Singulr AI Glossary

Granular AI policies are fine-grained rules that govern exactly how specific AI models, agents, and tools are allowed to operate within an organization. Instead of blanket policies like "AI is approved" or "AI is restricted," granular policies define precise boundaries — which users can access which models, what data types each AI tool can process, which actions an agent is authorized to take, and what conditions trigger human review. Granular policies matter because broad, one-size-fits-all AI rules don't work in practice. An AI tool that's perfectly safe for the marketing team to use for content drafting might be unacceptable for the legal team to use with confidential case files. An agent that's authorized to read a knowledge base might not be authorized to update records in a CRM. Different use cases carry different risks, and effective governance reflects that difference. Granular AI policies typically cover multiple dimensions: user and group-level permissions (who can use what), data classification rules (what types of data each AI tool can access), action-level controls (what operations an agent can perform), contextual conditions (time-based restrictions, approval workflows, geographic limitations), and exception handling (what happens when a request falls outside policy). The best implementations allow policies to be defined once and enforced consistently across all AI systems in the environment. For enterprises, granular AI policies are what make the difference between a governance program that exists on paper and one that actually works in practice. They enable organizations to say yes to AI adoption in specific, controlled ways rather than defaulting to blanket restrictions that slow down the business or blanket approvals that expose the organization to risk.