In the alphabet soup of cloud security tools, few acronyms have risen faster—or with more promise—than data security posture management (DSPM). Touted as a cure-all for sensitive data exposure, DSPM promises deep visibility into cloud environments.
But as many CISOs have found, visibility alone isn’t enough.
DSPM might show you where the problems are—but it rarely solves them. And in fast-moving, cloud-first environments where data is constantly created, shared, and reshaped, simply knowing the problem isn’t enough. Without the ability to act instantly, visibility alone can leave your data exposed.
That’s where runtime data security comes in. While DSPM focuses on discovery and analysis, runtime data security focuses on action: monitoring data in real time, automatically remediating risks, and helping employees course-correct in the moment. It builds on what DSPM started, but adds the capabilities today’s security teams vitally need.
Here, we’ll break down the differences between the two in-depth.
What is DSPM?
DSPM is a category of tools designed to help organizations understand where their sensitive data lives, how it’s being handled, and where it’s most at risk. These platforms continuously scan cloud environments to locate sensitive data, evaluate its exposure, and flag any misalignments with internal security policies or regulatory frameworks.
At their core, DSPM solutions revolve around four primary functions:
- Data discovery: DSPM continuously scans cloud environments to identify sensitive data across structured and unstructured sources. This gives security teams visibility into where data resides, including assets that may have been overlooked or forgotten.
- Data classification: Once discovered, data is sorted by type and sensitivity. DSPM determines what kind of information is involved (e.g. PII, trade secrets, confidential documents), who should have access to it, how it’s being used, and whether it’s subject to regulatory requirements such as GDPR, HIPAA, PCI DSS, or CCPA.
- Risk assessment and prioritization: DSPM highlights the most critical vulnerabilities tied to each data asset. Common issues include misconfigurations, over-entitlements, data lineage issues and policy violations.
- Remediation and prevention: Finally, DSPM tools surface prioritized issues via dashboards and reports, often with guided remediation steps or incident response playbooks. The goal is to help security teams take action on the highest-risk problems first and reduce the likelihood of recurrence.
In theory, DSPM offers a streamlined, automated way to bring clarity and control to an organization’s sprawling data estate. But while these tools can improve visibility and surface key risks, they aren’t designed to fix the systemic issues that make sensitive data vulnerable in the first place.
The downsides of DSPM
While DSPM tools have become essential for locating sensitive data and exposing the risks that surround it, they are far from a silver bullet for CISOs and their teams. Why? Because DSPM is fundamentally passive. They identify issues but don’t resolve them. That responsibility still falls on cybersecurity teams—teams that are already time-poor and overburdened.
The data speaks volumes. Over 70% of SOC analysts report burnout, with 66% saying that at least half their workload could—and should—be automated. DSPM, for all its promise, lands squarely in that repetitive, time-consuming category. In cloud-native environments where data never stands still, the job of tracking, triaging, and fixing every vulnerability manually is unsustainable—and unscalable.
And when the pressure is high and alerts keep coming, mistakes happen. Even experienced analysts can miss risks when they’re overwhelmed by competing priorities. Manual remediation at this scale creates more room for human error, not less.
Worse still, DSPM doesn’t tackle the root cause: the human factor. These tools offer no mechanism for employees to self-correct or learn from missteps. They operate in isolation from the people creating and handling the data every day. Without that feedback loop, the same issues resurface again and again. Analysts end up firefighting, while the root causes go untouched.
In essence, while DSPM offers visibility, it leaves critical gaps. Security teams need tools that do more than simply spot problems. They need ones that help prevent and remediate them—at scale, and at the source.
Enter: runtime data security.
What is runtime data security?
Runtime data security builds on the foundations of DSPM but moves several steps further. It blends the strengths of DSPM, next-generation data loss prevention (DLP), and human risk management into a single, cohesive solution. The result is real-time, context-aware protection that identifies and remediates risks in real time.
At a glance, runtime data security starts in familiar territory. It scans cloud environments for sensitive data, classifies it by type and regulatory context, and identifies potential misconfigurations or policy violations. But where DSPM stops at surfacing issues, runtime security is built to act on them.
That’s because runtime data security platforms are designed not just to observe, but to intervene. They introduce automation, behavior-driven insights, and human risk management into the security workflow—transforming how data risk is identified, mitigated, and ultimately reduced.
Runtime data security: Step-by-step
Here’s how runtime data security works in practice:
- Data discovery: Like DSPM, runtime data security platforms continuously scan cloud environments to locate sensitive data. This includes structured and unstructured data spread across SaaS applications, cloud storage, collaboration platforms, and developer tools. The aim is to give security teams a comprehensive and up-to-date picture of where their most valuable data lives.
- Data classification: Once data is discovered, it’s classified by sensitivity, business context, and regulatory exposure. The platform identifies whether the information qualifies as personally identifiable information (PII), intellectual property, or falls under regulatory frameworks like GDPR, HIPAA, PCI DSS, or CCPA. It also evaluates how the data is accessed, who has permission to use it, and whether that usage aligns with internal policies.
- Real-time monitoring: This is where runtime data security breaks from the DSPM model. Instead of scanning periodically and reporting after the fact, these platforms continuously monitor sensitive data in motion. They flag potential violations as they occur—whether it’s data being shared with the wrong party, copied to an unauthorized location, or mishandled in a way that violates policy.
- Automated remediation: Rather than relying on manual intervention for every alert, runtime platforms apply corrective actions automatically. This might be revoking an inappropriate permission, quarantining a file, or restricting access based on user behavior. All of this remediation happens in real time—without draining team capacity or delaying resolution.
- Human risk management: Crucially, runtime data security doesn’t treat people as an afterthought—it brings them into the process. When a violation occurs, the platform engages users in the moment with contextual guidance, nudging them toward safer behavior and helping them understand what went wrong. Over time, this helps build a more risk-aware culture where employees are part of the solution, not just the source of the problem.
Secure your data at speed and scale
Getting visibility into your sensitive data shouldn’t take weeks. And acting on that visibility shouldn’t mean piling more work onto already overstretched teams. That’s where runtime data security changes the equation.
Unlike DSPM platforms—with their long onboarding cycles and complex integrations—runtime data security gets you up and running fast. Polymer’s platform can be installed in under 10 minutes, with no code, no agents, and no disruption to your environment. Within half an hour, you’ll move from invisible data sprawl to real-time, granular data protection.
Ready to shift from visibility to action? See how Polymer helps you secure sensitive data—at scale, in real time, and without complexity.
