Zero Trust Security for Data Analytics in Federal Agencies
Federal programs prioritize data strategies and modernization, recognizing the potential of data and analytics to enhance decision-making. However, growing threats like ransomware attacks and espionage are intensified with the shift to the cloud and remote work. A Zero Trust architecture and centralized data security platform are crucial to protect data and systems, ensuring consistent and transparent access control across a complex data enterprise.
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Zero Trust Solutions for Federal Agencies
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Balancing Zero Trust and Data Analytics Modernization
Webinar
Balancing Zero Trust and Data Analytics Modernization
Webinar
Balancing Zero Trust and Data Analytics Modernization
Webinar
Balancing Zero Trust and Data Analytics Modernization
What 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.
What are the core Zero Trust principles?
Sensitive data is any data that needs to be protected against unauthorized access. If unintentionally exposed, sensitive data may have significant legal and/or ethical ramifications for both the data owner and the data subject. Data teams are compelled to protect sensitive data using data access solutions tools to appropriately permit or restrict data use, and to safeguard it from internal and external leaks and breaches.
How are Zero Trust and access control related?
The most well-known forms of sensitive data are personally identifiable information (PII) and protected health information (PHI), both of which are protected under HIPAA. However, sensitive data can also include confidential corporate information, credit card information, username and password combinations, attorney-client data, IP information, trade secrets, export-controlled research, and even personal calendars, among others. Data teams should work with legal and compliance teams to understand how to achieve compliance with federal regulations, as well as industry standards, employment clauses, and contractual agreements.
What should be included in a Zero Trust framework?
Data discovery and classification is a multi-step process aimed at providing a more detailed understanding of user data. Data discovery tools assess the data environment and identify data source locations. Next, the data is classified, using predefined parameters to identify and label certain data types that reside in these sources. Immuta’s sensitive data discovery feature automatically assesses incoming data, classifying sensitive data in columns as tags.