Quick answer: policies vary by tool and by plan — some train on your code by default unless you opt out, some never do, and the biggest real-world risk isn't training at all, it's developers pasting secrets straight into a prompt. This FAQ breaks down what each major tool actually does in 2026.
Does the AI actually train on my private code?
It depends entirely on the tool and plan. Some vendors default to opt-in training on interaction data unless you explicitly turn it off; others exclude business/enterprise tiers from training entirely, and some route everything through a dedicated privacy mode that disables both training and third-party data retention. Never assume — check your specific plan's current policy before pasting sensitive code.
What's the general shape of policies among the major tools in 2026?
As a pattern: consumer/free tiers are the most likely to use your data for training by default, paid business or enterprise tiers are typically excluded from training programs entirely, and some tools offer an explicit privacy-mode toggle that guarantees zero data retention with the underlying model provider as well as the tool vendor itself.
What's the single biggest real-world security risk with these tools?
Context-window leakage: a developer pasting secrets, API keys, or sensitive production code directly into a prompt sent to a cloud model. No contractual promise about training data stops that, because the data has already left your machine the moment it's in the request.
What is "slopsquatting" and should I actually worry about it?
It's a 2026-era supply chain attack: AI coding assistants sometimes suggest a package name that doesn't actually exist, and attackers pre-register that exact name on public package registries with malicious code inside. Estimates put hallucinated package suggestions at roughly 5–22% of AI code suggestions in some studies, which makes verifying every new dependency before installing it a real, not theoretical, precaution.
Is AI-generated code itself secure once it's written?
Not automatically. Independent testing has repeatedly found that roughly 40% of AI-generated code samples contain at least one security vulnerability when checked, which is why code review and standard security scanning still matter just as much — arguably more — on agent-written code as on human-written code.
Can my company legally claim ownership of code an AI agent wrote?
This depends on your jurisdiction and the specific tool's terms of service, and it's genuinely unsettled in places, so it's worth a real legal read rather than an assumption; most vendor terms grant the user rights to output, but underlying model training data provenance is a separate, still-evolving question.
What should a team actually do to stay safe?
Use a plan/tier with a confirmed zero-retention or business-grade privacy policy for anything touching real codebases, never paste live credentials or secrets into a prompt (use environment variables and secret managers instead), verify any new package an agent suggests before installing it, and keep human code review and normal security scanning in the pipeline for agent-authored code exactly as you would for human-authored code.
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