Data security posture management (DSPM) vendors provide visibility into where sensitive data is, who has access to it, and how it has been used.
Sentra is a DSPM platform with data access governance (DAG), and data detection and response (DDR) capabilities, that can help you track sensitive data across the cloud. With recent investments from Munich Re Ventures, Sentra aims to assist enterprises in identifying and addressing data issues.
We’ve prepared a comparison with 5 leading Sentra alternatives, a comprehensive overview of Sentra’s capabilities, and an in-depth user review analysis.
Sentra & its top 5 alternatives
Features
Vendors with:
- Automated remediation: Respond to data-related security threats in real-time, these tools can:
- Remove excessive permissions
- Fix risky misconfigurations
- Apply labels and enforce DLP policies
- Agentless scanning: Leverage the cloud to scan workloads and systems without installing an agent on every device in your network.
- High number of patterns and rules (based on top regulations like HIPAA, SOX, PCI GDPR) offer more flexibility for identifying sensitive data and other important secrets like API keys and tokens.
Threat intelligence support
Vendors with threat intelligence support offer integrated, monitoring and management of potential threats to an organization’s data.
Sentra overview
Sentra enables security teams to automatically identify sensitive data, assess risk, and remediate threats to all data stores and assets across numerous cloud, IaaS, PaaS, and on-premises environments.
It tracks data by discovering data in your public cloud environment, including shadow data, and prioritizing alerts based on sensitivity. Sentra detects unauthorized data movements such as:
- Data copied across cloud data repositories.
- Data processed by pipelines, ETLs, database migrations, or backups.
- Data moved between regions, environments, and accounts.
Why we like it:
- Sentra uses LLMs (BERT ML architecture) to classify data, it can classify unstructured documents, audio and video objects, and user-generated material. Sentra uses LLMs for both named entity recognition (NER), labels each word or sentence in the text with its correct entity, and document labeling tasks. Furthermore, Sentra’s data sensitivity evaluation (which drives classification definition) can be based on detected data classes, document labels, and detection volumes.
This LLM and data sensitivity classification capability enables the platform to deliver accurate data insights, tailored to specific business contexts. The company claims its product can achieve > 95% data classification accuracy.
- The solution enables you to use custom classifiers and sensitivity labels to gain context for your data. It provides detailed labeling through data sensitivity, lineage, environment, and data type (source code, structured DB table, web pages, synthetic data, and unstructured files).
This helps visualize data as it flows from data sources to consumption, allowing for precise classification, threat prioritization, and remediation.
Room for improvement:
- Sentra does not include data stored by Azure Synapse Analytics. It provides some visibility (above) into Amazon Web Services, Google Cloud, and Microsoft Azure’s cloud data repositories. However, this does not imply that they cover all the data-related services cloud providers offer.
Choose Sentra for data security posture management.
Real-life examples:
The solution is commonly used by financial services, healthcare, and retail companies, here are real-life examples from companies using Sentra:
SoFi uses Sentra to detect specific policy violations:
SoFi uses Sentra to identify PCI-DSS compliance issues where classified information like account numbers is publicly accessible or stored outside a PCI-compliant environment.
Sentra helped Global-e reduce cloud data risks
Sentra helped Global-e reduce cloud data risks and storage costs in AWS. Global-e gained a clear and prioritized view of their sensitive data assets and exposures, and the ability to drill down into specific issues. Tangible benefits include:
- 80% to more than 95% compliance with CIS, NIST SP 800, ISO 27001, PCI-DSS, CCPA, and GDPR regulatory frameworks.
- Cleaning up of shadow data reduced data storage by up to 20%, saving operational costs.
Coverage
Sentra offers broad coverage across IaaS, PaaS, SaaS, and on-prem environments. This helps security and IT teams gain visibility into their data stores, limit shadow data, and adhere to compliance.
