Secure enterprise search: secure AI enterprise search that enforces permissions at query time

InSearch is secure enterprise AI search built to clear that review. It is permission-aware, inheriting and enforcing each source's access controls so it never surfaces what a person cannot see. Your data is encrypted in transit and at rest, access is read-only and scoped, and it never trains models on your content. Security and groundedness are the product, not an afterthought, so your security team can say yes.

The risk that actually worries reviewers is not the model, it is the index. Any system that reads across your company has to store a representation of that content somewhere, and the moment that store does not carry the original access controls, you have built a permission bypass with a search box on it. The classic failure looks harmless in a demo: a shared index, a filter applied after retrieval, and one day a query returns a snippet from a document the person was never allowed to open. InSearch inherits item-level permissions from each connected source and enforces them at query time, before content is retrieved, so the scoping is not a post-filter that can be circumvented.

Freshness of those permissions is the second question worth asking any vendor, including us. Many enterprise search products refresh their identity and access-control data on a schedule, and some of the largest platforms let that interval stretch to days. Whatever the number is, it defines your revocation window: the period between someone losing access in the source system and the search product agreeing. When an employee leaves or moves teams, that window is the exposure. Ask for the number, not the reassurance.

Training is the third question and it has a one-word answer. InSearch does not train models on customer content, ever, under any tier. Your material is used to answer your questions and for nothing else. That commitment is easy to state and worth confirming in writing during procurement, because 'we do not train on your data by default' and 'we do not train on your data' are different sentences.

For the review itself, the artifacts that shorten it are predictable. Reviewers want to know what access the connectors request and whether it is read-only, how data is encrypted in transit and at rest, where it is processed, how long anything is retained, what happens to it on termination, and how deletion is handled. They also want the answer to a question few vendors volunteer: what does the system do when it is uncertain. An enterprise search product that returns a confident, unsourced answer is a security problem as much as an accuracy one, because it gets acted on. Citations to source documents are a control, not a convenience.

Groundedness and security reinforce each other here. Because every answer traces to specific documents the asker was already entitled to open, there is no path by which the system produces information that has no legitimate origin. That is the property that lets a security team approve deployment to the whole company rather than to a fenced pilot group.

Security reviews usually start with access control, covered in permission-aware AI search and in more depth in our guide to permission-aware enterprise search. Teams migrating off a platform with weaker document-level controls should also read the Quick Suite document-level permissions migration. If the corpus you are securing starts with a SharePoint intranet, intranet search engine covers how AI intranet search handles permissions at the intranet boundary.