Abstract: A system groups multiple entities in a large distributed data store (DDS), such as directories and files, into a subset called a domain. The domain is treated as a unit for defining policies to detect and treat sensitive data. Sensitive data can be defined by enterprise or industry. Treatment of sensitive data may include quarantining, masking, and encrypting, of the data or the entity containing the data. Data in a domain can be copied as a unit, with or without the same structure, and with transformations such as masking or encryption, into parts of the same DDS or to a different DDS. Domains can be the unit of access control for organizations, and assigned tags useful for identifying their purpose, ownership, location, or other characteristics. Policies and operations, assigned at the domain level, may vary from domain to domain, but within a domain are uniform, except for specific exclusions.

Abstract: A system groups multiple entities in a large distributed data store (DDS), such as directories and files, into a subset called a domain. The domain is treated as a unit for defining policies to detect and treat sensitive data. Sensitive data can be defined by enterprise or industry. Treatment of sensitive data may include quarantining, masking, and encrypting, of the data or the entity containing the data. Data in a domain can be copied as a unit, with or without the same structure, and with transformations such as masking or encryption, into parts of the same DDS or to a different DDS. Domains can be the unit of access control for organizations, and assigned tags useful for identifying their purpose, ownership, location, or other characteristics. Policies and operations, assigned at the domain level, may vary from domain to domain, but within a domain are uniform, except for specific exclusions.

Abstract: A system groups multiple entities in a large distributed data store (DDS), such as directories and files, into a subset called a domain. The domain is treated as a unit for defining policies to detect and treat sensitive data. Sensitive data can be defined by enterprise or industry. Treatment of sensitive data may include quarantining, masking, and encrypting, of the data or the entity containing the data. Data in a domain can be copied as a unit, with or without the same structure, and with transformations such as masking or encryption, into parts of the same DDS or to a different DDS. Domains can be the unit of access control for organizations, and assigned tags useful for identifying their purpose, ownership, location, or other characteristics. Policies and operations, assigned at the domain level, may vary from domain to domain, but within a domain are uniform, except for specific exclusions.

Abstract: A system groups multiple entities in a large distributed data store (DDS), such as directories and files, into a subset called a domain. The domain is treated as a unit for defining policies to detect and treat sensitive data. Sensitive data can be defined by enterprise or industry. Treatment of sensitive data may include quarantining, masking, and encrypting, of the data or the entity containing the data. Data in a domain can be copied as a unit, with or without the same structure, and with transformations such as masking or encryption, into parts of the same DDS or to a different DDS. Domains can be the unit of access control for organizations, and assigned tags useful for identifying their purpose, ownership, location, or other characteristics. Policies and operations, assigned at the domain level, may vary from domain to domain, but within a domain are uniform, except for specific exclusions.

Abstract: A method and system for securing sensitive data content in big data stores is provided. In an example method, entities within the big data store that contain sensitive data are identified. Then, users who have entitlement to access these sensitive entities are identified, along with their level of entitlement. Access controls are then set, based on which users can operate on the sensitive entities. Access or attempts to access these entities is monitored on an ongoing basis. An example system maps entitlement to entities within the big data store that contain sensitive content, to monitor access to these entities and to set access controls for users accessing the big data store.

Abstract: A system groups multiple entities in a large distributed data store (DDS), such as directories and files, into a subset called a domain. The domain is treated as a unit for defining policies to detect and treat sensitive data. Sensitive data can be defined by enterprise or industry. Treatment of sensitive data may include quarantining, masking, and encrypting, of the data or the entity containing the data. Data in a domain can be copied as a unit, with or without the same structure, and with transformations such as masking or encryption, into parts of the same DDS or to a different DDS. Domains can be the unit of access control for organizations, and assigned tags useful for identifying their purpose, ownership, location, or other characteristics. Policies and operations, assigned at the domain level, may vary from domain to domain, but within a domain are uniform, except for specific exclusions.