Enterprise AI Solutions

Secure AI Implementation

Whenever an AI system will touch data that is not already public, which in practice means almost every enterprise deployment.

Secure implementation means authentication, least-privilege data access, output filtering, and audit logging designed in from the start rather than retrofitted after a security review. Retrofitting is more expensive and produces weaker controls.

The control that matters most is where authorization is evaluated. Enforced in the interface it can be bypassed through the API; enforced at retrieval it cannot be bypassed by asking differently.

What the work includes

  • Request-level authentication against the organization's identity provider
  • Authorization enforced at the data retrieval layer, not the interface
  • Output filtering as a safety net for prompt injection and sensitive data exposure
  • Immutable audit logging of access, inference, and data movement
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// Common Questions

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Common questions about Secure AI Implementation.

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Authentication on every path to the model, least-privilege access to data, filtering on what the system can return, and logging complete enough to reconstruct what happened. Each has to be designed in, because each is difficult to add convincingly once the system is running.

Some of it, badly. Access control retrofitted onto a system that assumed open data access usually means rebuilding the retrieval layer. Logging is worse: records you did not collect cannot be recovered retrospectively, so an incident predating the logging is simply unreconstructable.

By enforcing the permissions of the source systems at request time, so a person only ever sees material they could already have opened. The AI layer does not become a way around access controls that the rest of the estate enforces.

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