7 Hurdles to DSPM Accuracy – and Why It Matters More Than Ever

In Data Management And Security, ‘Close Enough’ Isn’t Safe Enough

In data security, precision isn’t a nice-to-have; it’s a prerequisite.

If your Data Security Posture Management (DSPM) platform doesn’t have visibility into all of your data cross-cloud and cross-datastore, can't tell the difference between a public document and a sensitive one, or between normal user behavior and a red flag, you're not just flying blind, you’re flying with a broken GPS.

Accuracy in DSPM is what gives meaning to all the automation, alerts, dashboards, and compliance claims. Without it, the rest is noise.

Here’s the reality: most DSPM tools struggle with accuracy in real-world, enterprise-scale environments. They’re good at classifying static data in perfect conditions, but fall short the moment your data landscape gets messy, unstructured, or dynamic (i.e., normal).

Let’s break down seven of the biggest hurdles to DSPM accuracy and what’s at stake if you don’t overcome them.

1. Disconnected Data = Disconnected Truth

Data sprawls across clouds, SaaS apps, file shares, and AI pipelines. And as data fragments, so does context. Many DSPM tools struggle to consistently track sensitive data across various environments. They might classify a file as sensitive in AWS, but fail to detect that it has been copied into Azure or used to train a model in a SaaS notebook.

When visibility is partial, accuracy is impossible.

An accurate DSPM must correlate data sensitivity, access, usage, and movement across all environments. That level of insight doesn’t come from point solutions or disconnected scans. Ideally, this comes from a centralized metadata lake. A metadata lake provides:

• A unified layer of enriched metadata from every source—cloud, SaaS, structured and unstructured

• Cross-environment lineage and propagation tracking (e.g., following a customer record from ingestion to exposure)

• continuous entitlements, usage history, and sensitivity overlays in one interface

Without this foundation, DSPM tools may offer snapshots, but never a cohesive view of risk. You’re left stitching together logs, exports, and alerts with no clear story.

Accuracy in modern DSPM is a byproduct of complete, contextual, and correlated metadata. And that only happens when all roads lead to a metadata lake, your single source of truth for sensitive data, regardless of where it resides.

2. Static Policies Can’t Keep Up

Some DSPM platforms expect you to write a mountain of policies and RegEx rules, and then hope nothing changes. But data environments are dynamic. Teams spin up new systems, share files across regions, and run AI models on datasets you didn't even know existed.

In this reality, static policy logic is the enemy of accuracy.

An accurate DSPM must leverage adaptive policy engines that:

• React to changes through metadata lake context

• Ingest continuous usage data

• Apply business-aware logic (e.g., “Flag if anyone outside Legal can access NDA files”)

When policy is static, your DSPM becomes outdated the moment your infrastructure changes. When policy is metadata-aware and adaptive, your DSPM evolves, automatically, with your business.

3. RegEx Is Not Context

Many DSPM solutions still rely heavily on pattern matching and regular expressions that look for strings resembling credit card numbers, Social Security numbers, or other classic PII markers. But the world has moved on. Your data is no longer cleanly formatted or labeled. It’s embedded in screenshots, codebases, AI inputs and outputs, Slack threads, or semi-structured data lakes.

A RegEx might catch a string of digits, but it can’t tell if that number is a test value, an actual SSN, or a product ID - a common scenario for synthetic test data for GenAI model training. It doesn’t understand intent. It doesn’t understand risk.

An accurate DSPM must go beyond pattern recognition and apply contextual analysis, utilizing AI to understand what the data is, how it is used, and how sensitive it is in your specific environment.

4. Sensitivity Isn’t Static

A DSPM platform might classify a file as “non-sensitive” today, but what happens when that file is updated with customer data tomorrow, or when it’s copied into a less secure environment? Or when the context around it changes?

Inaccurate classification occurs when DSPM tools fail to account for change, treating sensitivity as a fixed property rather than a dynamic and contextual one.

An accurate DSPM must continuously reevaluate data as it is accessed and as business conditions shift. This requires continuous metadata, lineage, and entitlements mapping, not periodic scans and static labels.

5. Anomalies ≠ Threats

Let’s say someone in finance downloads 1,000 files at midnight. Is that a policy violation or a batch reconciliation process? Context is everything. However, many DSPM solutions treat all anomalies uniformly, resulting in alert fatigue and wasted analyst time.

Accuracy in detection isn’t just about what’s happening, it’s about why it’s happening, where it is happening, the subject that is involved, and what the impact would be if it’s malicious.

An accurate DSPM must be able to tie policy adherence results back to data sensitivity, access norms, user roles, and even business cycles. A truly accurate DSPM doesn't just flag anomalies; it prioritizes real threats and compliance risks.

6. Every Business Has Unique Data

Pre-trained models and industry-standard data tags are a starting point, but they won’t help you protect internal roadmap documents, proprietary algorithms, or M&A strategy decks.

An accurate DSPM must be able to create custom data categories, define custom policies, and train models on what your organization considers sensitive, not just what the world does.

Without this flexibility, even the most technically advanced DSPM will misclassify what matters most to you and miss the chance to enforce meaningful security controls where they count.

7. You Can’t Fix What You Can’t Explain

This last point is deceptively simple: accuracy doesn’t matter if you can’t see why the system made a classification or raised an alert.

Many DSPM tools operate like black boxes, issuing alerts without clear justification. That forces analysts to reverse-engineer the logic, slows remediation, and ultimately erodes trust in the platform.

An accurate DSPM should offer explainability, a clear rationale for its findings, transparent scoring models, and traceability across datasets and user activity. That’s what separates actionable intelligence from unverified noise.

Final Thought: Precision Is Power

In an era where data is fuel for AI and currency for criminals being approximately correct isn’t good enough. You need to know precisely what’s at risk, who can touch it, and when something changes. That’s accuracy.

Without accuracy, your DSPM becomes another source of noise. Worse, it gives you a false sense of control while the real risks stay hidden behind false negatives or buried beneath false positives.

The platforms that get it right are using AI, metadata lakes, and policy-driven classification that adapts in continuous. They’re accurate because they’re context-aware, transparent, and continuously learning.

So the question isn’t just “do you have DSPM?” It’s: “Can you trust what it’s telling you?”

Check out our DSPM testing guide for more information on how best practices to evaluate a DSPM.