In contrast, emerging methods of safeguarding PII, along with new privacy enhancing-technologies, combine a robust process of securing data. One such innovation leverages partitioned knowledge orchestration involving two partners that want to share insights and a third-party facilitator that never accesses any identifiable or usable data. This approach enables partners to match and identify overlaps of their data without ever losing custody of any of their data. It also enables sharing of insights but only non-identifiable attributes and only on matched records. This creates a zero-trust framework for data sharing and blocks re-identification. Layering in best practices associated with salted hashing and encryption makes it so that neither sharing partner nor the blind third-party facilitator can make use of any identifying elements that they did not already have. Further, because data partners only learn new insights about individuals for whom they already had data, there’s no residual data left behind. A matching process like this, without disclosure of PII, whether raw PII or pseudonyms, eliminates the largest and otherwise unavoidable risk vector associated with sharing data: the dissemination of identities. This means that a business risk profile is no longer an aggregation of all of the risk profiles for each and every data partner with whom they work. In a world where there is a tidal wave of demand for data from novel, more extensive, and more comprehensive sources, while, at the same time, hackers are continuing to escalate their activity, it’s essential that we continue to be looking for innovative approaches for connecting data safely and securely. Technology is changing; how we deal with big data must change with it. 13