Privacy is the foundation of everything we do.
Afore pushes boundaries. Our technology can access data that is traditionally off-limits. Data from messaging platforms could be one of the most abundant and useful sources of enterprise data to drive real change within a business. However, this use of data is only possible if it is done privately and transparently, using only decontextualized (counting of word types from text stripped of all nouns, numbers, etc.) and pseudonymized (without personally identifiable information)* data.
Afore’s privacy tech strategy is split into two parts.
Individual personal data
- User is fully aware of what Afore does before they personally opt in.
- Only the user has access to their own individual curated data.
Differential privacy
- User’s contribution to the aggregated data is guaranteed to be anonymous.
- No one can reverse engineer contributions to the aggregated data.
Pseudonymized vs anonymized
Pseudonymization is a data management process that sequesters personally identifiable information from Afore’s results. This technique uses placeholder data instead of user information and makes it extremely difficult to associate Afore’s metrics with an individual.
Anonymization is the removal of information such as name, address, phone number, photo or place of work, that may lead to the direct or indirect identification of a person.
Four key takeways
1.
Gives you control.
You run our algorithms on your infrastructure where your Slack data is stored, and you see the output before any additional processing occurs. Therefore your data never leaves your control.
2.
Never reads context.
The first step in our process is to strip all references to names, organizations, locations, monetary values, phone numbers, signatures, and email addresses.
3.
Aggregates all.
Ultimately, the output of Afore is an aggregated list of workforce attributes, which is so far removed from the individual that it makes reverse engineering impossible.
4.
Utilizes OpenMind.
Preserving machine learning. Any data quality checks performed are done so utilizing OpenMind to control and track our infrequent requests to check the data quality output.
Want to learn more about Afore and how it keeps your data secure?