The real-time imperative: why periodic scans create security gaps

There's a window in every periodic DSPM scan.

It starts when one scan finishes. It ends when the next scan begins. During that window, you're blind.

For most DSPM tools running daily scans, that window is 24 hours. For weekly scans, it's 168 hours. During those hours, anything can happen. Anything can change. Anything can be exploited.

Attackers know about this window. They count on it.

A 24/7 Data Defense Engineer eliminates the window entirely. There's no "between scans." There's only continuous observation.

The DSPM gap problem

Let's visualize what happens in a typical 24-hour DSPM cycle.

Hour 0: DSPM scan completes. Your DSPM tool finishes its crawl. You have a fresh snapshot. All the metrics look accurate as of this moment.
Hour 4: First changes. Engineering pushes overnight deployments. Configuration changes propagate. New data flows activate. Your DSPM snapshot is already outdated.
Hour 8: Morning activity. Business operations begin. Users log in. Data moves. APIs process requests. Your DSPM captures none of this activity.
Hour 12: Peak operations. Maximum data movement. Pipelines running. AI models training. Third-party integrations transferring data. Your DSPM is half a day old.
Hour 16: Afternoon changes. More deployments. More configuration updates. Maybe someone adds a new SaaS integration. Still not in your DSPM.
Hour 20: Evening wind-down. Batch jobs kick off. Data warehouses refresh. ETL pipelines run. Background processes that won't appear until tomorrow's DSPM scan.
Hour 24: Next DSPM scan begins. Finally, you'll see what changed. But you're already looking at history.

Every hour in that DSPM cycle represents a gap. Every gap represents risk.

What lives in the DSPM gaps

Here are real scenarios that exist between DSPM scans:

Temporary exposures. A misconfiguration exposes data for 6 hours before someone notices and fixes it. The DSPM before the exposure: clean. The DSPM after the fix: clean. The exposure: invisible.
Transient data. Processing creates temporary tables with unmasked PII. They exist for 2 hours, get processed, and are deleted. They never appear in a DSPM scan. But they were there. Someone could have accessed them.
Short-lived credentials. API keys committed to repos, then quickly rotated. Tokens exposed and revoked. The window of exposure might be minutes or hours. DSPM sees none of it.
Burst data flows. Sudden spikes in data transfer to unusual destinations. Data exfiltration that completes between DSPM scans. Gone before the next crawl.
Shadow connections. An employee connects a shadow AI tool on Friday, uses it over the weekend, and disconnects Monday morning. Weekly DSPM never sees it.

Point-in-time DSPM isn't designed to catch these scenarios. It's designed to catalog what exists at scan time. The gaps are architectural, not operational.

The acceleration problem

The DSPM gap problem is getting worse, not better.

Faster deployments. CI/CD pipelines push changes continuously. Daily deployments are common. Some organizations deploy hundreds of times per day. Each deployment can introduce data security changes that DSPM misses.
More integrations. The average enterprise uses 975 cloud applications. Many integrate with each other. Many handle sensitive data. The integration surface grows constantly.
AI proliferation. AI adoption is accelerating. More models, more training pipelines, more inference endpoints, more vector databases. Each represents new data flows that DSPM wasn't built to track.
Real-time data. Modern applications process streaming data. Event-driven architectures. Real-time analytics. The data never sits still long enough for periodic DSPM to catch it.

The gap between what DSPM sees and what actually happens grows wider every year. Faster DSPM scanning doesn't solve it. Hourly DSPM instead of daily DSPM just creates smaller gaps. The fundamental problem remains.

Why continuous beats frequent DSPM

"We'll just run DSPM more often" is the typical response to the gap problem.

It doesn't work. Here's why.

Cost scales linearly. More frequent DSPM scans mean more compute, more API calls, more processing. Costs increase proportionally. Hourly DSPM costs 24x daily DSPM.
Coverage stays incomplete. Even hourly DSPM has 60-minute gaps. Reducing gaps requires exponentially more resources. You can never reach true continuous coverage by running DSPM more.
Performance impact. Frequent DSPM loads your systems. Database queries, API calls, network traffic. Production systems feel the impact of aggressive DSPM schedules.
Diminishing returns. Going from daily to hourly DSPM catches issues 24x faster. Going from hourly to every minute costs 60x more but doesn't proportionally reduce risk.

Continuous monitoring takes a different approach. Instead of crawling periodically, it observes constantly.

Data Journeys™ don't scan for data flows. They watch data flows happen. They're instrumented into your environment, not running against it.

The result is zero-gap visibility. No windows. No blind spots. No DSPM cost curves to manage.

The business case for real-time DSPM

The argument for real-time isn't just technical. It's economical.

Incident cost reduction. The average data breach costs $4.45 million. Faster detection reduces that cost by $1.5 million on average. Real-time detection is the fastest possible detection.
Compliance efficiency. Point-in-time DSPM requires extensive documentation to prove what happened between scans. Continuous monitoring provides complete evidence automatically. Audits become simpler.
Team productivity. DSPM-based security requires analysts to investigate what happened during gaps. Continuous monitoring shows exactly what happened. Your team gets full context immediately, reducing investigation time from hours to minutes.
Coverage at scale. As your data footprint grows, DSPM costs grow proportionally. Continuous monitoring through Data Journeys™ scales more efficiently because it observes rather than crawls.

Making the transition

Moving from periodic DSPM to continuous doesn't require a big bang migration.

Start with critical flows. Identify your highest-risk data movements. Customer PII to third parties. Sensitive data into AI systems. Financial records crossing boundaries. Deploy continuous monitoring here first.
Layer, don't replace. Keep your existing DSPM for compliance snapshots and periodic validation. Add continuous monitoring for real-time security. They complement each other.
Measure the gap. Track what continuous monitoring catches that DSPM misses. The delta justifies the investment and guides expansion.
Expand systematically. Once critical flows are covered, expand to adjacent systems. More data types. More integrations. More coverage.

The goal is eliminating DSPM gaps entirely. A 24/7 Data Defense Engineer that sees everything, continuously, without blind spots. Your team gets the visibility and context they need to protect your data around the clock.

Your attackers don't wait for your DSPM schedule. Your security can't afford to either.