Usable archives, measured risk
Kubli Echo produces a usable copy of your archives and measures its re-identification risk with the three attacks the EDPB names: singling out an individual, linking them to another dataset, inferring an attribute. Above the threshold, the file is not released.
An ordinary row from a customer database. On the left as it sits in your systems, on the right as it appears in the released file.
Product, spend and outcome stay intact: they are the reason anyone wants that file. The customer id changes, but it changes the same way in every table, so the queries you already wrote keep working.
In 2024 the European regulator made clear that two datasets looking alike prove nothing about anonymity: you have to show that nobody can be traced. Echo shows it, and the answer is a number.
Sectors
How it works
Kubli Echo produces a usable copy of your archives and measures its re-identification risk with the three attacks the EDPB names: singling out an individual, linking them to another dataset, inferring an attribute. Above the threshold, the file is not released.
Names, contact details and identifiers are removed. Ages, amounts and dates shift by just enough that nobody is recognisable. Product, behaviour and outcome stay identical: they are the reason that file is worth having.
Echo tries to single out an individual from a handful of attributes, to link them to another list, and to infer a hidden attribute from the visible ones. These are the three attacks the EDPB uses to define anonymity. Each returns a risk between 0 and 1.
The threshold is 0.30 and there are three verdicts: pass, borderline, fail. The report names the column driving the risk, and states that the threshold is a Kubli convention with EDPB rationale, not a number set by law.
The same person gets the same code in every table, so the queries you already wrote keep working. Dates all shift by the same amount, so the sequence of events stays consistent even though it no longer matches the real days.
Every report lists what the measurement does not cover, computed for that specific run: attacks sampled rather than exhaustive, bounds estimated from the data rather than declared upfront. It is the part you need the day somebody asks on what basis you decided this was allowed.
The other modules
Anomalies caught as they happen
Kubli Pulse watches your recurring flows and flags what departs from normal behaviour, before the operation completes. It works on payments, insurance claims, utility usage and returns: the model learns from your own closed cases, not an industry average.
Discover PulseDocumented automated decisions
Kubli Compass records what every automated decision about a person was based on, at the moment it is made. It produces the documentation the EU AI Act requires for high-risk systems, including differences in behaviour between groups.
Discover CompassUsable logs, anomalies surfaced
Kubli Sentinel anonymises access and system logs, so you can keep them, share them and analyse them. It then filters the stream and surfaces only what departs from a normal day on your network.
Discover SentinelTwo routes
If you have a problem to solve, start with a demo on your own case. If you are assessing us, as a fund or a procurement team, start with the confidential documentation.
Kubli issues no certifications and holds none. It hands over the measurements taken and the documents to stand behind them when someone asks.
Thirty minutes. You tell us which data is blocked and what you would do with it, and we show you the result on an example close to yours. If the case is not a fit we say so on the call.