If you're evaluating data security solutions, you've probably seen demos from Cyera, Securiti, and BigID. They're impressive demos. Fast classification. Clean dashboards. Lots of integrations. But here's what the demos don't show you: all three share the same fundamental limitation.
They're scanners.
They crawl your data stores, classify what they find, and generate alerts. They take snapshots of data at rest. They miss data in motion.
Understanding this shared limitation helps you evaluate data security solutions more effectively. It's not about which scanner is fastest. It's about whether scanning is enough.
The scanner generation
Cyera, Securiti, and BigID emerged from the same era with the same approach to data security.
Cyera focuses on speed. Fast deployment, fast scanning, fast classification. They've optimized the scanner model for efficiency. But it's still a scanner.
Securiti focuses on breadth. Privacy automation, AI governance features, LLM firewalls. They've expanded what their scanner does. But the core architecture remains scanner-based.
BigID focuses on depth. 1,500+ classifiers, enterprise scale, hybrid deployment. They've made their scanner more comprehensive. But scanning is still the foundation.
Each has strengths within the scanner paradigm. None transcends it.
What scanners see (and miss)
All scanner-based data security solutions share the same visibility model:
They see: Data at rest in databases, object storage, data warehouses, and file systems. They connect, crawl, classify, and catalog.
They miss: Data in motion through APIs, microservices, pipelines, and integrations. Data flowing to third parties. Data entering AI systems. Data transforming in real time.
Research suggests 65% of data security risks involve data in motion. Scanner-based data security solutions see the 35% at rest.
This isn't a knock on Cyera, Securiti, or BigID specifically. It's a limitation of the scanner architecture they all share.
Cyera: the fast scanner
Cyera's pitch is speed. Quick deployment, rapid scanning, immediate visibility. They've optimized well within the scanner model. Their classification accuracy is solid. Their deployment is genuinely fast.
But speed doesn't solve the architectural limitation.
What Cyera sees: Your S3 buckets, databases, and warehouses. Sensitive data at rest across cloud environments.
What Cyera misses: The API call sending that data to a third-party analytics service. The code commit that introduced a new data flow. The AI model training on production data. The real-time pipeline transforming records.
Cyera is a fast scanner. It's still a scanner.
Securiti: the broad scanner
Securiti's pitch is breadth. Privacy automation, AI governance, data security in one platform. They've expanded the scanner model to cover more use cases. Their privacy features are legitimate. Their AI governance capabilities exist.
But breadth on top of scanning still means scanning.
What Securiti sees: Data at rest across your environment. Privacy obligations mapped to data stores. AI models as static assets.
What Securiti misses: Dynamic data flows that privacy depends on. AI data lineage showing what models actually consume. Real-time policy violations as they happen.
Securiti offers more features than a pure scanner. The foundation is still scanner-based.
BigID: the deep scanner
BigID's pitch is depth. More classifiers, more enterprise features, more deployment options. They've deepened the scanner model for enterprise requirements. Their classification library is extensive. Their hybrid deployment supports complex environments.
But deeper scanning is still scanning.
What BigID sees: Comprehensive classification across data stores. Detailed catalogs of sensitive data at rest. Enterprise-scale scanning capabilities.
What BigID misses: The same things all scanners miss. Data in motion. Real-time flows. AI data lifecycles. Third-party data sharing as it happens.
BigID is a thorough scanner. The architecture remains point-in-time.
Beyond the scanner generation
A 24/7 Data Defense Engineer takes a different approach.
Instead of scanning periodically, it observes continuously. Instead of cataloging data at rest, it tracks Data Journeys™ in motion. Instead of generating undifferentiated alerts, it prioritizes by actual risk and provides the context your team needs to respond effectively.
This isn't an incremental improvement over scanner-based data security solutions. It's architectural differentiation.
Choosing your approach
Scanner-based data security solutions have their place.
If you need compliance snapshots for audits, scanners work. If you need to catalog sensitive data at rest, scanners work. If your architecture is simple with minimal data movement, scanners might be sufficient.
But if you operate cloud-native architectures with constant data flows, scanners fall short. If you're adopting AI and need to govern data through training and inference, scanners weren't built for it. If you need continuous visibility rather than periodic snapshots, scanners can't deliver.
The question isn't which scanner is best. It's whether you need a scanner or a Data Defense Engineer.
The evaluation framework
When comparing data security solutions, ask these questions:
- Do you show data in motion or only at rest?
- Can you track data through AI systems?
- What happens between scans?
- Do you provide risk-prioritized findings with context, or just alerts?
- Can you see data in source code before deployment?
Cyera, Securiti, and BigID will answer these questions within the scanner paradigm. A Data Defense Engineer answers them from a fundamentally different architecture.
Know what you're buying. Know what you need.
