Data governance
Data governance is the set of policies, roles, and controls that decide who can access, change, and be accountable for a company's data.
What is data governance?
Data governance is the set of policies, roles, and standards that decide who owns a piece of data, who can access or change it, and how its quality and compliance get enforced. It covers accountability, who's responsible for a dataset, access, who can read or write it, and quality, whether it's accurate and consistent enough to rely on. A company without data governance has no answer to who's allowed to touch it or whether it can be trusted.
How does data governance differ from context management?
Data governance sets the policy: who owns a dataset, what quality bar it has to meet, what compliance rules apply. Context management is one place that policy gets enforced for AI agents specifically, deciding what an agent actually has access to at the moment it retrieves something and keeping that information current. Data governance can exist without a single AI agent in the company. Context management only exists because agents need a live, permissioned view of whatever data governance has decided is available.
How does data governance work in an AI-native company?
Governance policy gets expressed as access rules, retention rules, and ownership records, then applied wherever data lives or moves. In a company running AI agents against its own knowledge, every agent and API key inherits the same access rules as the people who set them, every retrieval gets logged against who or what asked for it, and a change to a governed file stays traceable to its source. Qontext's context repository enforces governance at the level of individual files and folders, so an agent's access to context matches the policy a company has already set, rather than running on a separate, unmanaged copy of the data.