u/gosricom

Our org is a mid-size financial services company, hybrid environment, mix of on-prem file servers (NetApp NAS), SharePoint Online, and a handful of AWS S3 buckets that different teams have spun up over the years. We're heading into a PCI DSS audit in about 4 months and the auditors want, evidence of a formal sensitive data inventory, not just a network diagram and a promise.

The problem we ran into: we don't actually know where all the cardholder data is. We assumed it was contained to three known systems. Turns out, after a spot check, there are Excel files with PANs sitting in SharePoint libraries that, haven't been touched since 2021, and at least two S3 buckets where nobody's sure what's in them anymore. Classic sprawl situation.

We tried to scope this manually first. Two people, three weeks, partial coverage of maybe 30% of the file shares. Not sustainable and still left the cloud storage completely unaddressed.

We ended up running Netwrix Data Discovery & Classification across the environment, which handled the hybrid scope really well, it covered the NAS and M365 in, the same pass rather than needing separate tools, and the incremental indexing meant we weren't hammering the file servers every time we needed a fresh scan. Took about two weeks to get a full picture, and it surfaced PAN data in locations we hadn't expected, including some Teams channel files. The fact that it ties discovery directly into risk reduction and audit evidence made it a, lot easier to build the case internally for doing this properly rather than just winging it.

Here's the specific question: once you have a classification run complete and you've identified, where the regulated data actually sits, what's your process for deciding what to remediate vs. what to just document and accept? We're debating whether to delete/move the stale SharePoint files outright or just apply tighter access controls and log it as a finding with compensating controls. The auditors haven't given clear guidance on which approach satisfies the intent of requirement 3.2 in this context. Has anyone navigated this with a QSA and gotten a definitive answer on what's acceptable?

The EU just hit Apple and Meta with massive DMA fines, Apple €500 million and Meta €200 million, handed down, April 23, 2025, for breaching DMA obligations, and most of the coverage is framing it as a competition or antitrust story. From a security and compliance standpoint, though, what stands out to me is the underlying data inventory problem these cases keep circling back to.

The specific violations here were anti-steering for Apple and a pay-or-consent ad model for Meta, but the broader compliance documentation challenge isn't unique to big tech. I've seen the same gap in mid-sized enterprises during audit prep. The org knows it has GDPR-scoped data somewhere in its M365 environment or on a, legacy file share, but the actual inventory is a mix of spreadsheets, tribal knowledge, and assumptions.

The enforcement trajectory here matters. DMA is a different instrument than GDPR, but the evidentiary expectations regulators are developing through these cases will bleed into how GDPR audits get conducted too. Demonstrating data minimization or legitimate processing basis is much harder when you can't even produce a current, accurate map of where regulated data sits. I've been evaluating a few classification tools for this kind of problem, including Netwrix Data Discovery & Classification, partly, because the access-context layer matters as much as the raw discovery output when you're trying to answer a regulator's question.

The part I keep thinking about: fines at this scale are still recoverable for Apple or Meta. For a smaller company, a significant fine as a proportion of revenue can be genuinely existential, DMA allows up to 10% of global turnover, which hits very differently depending on your size. And the documentation gap that left those companies exposed to enforcement is exactly the same, gap that exists in most enterprises, just with fewer lawyers in the room when it surfaces.

The Ticketmaster breach is getting covered mostly as a credential or cloud misconfiguration story, but the detail that keeps standing out to, me is the scale of what was apparently sitting in one place: names, addresses, payment data, and ticket history for 560 million customers. ShinyHunters listed the dataset at 1.3TB for sale on BreachForums before the confirmation even came out.

That volume suggests this wasn't a narrow exfiltration. Attackers had enough access to pull broadly across what sounds like a poorly scoped data store. Which raises the uncomfortable question of whether Ticketmaster's own security team had a clear, inventory of what sensitive data existed where, and who or what had access to it. In a lot of orgs that size, the honest answer is no. Payment data, PII, and behavioral data accumulate across environments over years and nobody does a full reconciliation.

This is where data discovery and classification tooling matters in a real operational sense, not just for compliance checkbox purposes. If you don't have continuous visibility into where regulated data is landing across your storage footprint, you can't scope your controls accurately, and you definitely can't scope your blast radius after an incident. Tools in this space range from cloud-native DSPM platforms to more identity-contextual approaches like Netwrix Data Discovery & Classification, which ties sensitivity findings to, access exposure rather than just flagging file types, and connects those findings directly to risk reduction and downstream controls like DLP and Copilot governance.

The entertainment sector has historically underinvested here compared to financial services or healthcare, partly because the regulatory pressure is lower. That calculation changes when you're handing over a breach notification to 560 million people.

Our org is a mid-size financial services company, hybrid environment, mix of on-prem file servers (NetApp NAS), SharePoint Online, and a handful of AWS S3 buckets that different teams have spun up over the years. We're heading into a PCI DSS audit in about 4 months and the auditors want, evidence of a formal sensitive data inventory, not just a network diagram and a promise.

The problem we ran into: we don't actually know where all the cardholder data is. We assumed it was contained to three known systems. Turns out, after a spot check, there are Excel files with PANs sitting in SharePoint libraries that, haven't been touched since 2021, and at least two S3 buckets where nobody's sure what's in them anymore. Classic sprawl situation.

We tried to scope this manually first. Two people, three weeks, partial coverage of maybe 30% of the file shares. Not sustainable and still left the cloud storage completely unaddressed.

We ended up running Netwrix Data Discovery & Classification across the environment, which handled the hybrid scope really well, it covered the NAS and M365 in, the same pass rather than needing separate tools, and the incremental indexing meant we weren't hammering the file servers every time we needed a fresh scan. Took about two weeks to get a full picture, and it surfaced PAN data in locations we hadn't expected, including some Teams channel files. The fact that it ties discovery directly into risk reduction and audit evidence made it a, lot easier to build the case internally for doing this properly rather than just winging it.

Here's the specific question: once you have a classification run complete and you've identified, where the regulated data actually sits, what's your process for deciding what to remediate vs. what to just document and accept? We're debating whether to delete/move the stale SharePoint files outright or just apply tighter access controls and log it as a finding with compensating controls. The auditors haven't given clear guidance on which approach satisfies the intent of requirement 3.2 in this context. Has anyone navigated this with a QSA and gotten a definitive answer on what's acceptable?