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Automatic Data Discovery and Classification
Immuta automatically scans data sources, detects sensitive data, and creates standardized tags across any connected platforms. Pre-built and customizable classifiers simplify the process and ensure that access controls are properly applied to the right data. Immuta also integrates with catalog metadata to streamline data discovery and eliminate manual, error-prone tasks.
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Scalable Purpose-Based Access Controls
To simplify compliance with zero trust principles, Immuta provides attribute- and purpose-based access controls that permit or restrict access based on how and why data will be used. These highly specific controls are easily scalable and adaptable, and with fewer rules to maintain there is less risk of human error.
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Dynamic Data Masking for Minimization
By dynamically implementing advanced data masking techniques, including k-anonymization and randomized response, at query time, Immuta makes it possible for data teams to tailor access authorization to a relevant set of data. This helps them achieve data minimization by limiting data use to only what is directly relevant and necessary and ensure a high level of confidentiality.
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Always-On Data Monitoring and Auditing
Immuta automatically monitors and logs all actions in your data platform to prove compliant data use and investigate incidents. By tracking policy changes, access requests, queries executed, and more, data governance and compliance teams can ensure that actions taken on data adhere to approved purposes and the zero trust security model.
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Trusted by World Leaders
Federal governments like the United States are stressing the importance of the zero trust architecture by mandating it across departments and agencies. With massive volumes of sensitive data and use cases, these governing bodies cannot let manual processes or weak systems put their data at risk. Implementing automation and dynamic controls helps remove the guesswork.
Learn MoreWhat is the zero trust methodology?
The zero trust methodology is based on the idea that no users should be implicitly trusted with access to data within a specific ecosystem. Instead, zero trust security models require continuous authentication and authorization of user access. Zero trust methodologies are increasingly relevant amid the broad shift to the cloud, as fewer organizations rely on traditional on-premises environments with defined network edges. Aligning to zero trust principles better enables secure data use across any type of data ecosystem, whether local, cloud-based, or hybrid, and reduces risks of data leaks or breaches.
Learn MoreWhat are the core Zero Trust principles?
The core zero trust principles comprise six pillars: identity, device, network, application, workload, and data.
- Identity is an attribute(s) of the data user, such as their title, department, or location.
- Device refers to technology assets that can connect to a network, including laptops and servers.
- Networks are mediums through which communications flow, such as the internet and internal messaging systems.
- Applications and workloads are the systems, programs, and services operating within the data ecosystem.
- Data refers to the assets that must be managed across devices, networks, and applications. This includes data at rest and in transit.
How are Zero Trust and access control related?
Access control is a key tenet of zero trust security models because it helps data and compliance teams ensure that the right data is accessed by only the right people at the right time. Attribute-based access control (ABAC) is best suited for enabling zero trust architectures because it makes dynamic access decisions at query time based on qualities about the user, object, environment, and intended action. Role-based access control (RBAC), on the other hand, singularly ties access decisions to a user’s role, which runs the risk of permitting data access too broadly instead of continuously authenticating it.
Learn MoreWhat should be included in a Zero Trust framework?
Zero trust frameworks should give organizations a full view into what data they have, where it lives, how it is being used, and by whom. Such a framework will provide the ability to easily discover, tag, and classify sensitive data as it enters the network; automatically enforce dynamic access controls and masking techniques to secure data at scale; and continuously monitor data to prove compliance and/or investigate incidents in real time. In essence, zero trust frameworks enable data teams to put the “never trust, always verify” ethos into practice while seamlessly integrating with existing systems and processes.
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