Microsoft ecosystem:
- Cloud Services: Azure
- Collaboration Tools: Microsoft 365, Teams
- File storage: OneDrive, SharePoint
Amazon AWS ecosystem
- Cloud services: Amazon AWS, EC2
- Storage solutions: S3
Google ecosystem
- Cloud services: Google Cloud Storage
- Database solutions: BigQuery, Cloud Bigtable, Cloud SQL, Cloud Spanner
- Data processing: Dataflow
Data warehousing and analytics:
- Data warehouses: Snowflake, Databricks, Amazon Redshift
- NoSQL Database: MongoDB Atlas
Pricing
Senta offers annual subscriptions, which typically start at $50,000 and can go up to six figures per year. Sentra’s pricing is per TB scanned, not per user.
Product offerings
Sentra’s product offers 3 major capabilities:
1. Data security posture management (DSPM)
Sentra offers proactive approaches to mitigate your data security risks such as:
- misconfigurations
- mislocated data
- sensitive data exposures
- Data duplication
- compliance violations
Sentra DSPM uses machine learning to identify complex data types like intellectual property and unique customer records in unstructured databases.
Sentra includes LLMs as part of the data attack surface and applies the same data discovery/classification, and posture management capabilities to any training data used in these applications.
Additionally, Sentra can enrich your data loss prevention (DLP) solutions like Microsoft Purview Data Loss Prevention. by integrating sensitive data findings into cloud security events.
2. Data access governance (DAG)
Sentra Data Access Governance (DAG) monitors and enforces the necessary access permissions for all user identities, third parties, and applications. With Sentra’s DAG capabilities, you can enforce least privilege access controls.
For example, you can review the connection between cloud identities, roles, permissions, data assets, and sensitive data classes to examine which cloud identities and data stores are overprivileged.
Sentra can monitor data perimeters and movement. This helps reduce the gap between what data users and applications may and should access, using the least privileges and a zero trust approach.
Additionally, Sentra DAG can track access behavior across your data assets to detect anomalies. It then sends alerts to inform you about any unexpected or suspicious activity (e.g., inactive or unused identities and API keys, and provisioning/de-provisioning) that could lead to a breach.
3. Data detection and response (DDR)
DDR, alongside DSPM, complements the missing data-centric context by
- Differentiating security events with high-risk sensitive data
- Connecting high-risk sensitive data sources to potential threats.
- Prioritizing risk according to data security posture and context.
- Automated risk and threat prioritization.
Sentra’s data detection & response (DDR) capabilities monitor log and change activities. This helps identify breaches, data exfiltration, posture and permission changes, and unusual data transfers.
This helps identify data-centric threats that might go unnoticed by other monitoring tools such as XDR/EDR/CDR. A few examples include:
- Personnel downloading sensitive data they do not normally access.
- A ransomware that attacks critical business data.
- Users or applications gain access to sensitive data via privilege escalation.
- A third-party application manipulated an LLM training dataset.
Sentra’s DDR detects obscure data threats, which are frequently executed by unsuspected authorized insiders. DDR automatically prioritizes the severity of suspicious activities based on contextual information such as location, user, data sensitivity, and so on, and then alerts you accordingly.
User reviews
User feedback indicates that Sentra delivers precise results (some users claim that they’ve achieved 97% data classification precision) with low false positives. This helps reduce unnecessary noise and helps users focus on actionable insights.
However, the platform has some limitations such as Limited SaaS support unexpected AI assistant responses, and manual remediation requirements. See key highlights of Sentra’s pros and cons:
Pros
Accurate data classification: Sentra provides low false positive rates, even with custom data categories. This ensures that users can rely on Sentra to correctly identify and prioritize sensitive data without receiving inaccurate alerts.
Source: Gartner
Automated data discovery and remediation suggestions: Provides clear suggestions to tackle data risks at scale.
Sentra effectively informs users about the steps needed to secure their data, such as fixing misconfigurations and implementing necessary changes to ensure compliance and security.
Furthermore, Sentra’s seamless integrations with SIEM and SOAR solutions enable users to quickly send these remediation recommendations to their existing tools.
Source: Gartner
Robust data discovery & classification: Intuitive and efficient for data discovery and access analysis, providing clear insights into sensitive data locations and risks.
Source: Gartner
Cons
AI assistant: The AI chatbot/copilot feature is not consistently accurate or useful.
Source: Gartner
Automated remediation suggestions: While Sentra identifies the necessary actions to address data vulnerabilities, the platform does not fully automate the remediation process, requiring users to implement the recommended changes themselves.
Source: Gartner
