Let’s talk about data classification. We know, it sounds as exciting as filling out tax forms. But stay with us.
Seems like every week, another company makes the news for losing sensitive data. The reasons are numerous, but far too often, it’s due to internal users or gen AI sharing information they shouldn’t. A common excuse: “We didn’t know that file was confidential.” Which is a bit odd, because it was labeled “Confidential,” right after “Q3 Bonus Plan – Layoffs.xlsx.”
Data security teams are out there setting rules and writing policies, as if it’s going to stop Karen in Accounting from forwarding payroll data to her cousin for “just a quick look.” Sure, you’ve got tools that can label files, but they simply follow rules. They can’t understand context or spot the difference between a resume and a medical record unless you spell it out for them. In binary.
Depending on regex and end-user labeling to protect your organization is like giving a toddler a fire extinguisher and calling it a risk management plan.
Because if you want security that actually works, and not just something that looks good on a PowerPoint slide, you need tools that can see what your rules can’t.
What is automated data classification?
Automated data classification is the process of discovering, identifying, and labeling sensitive data without manual effort, custom rules, or endless tuning.
Instead of relying on humans to tag files (bad idea) or regex patterns that miss context (also a bad idea), automated classification uses AI to actually understand what the data is about.
It scans across cloud apps, emails, databases, and collaboration tools, figures out what’s sensitive (and what’s not), and applies the right labels at scale. All without begging your users to care or trusting them to get it right.
Labels don’t lie… unless they’re wrong
If your organization uses Microsoft Purview, you’re already ahead of the game. It’s powerful, integrated across your M365 stack, and essential for compliance and data governance. But there’s a catch: Purview is only as good as the labels it’s working with. And let’s be honest—those labels are often wrong, inconsistent, or missing entirely.
Regex-based rules miss nuance. Pre-built classifiers don’t understand your business. Trainable models take forever to build. As for end-user labeling, let’s not pretend people enjoy (or are willing) to pause their day to tag files with the right sensitivity level.
In finance, that means a folder full of M&A projections or loan application data may get the same label as a marketing deck. In healthcare, a PDF with PHI might fly under the radar because it wasn’t structured like an EHR export. When the labels are wrong, everything downstream—DLP, insider risk, audit reporting—starts from a flawed foundation.
The curious case for automated data discovery and classification
Before you can classify your data, you have to find it, and that’s where most traditional systems fall apart.
Automated data classification is powerful, but it’s only as good as the discovery engine behind it. Without a way to autonomously surface and categorize sensitive data—across silos, formats, and platforms—you’re applying classification to only a fraction of your risk.
Once sensitive data is autonomously discovered, automated data classification changes the game by doing what rules-based systems can’t: understanding data in context, without manual input or constant tuning.
Think of it as classification with common sense. The system knows that a résumé and a W-2 aren’t the same thing, even though they might look similar. It can tell the difference between an NDA and a master services agreement. It sees not just what the data says, but what it means.
In other words, it understands context.
And it applies that insight—automatically, consistently, and at scale.
So, what makes it “automated”?
It’s not just about speed (though it’s definitely so much faster). Automated classification powered by AI and natural language processing can:
- Scan unstructured and structured data across cloud apps, email, SharePoint, Teams, and beyond
- Identify sensitive content without needing custom regex or keywords
- Apply pre-mapped sensitivity labels like the ones used by Microsoft Purview
- Update classifications continuously as data changes hands or content evolves
For a healthcare provider, this means medical billing records shared over Teams chats can be correctly labeled and protected, even if no one manually flags them as PHI. For a financial institution, it means investor decks, regulatory filings, and private client communications are automatically recognized and secured without endless tuning.
Why classification fails without automation
If you’re still relying on manual or rule-based methods, it’s practically game over. Black screen. No Continue button.
Here’s what you’re up against:
Files without matches get ignored: If a file doesn’t fit a known pattern, it’s invisible.
Context gets lost: “Confidential” in a footer doesn’t make something sensitive. The content does.
End users are inconsistent: Some over-classify to be safe. Others under-classify to avoid friction.
You miss what matters: Like that spreadsheet with thousands of unencrypted SSNs sitting in a shared folder no one’s checked since 2022.
These failures are beyond annoying. They’re dangerous. Every misclassified file opens a door to exposure, compliance risk, or a headline-grabbing breach.
Automated classification with Concentric AI + Purview = Better together
What if Purview didn’t just enforce policies and it actually understood the data it’s protecting?
That’s what happens when you add Concentric AI’s Semantic Intelligence to the party. It acts as the brain Purview never had: applying accurate, context-aware classifications and directly attaching sensitivity labels Purview can act on. You get smart labels. Better DLP. Real insider threat detection.
And you never have to write a regex string again. Celebrate good times, y’all.
For a regional bank dealing with thousands of customer records daily, that means every file gets tagged the moment it’s created or modified—whether it’s a new loan application or a branch-level compliance report. For a health clinic, that means diagnostic reports, imaging results, and even scanned forms are identified, labeled, and protected without depending on staff to do it manually.
What you gain with automated data classification
Automated data classification transforms data security like when a superhero puts on their cape/costume/Iron Man Suit.
Here’s what actually improves when your classification isn’t duct-taped together:
Better DLP outcomes: No more false positives or missed threats. Just clear visibility into what’s sensitive and what’s not.
Stronger compliance: Labels drive reports, audits, and regulatory outcomes. Get them wrong, and you’re already behind.
Faster incident response: If a file leaks, you’ll know exactly what it contained—and how bad the damage is.
Less manual effort: Free your team from playing data detective or policy babysitter.
Consistent enforcement across tools: Apply the same logic across Teams, OneDrive, SharePoint, Exchange, and beyond.
Whether you’re preparing for a HIPAA audit or navigating SEC regulatory reporting, automated classification gives you a single, consistent source of truth for what (and where) your data is and how it should be treated.
Use cases that matter
If you think classification only matters in compliance audits, let’s expand that perception.
Here are a few more reasons why data classification is so important.
Detecting insider threats: Catch that analyst uploading classified product specs—or sensitive payer contracts—to a personal Dropbox.
Preventing data loss: Stop a former employee from walking off with unreleased earnings reports or raw clinical trial results.
Securing unstructured data: Auto-label documents containing patient names and birthdates shared in Teams chats or stored in SharePoint folders.
Safe collaboration: Let employees send financial planning documents or care coordination notes without putting regulated data at risk.
Reducing alert fatigue: Only get flagged when something actually goes wrong.
Automated data classification gives you the coverage and control to act with confidence, even when your data is everywhere.
Don’t wait for a breach to get smart
Here’s the blunt truth: most organizations don’t get serious about classification until something breaks. A leaked doc. A failed audit. A headline they wish they could take back.
Automated classification is a smarter way to manage your data every day. It makes everything downstream more effective: from DLP to insider risk to compliance. It gives your team clarity and control without extra work. And it keeps your most important data from falling through the cracks.
If you want better data security—whether you’re managing PHI, PCI, or PII—start where it all begins: with the labels. And if you want those labels to actually be right, you need automated data classification.
