Sensitive data held back before a request reaches a model.
Data Protection applies through Guard. Personal and sensitive data is redacted or held back before a request reaches a model, according to the rules you set.
Client and personal data gets pasted into AI tools every day, usually without anyone intending harm.
One leak can mean a breach, a regulator, a client conversation and a bill, long before anyone sees the pattern.
extra cost on the average UK breach where shadow AI use was high (on a £3.78m average)
IBM, Cost of a Data Breach, UK, 2025 (vendor research)The benefits of Data Protection.
The people who feel the problem first.
Reduce the chance of a reportable breach.
Protect privilege and client material.
A control that works in the flow of work.
Three ways teams use Data Protection.
Redact customer details before a request reaches a model.
Stop confidential documents leaving the business.
Block sensitive files and notify the owner.
Works best alongside.
The right model for each task, so premium is by exception.
See Model Choice RulesPolicy as enforced rules: approved models by task, data type and risk.
See Rules LogLog: the record of events.
See Log All of GuardSystems, controls and process designed with AI agents and aligned to the regulations, your policies and outside policies. Used with Evidence.
See the Guard modulePrices are indicative ranges, confirmed in writing before you commit. Estimates are replaced by your own figures once your health check runs.
How the supporting tools fit together
GenWA Frame brings together your policies, business knowledge and our methodology to guide the work.
The Log records what connected AI saw, produced and who approved it.
The Intelligence Layer is being built to turn those records into trends and answers linked to their source. We agree which tools are available and appropriate for your engagement.
Find out more about the GenWA FrameSee Data Protection working on your business.
Start with a health check. Agree the scope, fees and next steps with us.