Buyers now ask whether a target uses AI coding assistants, how output is reviewed, what code and data is sent to those tools, and who owns the result. A short written policy plus evidence of review gives a straight answer and avoids a long debate.
Questions to expect
Usage. Which assistants do engineers use, and is it sanctioned or informal?
Review. Is AI assisted code reviewed like any other change? Do pull requests need a human approver?
Data exposure. What goes into prompts: proprietary source, customer data, secrets? What are the retention terms of each tool?
Licensing and ownership. Do you track whether suggestions match existing licensed code? What do the tool terms say about output ownership? Have counsel read them.
Quality and security. Do scans run on all code regardless of origin?
What a good policy covers
- Approved tools and the accounts to use.
- What may not be pasted: secrets, personal data, customer content.
- Required review and testing for every change.
- Scanning in the build for vulnerabilities, secrets and licenses.
- Logging or review of agent permissions if autonomous agents are used.
Evidence beats assurances
Show branch protection, required reviewers and scan results on pull requests. These apply to every change, so they cover AI output without special handling. Be careful not to quote percentages of AI authored code unless you measured them.
Agents with access
If agents can run commands, open pull requests or call internal tools, buyers will ask what they can reach. Use scoped credentials, an audit trail and approval steps for risky actions.
Don't over promise
Avoid saying AI code is "fully reviewed" unless process data proves it. Say what the process is and where it is enforced.
Safeguard offers policies, gates and guardrails, plus an MCP Guardrail and agent identity controls, for teams using AI coding tools.
Questions to ask engineers directly
Policy documents tell you intent. A few direct questions tell you practice.
Ask an engineer to show the last pull request where an assistant helped, and walk through who reviewed it. Ask what they do when a suggestion pulls in a package they have not used before. Ask whether they have ever pasted a secret or customer data into a tool, and what happened. Ask how they would know if an agent changed something it should not have.
Good answers are concrete and a little imperfect. Perfect answers with no examples suggest a rehearsed story. Note gaps as integration tasks: scanning on every change, scoped agent credentials and a short written usage policy are all quick to add.
Quick check
Should we ban AI assistants before a sale?
Not necessarily. Buyers care about control and records, not abstinence.
Last reviewed: 19 October 2026.
