- Purpose-Based Access Control (PBAC)
- Attribute-Based Access Control (ABAC)
- Plain language data policies
- Distributed Stewardship
Author data policies as a team
Orchestrate and enforce data policies in real-time
- Fine-Grained Data Security (row-, column-, & cell-level)
- Dynamic Data Masking
- Policy Orchestration
- Transparent Policy Enforcement
- Secure Data Collaboration
- Access Request Workflows
- User Impersonation
Apply advanced privacy controls
- Privacy Enhancing Technologies (PETs)
What is considered sensitive personal information?
Sensitive personal information refers to any data about an individual that must be kept confidential and protected from unauthorized access. Two well-known categories of sensitive personal data are personally identifiable information (PII), like first and last names, email addresses, and credit card numbers, and protected health information (PHI), such as medical records, lab results, and medical bills. Other types of sensitive data also exist, including commercially sensitive data, like private company revenues, HR analytics, and IP, as well as classified information, like top secret, secret, and confidential data. Direct identifiers, like names, are often considered highly sensitive, but indirectly identifying attributes like hair color, height, and job title, can also be considered sensitive when combined with other data sets.
What does it mean when data is de-identified vs. anonymized?
Data anonymization is the process of totally adjusting or removing personally identifiable information (PII) from a dataset in order to protect the individual who created the data. An anonymized data set completely scrubs or encrypts this PII to prevent it from being linked back to a given individual. Data de-identification similarly detaches direct identifiers from PII to protect individuals through methods like pseudonymization and randomization. This is done, however, in a way that does not completely sever the data from being re-identified if need be.
What should I look for in a multi-cloud governance platform?
When evaluating a multi-cloud governance platform, teams should consider the following: financial capability, product vision, market share, and partner ecosystem. Beyond this, teams need to consider a range of functional requirements, including the level of support needed, various security needs, applicable regulatory requirements, and pricing structure. Ultimately, a multi-cloud governance platform should be able to apply policies and govern access to all data in a given ecosystem, regardless of which cloud platform it is stored or accessed in.
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