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Preventing Data Exposure in M&A with Continuous Validation

BigID
10/12/2026
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risk became very real, perhaps when unexpected exposure or compliance gaps were discovered. Yeah, absolutely. As part of that investigation and data modeling and threat, you know, threat modeling exercise we go through, a lot of times, you know, before we bring in the acquisition, we've already sort of discovered any sort of gaps in configuration or any drifts in how the configuration should be. And so our goal is to always sort of prevent an acquisition from completing without closing any of those gaps or holes, Brad. But we've had a couple of situations after an acquisition, you know, like a year or so after an acquisition where the, you know, we prevented exposure by detecting a tokenization issue. As an example, I'll bring up just recently we had one of our acquisitions that's a very critical acquisition for Fiserv. They tokenized the data and the tokenization process had stopped working and they did not detect that it had stopped working. And so, you know, thankfully we had BigID in place to discover that there was data not tokenized sitting in a data share in Snowflake that we were about to share and we were able to use our detective controls and preventative controls to block that data from being shared in real time. So definitely important to know your data. I know that's a phrase that people like to use in the industry, but it is important to know your data and know if that data is still following the processes you've set up because those processes can change, especially today with, you know, with the fast-paced movement we have in the industry, a lot of configuration changes are being done by your providers and by your solutions. And you have to, you know, kind of detect those changes and understand if there's anything that has changed in that environment that you need to then take action to reconfigure back into the right configuration policies.

TL;DR

  • Continuous data validation is essential in M&A scenarios because security controls can silently fail after acquisition, leaving sensitive data exposed without detection by traditional monitoring systems.
  • A Fiserv acquisition experienced a tokenization failure that went undetected internally, but BigID discovered untokenized sensitive data in a Snowflake share before it could be exposed to external parties.
  • Configuration drift is accelerated in modern cloud environments where providers make frequent changes, requiring ongoing data discovery and policy validation rather than one-time due diligence assessments.

Summary

This brief discussion examines the critical role of continuous data validation in preventing security exposures during and after mergers and acquisitions. Ken, a Fiserv representative, shares a real-world incident where a tokenization process silently failed at an acquired company, leaving sensitive data unprotected in a Snowflake data share. The failure went undetected by the acquisition's internal systems, but BigID's data discovery and classification capabilities identified the exposed data before it could be shared externally. The conversation emphasizes that M&A due diligence must extend beyond initial assessment to include ongoing monitoring of data protection controls, as configuration drift and silent failures are common in fast-moving cloud environments where providers frequently update systems. The case illustrates how detective and preventative controls working together can catch security gaps that traditional monitoring misses, particularly in complex post-acquisition integration scenarios where data governance processes may degrade over time.

Chapters

0:00 - M&A Data Risk Discovery
0:44 - Tokenization Failure Case Study
1:31 - Continuous Validation Imperative

Key Quotes

0:56 "... we prevented exposure by detecting a tokenization issue ..."
1:08 "... the tokenization process had stopped working and they did not detect that it had stopped working ..."
1:31 "... it is important to know your data and know if that data is still following the processes you've set up because those processes can change ..."

FAQ

Why is continuous monitoring necessary after M&A integration is complete?

Data protection processes like tokenization can silently fail without triggering alerts in traditional monitoring systems. Configuration drift accelerates in cloud environments where providers make frequent updates, so ongoing validation ensures security controls remain effective long after the acquisition closes.

How did BigID prevent the data exposure in the Fiserv example?

BigID's data discovery capabilities identified that sensitive data in a Snowflake data share was not tokenized as expected. The platform's detective controls flagged the exposure, and preventative controls blocked the data from being shared externally in real time, preventing a potential breach.


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