AI & Technical question
Glean supports multiple LLMs, 100+ SaaS connectors, and both UX-driven and API-driven agent experiences. How would you design a governance architecture that enforces consistent policies across models, connectors, and third-party integrations while still allowing flexibility for enterprise customers?
- Glean
- AI & Technical
- Hard
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What this question tests
Systems thinking for enterprise governance: can you design a policy architecture that stays consistent across heterogeneous models and connectors while still giving enterprise customers real flexibility.
How to approach it
- Separate policy definition from policy enforcement: define governance rules, like data access scope and allowed actions, once in a central policy layer, then enforce them consistently at the point where any model, connector, or integration touches enterprise data.
- Standardize a common permission and audit interface that every connector must implement, regardless of the underlying SaaS system, so governance does not vary by which of the 100+ connectors is in play.
- Handle multi-LLM support by keeping governance model-agnostic: policies constrain what data and actions are available, not how a specific model behaves, since model behavior is the wrong enforcement point for consistency.
- Give enterprise customers flexibility through configurable policy templates and scoped exceptions, rather than a single rigid ruleset, so different customers can tune sensitivity without touching the core enforcement mechanism.
- Require both UX-driven and API-driven agent paths to route through the same policy layer, so a governance rule cannot be bypassed just because the agent was invoked programmatically instead of through the UI.
What a strong answer includes
- Separates policy definition from enforcement and makes enforcement model-agnostic, which is the key architectural move for consistency across 100+ connectors and multiple LLMs.
- Explicitly closes the API-versus-UX bypass gap, a real and often-missed governance hole.
- Gives flexibility through configurable templates rather than a fixed ruleset, addressing the enterprise flexibility half of the question directly.
Common mistakes
- Proposes per-connector or per-model governance rules, which do not scale consistently across 100+ integrations.
- Ignores that API-driven agents need the same enforcement as UX-driven ones, leaving a bypass path.
Likely follow-up questions
- How would you onboard a new connector without weakening the common enforcement interface.
- What would you do if a customer's requested exception conflicts with a baseline safety policy.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture