companies.
3. Who Owns AI-Generated Code?
Your contract should state clearly that all code delivered under the project — however it was produced — belongs to you. There is also a smaller, but real, risk that an assistant reproduces fragments of code under a restrictive open-source licence.
Good practice: explicit IP clauses that cover AI-assisted work, assistant settings that block suggestions matching public code where available, and licence scanning of dependencies as part of the build.
4. Plausible Is Not the Same as Correct
AI-generated code usually looks clean and confident. That is exactly why its mistakes are easy to miss: an outdated API, a missing permission check, an edge case nobody tested. Security issues are especially sneaky — code that works perfectly in a demo can still be vulnerable.
Good practice: every change goes through review by an engineer who understands it, automated tests are required for new functionality, and static analysis and dependency vulnerability scanning run in CI. The rule is simple: the developer who merges the code is responsible for it, no matter who or what wrote the first draft.
5. Keep It Transparent
The healthiest approach is to talk about AI openly. Agree at the start of the project which tools may be used and for what, record it in the contract or project handbook, and revisit the list when tools or requirements change.
A Short Checklist
- Approved AI tools and their data terms are agreed in writing.
- No secrets or real personal data in prompts, code or test fixtures.
- IP clauses explicitly cover AI-assisted work.
- Every change is reviewed and tested before release.
- Security and licence scanning are part of the pipeline.
AI makes good teams faster — and makes weak processes fail faster too. If you want to discuss how to use AI safely in your project, contact us.