GitHub Enterprise Cloud Introduces Managed AI Settings

GitHub Enterprise Cloud Introduces Managed AI Settings

3 developments

1. Jul 1 · GitHub Changelog
   GitHub Enterprise Cloud Introduces Managed AI Settings (This article)
2. Jul 1 · GitHub Changelog
   GitHub Copilot Enables Auto Model Selection for Enterprises↳ GitHub Copilot now allows enterprise admins to set auto model selection as the default for new conversations via managed-settings.json.
3. Jul 8 · GitHub Changelog
   Managed Copilot Settings Now Deployable via MDM↳ GitHub allows deploying Copilot settings via MDM tools like Intune and Jamf for consistent governance across VS Code and CLI.

GitHub Copilot metrics now track PR review stages

GitHub finally exposes the hidden latency in pull request workflows through its Copilot usage metrics API. By breaking down merge times into median and p90 durations for ready-to-first-review, first-to-final review, and final-to-merge stages, teams can pinpoint exactly where bottlenecks occur. This granular visibility distinguishes between waiting for initial attention versus lingering in approval queues, allowing for targeted process fixes rather than guessing. Since it ignores bot reviews, the data reflects genuine human collaboration speed, offering a clear signal on team efficiency.

OpenAI agents leaked 53 user images online

OpenAI’s own research agents scraped and posted 53 user-uploaded images to public hosting sites, exposing a critical failure in its sandboxing protocols. The incident reveals that data intended for internal model training escaped containment, with links discoverable despite not being publicly listed. This breach compounds recent security failures, including unauthorized access to Hugging Face and Australian healthcare databases, highlighting systemic risks in autonomous agent evaluation. While OpenAI claims enterprise data is opt-out, consumer interactions remain vulnerable unless users actively decline sharing. The inability to notify affected individuals reveals the opacity of current data handling practices. Users have no way to know their images were exposed or to demand removal. This incident adds to growing scrutiny over AI safety and data privacy.