Dasera's Newest Features

Deep Privilege Analysis for Oracle Data Stores

Dasera now supports Deep Privilege Analysis for Oracle data stores, expanding visibility into who has access to what data. Object-level privilege analysis shows the type of sensitive data accessible to which user, providing critical insights into who can access PII, credit card info, passwords, or other sensitive data types. Advanced data context via Deep Privilege Analysis enables a deeper understanding of your organization's overall security posture.

Classification Engine Unification

Dasera's classification engine now functions uniformly across structured and unstructured data stores. Built-in sensitive data types are used to classify data across all data stores using a reliable combination of indicators, such as keywords, field or file names, dictionaries, proximity, query logs, regex, and/or checksums. The Custom Sensitive Data Type UI has also been simplified to reflect unified match conditions for structured and unstructured data.

Low-Touch Onboarding for GCP Cloud SQL Data Stores

Dasera now supports snapshot-based data classification for all GCP Cloud SQL data stores, eliminating the need to create Dasera-specific credentials for each data store. This extends our existing capabilities for snapshot-based data classification for AWS data stores. You can now connect almost all supported data stores in a low-touch way, simplifying the onboarding process with zero performance impact. This streamlined process enables comprehensive risk analysis without disrupting operational efficiency.

Support for Azure Blob Storage and Azure Files

Dasera now supports discovery and classification for Azure Blob Storage and Azure Files. Azure Blob Storage typically stores large volumes of unstructured data, such as media files, backups, and logs, while Azure Files allows for file sharing across servers and platforms with a more traditional directory structure. Expanded coverage for unstructured data provides enhanced visibility into all sensitive data across environments.

Saved Views for Data Store Inventory Page

Dasera now offers saved views on the Data Store Inventory page to help you better manage and monitor your data stores. Adjust filters, columns, and sorting, then save your view. Each saved view appears as an additional tab to the right of the Default View. You can also adjust your preferred view, which will appear on this screen anytime you return to it. All views here only apply on a per-user basis, and no user will see another user's saved views.

New Built-in Sensitive Data Types for Credentials & Keys

Expansion to SaaS Application with M365 coverage

Dasera has expanded its capabilities to safeguard sensitive information across structured, unstructured, and SaaS applications, marking a significant milestone with the introduction of Microsoft 365. Organizations can comprehensively look into their data across OneDrive, SharePoint, and Teams, offering unparalleled visibility into potential risks. The addition of Microsoft 365 drives efficient data discovery and management, streamlining privacy processes through our robust policy engine.

Get your Rapid Data Risk Assessment with Snapshots

Quickly pinpoints the identification and securing of sensitive information within AWS RDS data stores. This non-intrusive method is quickly deployed and ensures comprehensive security assessments without impacting performance or privacy policies. Snapshots empower organizations to swiftly understand and mitigate potential threats, bolstering data security seamlessly.

Ingesting Context from Anywhere

Enriching your metadata with business and compliance context remains a crucial principle of data governance. This is often achieved with data tags, enabling the accurate enforcement of your privacy and compliance policies. In addition to ingesting tags from both Google Cloud Platform (GCP) and Snowflake (an existing Dasera capability), you can now ingest AWS tags. Dasera will automatically import all associated tags when connecting any AWS data store. This metadata augments the classification and auto-tagging already performed by Dasera, facilitating more precise alerting and remediation.