What technical risks would you review before launching AI assistance in cloud file collaboration?

Problem Statement Description

Drive is preparing to launch an AI assistance capability inside cloud file collaboration for student project teams. The assistant may help users find files, summarize shared documents, answer questions across folders, or suggest collaboration actions, but it will operate in an environment with complex permissions, mixed-quality content, sensitive academic work, and real-time multi-user activity.

As a Technical PM, your task is to describe the technical risks you would review before launch. Focus on what could go wrong across data access, model behavior, infrastructure, user trust, and product reliability, especially when multiple students, instructors, or external collaborators share files with different permission levels.

Your response should show how you would evaluate readiness for launch without jumping directly to feature ideas. Consider both user-facing failure modes and backend/system risks, including how the product team would detect, contain, and learn from issues after rollout.

The experience should consider:

- Permission boundaries across files, folders, shared drives, comments, and externally shared content

- Data privacy expectations for student work, uploaded documents, and AI-generated responses

- Accuracy, hallucination, citation quality, and handling of incomplete or outdated file context

- APIs, indexing, retrieval pipelines, and latency requirements for collaboration workflows

- Reliability under peak usage, large files, concurrent edits, and global access patterns

- Security risks such as question injection, data leakage, unauthorized retrieval, and abuse

- Rollout controls, observability, incident response, and rollback plans

- Accessibility, cost, operational load, and product trade-offs for student teams

The goal is to assess whether you can think like a Technical PM launching AI in a trusted collaboration product: identifying the most important launch risks, connecting them to user harm and business impact, and outlining how you would evaluate readiness before exposing the feature broadly.