What to do
A secure AI coding workflow limits what the tool can read and execute, removes secrets from context, isolates agent runs, enforces automated checks, and keeps a human accountable for every production change.
Map data and permissions
Document source code, prompts, logs, telemetry, and generated artifacts that leave the environment. Check retention, model-training controls, subprocess access, network access, identity scopes, and administrator visibility.
Use least privilege
Run agents with short-lived credentials, restricted network access, and the smallest repository and cloud permissions needed. Put experimental work in a sandbox without production data or deployment rights.
- Keep secrets out of files and prompts.
- Require approval for commands and external writes.
- Pin or review generated dependencies.
- Log actions without logging credentials.
Make verification mandatory
Protect branches, require review, scan dependencies and secrets, and run tests before merge. Define an incident path for accidental disclosure or unsafe execution, including credential rotation and audit-log preservation.
Practical checklist
Continue researching
This guide is an editorial framework, not a product endorsement. Recheck vendor documentation and your organization's requirements before making a purchasing or security decision.