Data Exposures |
Rich context helps automatically map real exposures, such as shadow data, to compliance frameworks. |
Alerts on many false positives due to missing context. Does not help to reduce the data attack surface. |
Flexible Policy Engine |
Allows users to easily create custom policies that match their security programs. |
Limited policies that do not allow to express security team requirements. |
Flexible Policy Engine |
Data-aware detection of suspicious data access/activity.
Stop data breaches in
the cloud.
Cloud DLP built for cloud-native attack.
|
Does not detect data-aware threats in data lakes and databases in IaaS and PaaS. |
Data Similarity and Perimeters |
Automatically detects when data moves and travels across designated data stores and data perimeters. |
Does not provide any detection when data is being duplicated or copied across zones. |
Secure and Responsible AI |
Extends protection to GenAI/LLM applications, for strong risk posture and secure training sets, prompts, and outputs. |
Many tools do not support AI services (within cloud providers). |
Time to Value |
Install quickly in minutes. Immediate value. |
9-12 Months to be fully operational in large environments. |
Scanning Architecture |
Agentless, continuous and autonomous data discovery across IaaS, PaaS, and SaaS.
Includes known and unknown data.
|
Limited discovery requires agents and network connectivity. Can not discover unknown (shadow) data. |
Low Operational Cost at Petabyte Scale |
100-1000x faster and more compute efficient. Scans dozens of petabytes in a week using advanced ML-based clustering and sampling.
|
Very expensive and compute intensive. Limited scalability. Scan costs are 10-100X higher compared to Sentra. |
Robust Scan Settings |
Highly customizable scans. Enterprise-ready. |
Too simplistic. No options to customize different scanning strategies. |
Automatic and Accurate |
Keeps pace with >95% accuracy in detecting PII, PCI, PHI, secrets and more.
AI-powered classification.
|
Manual. Relies on regular expressions and customer-specific rules which create many FPs. |
Context Rich |
Deep context on the data's business use, data residency, data security, and more. |
Missing critical context required by security, such as whether the data is real or synthetic. |