Prevent Data Leakage
The potential for leaks in the data pipeline exists when users with different permissions are producing new tables or transformations across the data platform. Immuta’s fine-grained access control and patented Projects capability dynamically enforce equalized permissions across users, thereby protecting data platforms from leakage.
Secure Data Access Across the Data Mesh
The diversity of stakeholders in the data mesh journey can create bottlenecks around managing distributed data access control policies. Immuta provides self-service data access with automated, dynamic access control that can be applied consistently across the data mesh, and easily audited to monitor data access and use across domains.
Enable Zero Trust
Zero Trust architectures offer a secure solution to risks posed by remote or distributed work environments and cloud infrastructure adoption, but can in turn slow the access to and insights from important data. Immuta’s automated data access controls enable self-service data analytics at scale, without sacrificing data security and privacy.
Immuta is a great tool for managing access to our data warehouse. We like being able to configure access to tables in a very granular manner. User management via groups is easy and intuitive. Auto-detection of sensitive data saves a lot of time for our data stewards.
Igor Chtivelband VP of Data at Billie.io
What are the most common types of data security?
There are many approaches to protecting data, but the most common types of data security are data access control, data masking, encryption, identity access management and authentication, data backup and resilience, and data erasure. The strongest strategies will use a combination of these types of data security to ensure that data remains protected from threats such as data loss or leaks.
What are the core data security standards?
The core data security standards are confidentiality, integrity, and availability, also referred to by the acronym CIA. These three elements are central to ensuring your data remains protected without sacrificing data quality or accessibility.
How are data security controls used in practice?
Data security controls are used to protect data ecosystems and the sensitive data within them from inadvertent or unauthorized exposure. These types of controls are meant to help data teams understand what sensitive data is in their networks, implement policies to ensure only the right people can access it, and monitor data use across all cloud databases to detect and respond to anomalies and potential risks before they become too serious. In practice, data security controls are best implemented through a tool that enables data discovery and classification, dynamic access control and data masking, automated policy enforcement, and data auditing and reporting.
What are the best practices for implementing cloud database security?
Cloud database security starts with an overall risk assessment and strategy for managing data access and security. Those involved in data management – from the leadership team to engineers and GRC stakeholders – should provide input on the strategy. As organizations adopt multiple cloud data platforms, data architects should incorporate a data access control and security solution that can provide consistent policy enforcement by separating policies from individual cloud databases. This will provide the best approach for ensuring security across the entire ecosystem. Finally, that same solution should provide the ability to automate fine-grained access controls that will allow teams to scale cloud database security and stay ahead of the compliance curve.
What is row-level security?
Row-level security is an approach to data access control that permits or restricts visibility to specific database table rows. If using an attribute-based access control (ABAC) approach, which rows a user is able to see is based on a combination of user, object, environment, and/or contextual attributes. Row-level security is a more dynamic approach to protecting data than arbitrarily restricting the table’s entire contents because it still allows users with some restrictions to derive value and utility from the information they are permitted to see.
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