An information barrier is a control that prevents material non-public information from moving between business units that are required to stay separate — most commonly between an investment bank's advisory side and its trading and research functions. The obligation is long-standing and well understood in email, chat and document systems. It is largely unimplemented in AI.
The AI problem is structural. A shared assistant, a shared vector index or a shared cache is a channel between everyone who can reach it. A banker asks a question about a live deal; the content is embedded, retrieved or cached; a colleague on the other side of the barrier asks something adjacent and the system, doing exactly what it was built to do, surfaces it. No one leaked anything. The architecture did.
How it differs from access control. Access control asks whether a person may reach a system. An information barrier asks whether this content may reach this person, and the answer depends on which deal, which desk and which day. It is a content-and-identity decision made per request, not a permission granted once.
Why the cache is the exposure people miss. Deduplication and semantic caching are both, by design, mechanisms for showing one person something produced for another. Any AI cost-optimization layer deployed across a barrier needs a partition that honours it, or the saving comes out of the control.
Smartflow enforces barriers at the AI gateway, using group identity combined with real-time content classification.
Enforcing a barrier against an agent
An information barrier assumes the system can name the party it is separating. That assumption quietly breaks when an autonomous agent is in the path. If the agent acts on a credential belonging to a person, every barrier is evaluated against that person, and an agent working across both sides of a wall looks in the record like an employee who did.
Enforcing a barrier at runtime therefore needs directory group membership as the boundary, classification of the content in flight rather than only the destination, and an identity for the agent distinct from its launcher. Directory-bound identity supplies the first, DLP for AI the second, and agent governance the third. The rules themselves are expressed and versioned in Maestro. For a broker-dealer the barrier and the supervision obligation under FINRA Rule 3110 are examined together, which is why both appear on the financial services page.
Related terms
FINRA Rule 3110 · DLP for AI · Policy as code · GLBA · Human in the loop · Full glossary
Put this in the path of your own agents.
Policy enforced inline between your agents and every model and tool they reach, with a record bound to the human who owns it.
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