Natural-language analytics has changed who can ask questions of company data. An employee no longer needs to know SQL or navigate a reporting tool before they start exploring. An agent can retrieve the data and do the analysis.
That makes access control more important. When an AI system can query a warehouse on someone's behalf, the data team needs to decide exactly which data that person is allowed to use.
Supper User Entitlements gives administrators that control. It determines which schemas, tables, and columns an individual user can access through Supper. Administrators can set allow and deny rules for each user, from broad access across a data domain to a restriction on a single column.
The aim is to make company data easier to use without making sensitive data easier to reach.
A permission model for the data inside Supper
User Entitlements are the data-access permissions that apply when someone uses Supper. They operate at the level of schemas, tables, and columns.
An administrator may grant access to an entire schema when a user needs to work across a data domain. A more targeted rule can limit that user to a particular table. When a table contains sensitive fields, column-level permissions can narrow access further.
Supper supports both allow and deny rules. The structure takes inspiration from AWS IAM, which gives administrators room to start with broad wildcard-style access when it makes sense and then define exceptions where they need them. Permissions can also be copied or cloned when several users need the same access pattern.
This level of control reflects how company data is actually organized. Few organizations can manage access well with a small set of generic roles. A user may need access to product data for day-to-day analysis while still being excluded from a sensitive field in that same environment.
Why agentic analytics changes the access question
Data teams have always managed access to sensitive information. Agentic analytics raises the stakes because it removes much of the work involved in reaching that data.
With a traditional reporting tool, the available dashboards and fields place some limits on what a user can explore. An AI agent can take an open-ended question and turn it into a query. If the underlying connection exposes a wide range of data, the agent may be able to reach far more than the user should see.
This is why access controls need to be part of the analytics system itself. The agent should work with the same boundaries that apply to the person asking the question.
User Entitlements gives Supper an explicit data surface for each user. Data teams can connect the schemas and tables that make Supper useful, then control which parts of that data are available to each person.
Product roles and data entitlements solve different problems
Supper controls access along two separate tracks.
Product roles determine what a person can do inside Supper. An administrator can manage the semantic model and use configuration controls. A regular user can ask questions and view answers without receiving administrative access.
Data entitlements determine which company data that person can use. The entitlement rules apply independently of the product role, so an administrator can set data access at the schema, table, or column level.
This distinction keeps two decisions clear. One concerns how someone can use the product. The other concerns which data is available while they use it.
Granular controls make broader access practical
Data teams often hesitate to connect more data to an AI system because each additional table may create another access question. That caution is reasonable when a warehouse connection exposes too much by default.
User Entitlements changes the calculation. A team can make more data available in Supper while setting explicit boundaries for each user. Someone who needs to work with a particular schema can have that access. Someone else can be limited to a smaller set of tables. Sensitive columns can remain unavailable even when the rest of a table is useful.
The result is a more practical way to expand analytical access. Employees can explore the data relevant to their work, and data administrators keep control of the underlying permissions.
Built for the way data teams work
Sophisticated permissions are not only for large companies. A growing business may still have customer records, payroll information, or other data that needs tighter control. The right access model should reflect the sensitivity of the data, not the size of the company.
Supper gives data teams a granular permission layer for that work. They can manage rules directly through the entitlement structure or use the permission-management interface to configure access. When a pattern is common across a group of users, permissions can be copied and adapted.
That gives teams a way to support broader use of agentic analytics while maintaining clear boundaries around company data.
Frequently asked questions
What are Supper User Entitlements?
User Entitlements are Supper's data-access permissions. They determine which schemas, tables, and columns a user is allowed or denied access to when using Supper.
Can Supper permissions be set at the column level?
Yes. Supper supports permissions at the schema, table, and column level, including column-level rules for more sensitive data.
Does Supper support allow and deny rules?
Yes. Supper's entitlement structure supports both allow and deny rules, which administrators can use to create broad access patterns or more specific restrictions.
How do User Entitlements relate to product roles?
Product roles determine what someone can do in Supper. Data entitlements determine what company data they can access while using it. These are separate controls.