AI Data Guard

AI inputs, outputs and consumption cross more than one boundary. Sensitive information may be exposed before a prompt reaches the model, unsafe, inaccurate or non-compliant content may create impact after the response leaves it and uncontrolled use can generate material cost across multiple models and providers.

Controls embedded in prompts, applications or model platforms cover only part of that journey and vary by use case. Organisations need governance that remains independent of the selected model, can act in both directions before data leaves or an action occurs and can apply cost policy consistently across AI services.

An inline mediation layer applies policy at the point where data crosses the AI boundary. Sensitive information can be masked, requests can be constrained, model and token usage can be governed and outputs can be inspected or redirected before they create impact.

• Defined information can be prevented from leaving the governed environment under the applied policy
• Unsafe or non-compliant outputs are intercepted before use
• Controls remain independent of the selected model
• Each decision is available as operational evidence

The exact scope, controls and evidence depend on the customer environment and are agreed before implementation.