Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The developers and engineers here at 1Password are always working to improve our products. With all the active development to introduce features, fix bugs, and enhance the overall user experience, numerous code changes go into every release. We strive to ensure each iteration is better than the last and that new code doesn’t introduce vulnerabilities. A key part of this process is our Product Security (ProdSec) team’s review of all code changes that may have security implications.

Most AI governance programs assume they know what they're governing. They track which AI tools employees use through browser proxies and SSO logs, block access to unauthorized platforms, and monitor data leaving through known egress channels. Shadow agents break every one of those assumptions. Agents run locally, act autonomously, and access data through pathways the tools monitoring your environment were never built to see, creating a new, and difficult to govern, attack surface.

A customer support agent needs a payment reference, a token or transaction ID, to issue a refund. A summarization agent reading the same ticket needs none of it. A billing agent needs only the last four digits to match a transaction. A fraud agent needs the full credit card number, but only when a case is open and only for the account it is reviewing. Traditional DLP sees one thing across all four: sensitive data, a 16-digit string that matches a card pattern. It makes one choice: block, redact, or allow.

AI assistants are repeating a common Git mistake: committing fixes that remove secrets only from the latest code, not from repository history. GitGuardian AI Skills can help.