Where personal and confidential data sits, who can reach it, and how it is stripped out as it moves between systems.
We design where personal and confidential data sits and who can reach it, holding sensitive data apart from the rest and granting access by role rather than by exception, so that the data most people never need is data most people cannot get to.
We automatically identify and remove personal and confidential information as data moves between systems.
Privacy work on a model is decided by design choices made early, so it is cheaper before the build than after. At Gini we write the data protection impact assessments for machine learning and AI applications, design where sensitive data sits and who can reach it, and set the redaction that removes personal information automatically as data moves between systems.
That is where the exposure usually sits, so Gini treats the copies as carefully as the source. Personal information is identified and removed as data moves, access is granted by role rather than by exception, and where a realistic test set is genuinely needed we generate synthetic data instead of extracting live records.
For testing, usually yes, and for filling gaps in rare cases, often. At Gini we generate data that preserves the statistical relationships the test depends on and state plainly which properties it does not preserve. A synthetic set is a poor basis for a final calibration and a good basis for building the pipeline that will carry the real one.
Yes. The frequent finding is an assessment written against the system as designed rather than as built, particularly where a model gained access to a field nobody assessed. Gini tests the assessment against what the system can actually reach, and reports the gap alongside the change that would close it.
