Direct links from the subject.
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The subject is an instance of a class. |
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The subject is an instance of a class. |
An idea or notion; a unit of thought. |
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A human-readable name for the subject. |
ID.AM-07.2: Inventories of data and associated metadata shall be maintained for designated data types. |
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ID.AM-07.2 |
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http://cyfun.data.gift/data/loc_CyFun2025_Booklet_ESSENTIAL_E_p55 |
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http://cyfun.data.gift/data/loc_CyFun2025_Booklet_IMPORTANT_E_p39 |
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Relates a concept to a concept that is more general in meaning. |
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A general note, for any purpose. |
The goal of this control is to ensure that inventories of data and their associated metadata are maintained for all data types explicitly selected or designated by policy or guidelines (for example, customer data, financial infor- mation, or production data). This supports secure data management, regulatory compliance, and operational integrity across ICT and OT environments. To achieve this goal: • Define a Data Classification Scheme A classification scheme should be established to categorise data types based on sensitivity and usage. This scheme should guide how data is handled, protected, and retained. • Apply Security Measures by Classification Level Appropriate securitycontrols should be implemented foreach classification level.These mayinclude encryp- tion, role-based access controls, and tailored data retention policies. • Identify and Protect Sensitive Data Types Inventories should include sensitive data such as: o Personally identifiable information (PII) o Financial and health information o Confidential business data o Intellectual property o Government and personnel records OT environments should also include operational data critical to safety and reliability. • Track Metadata for Each Data Instance Metadata should be maintained to support data governance. This includes: o Descriptive metadata (e.g. title, author, keywords) o Structural metadata (e.g. format, schema) o Administrative metadata (e.g. access rights, retention policy) o Technical metadata (e.g. encoding, checksum) • Monitor Data Provenance and Location The origin, ownership, and geolocation of each data instance should be documented. This helps to under- stand where and how data is stored and processed, especially in distributed or OT systems. • Continuously Discover and Analyse Ad Hoc Data Processes should be in place to identify new instances of sensitive data not captured by initial inventories. This helps address gaps in data flow mapping and supports ongoing data protection. • Consider Using the Traffic Light Protocol (TLP) TLP should be considered as a method to classify and share cybersecurity-related data, especially in incident response and threat intelligence contexts. |
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A general note, for any purpose. |
The goal of this control is to ensure that inventories of data and their associated metadata are maintained for all data types explicitly selected or designated by policy or guidelines (for example, customer data, financial infor- mation, or production data). This supports secure data management, regulatory compliance, and operational integrity across ICT and OT environments. To achieve this goal: - Define a Data Classification Scheme A classification scheme should be established to categorise data types based on sensitivity and usage. This scheme should guide how data is handled, protected, and retained. - Apply Security Measures by Classification Level Appropriate securitycontrols should be implemented foreach classification level.These mayinclude encryp- tion, role-based access controls, and tailored data retention policies. - Identify and Protect Sensitive Data Types Inventories should include sensitive data such as: - Personally identifiable information (PII) - Financial and health information - Confidential business data - Intellectual property - Government and personnel records OT environments should also include operational data critical to safety and reliability. - Track Metadata for Each Data Instance Metadata should be maintained to support data governance. This includes: - Descriptive metadata (e.g. title, author, keywords) - Structural metadata (e.g. format, schema) - Administrative metadata (e.g. access rights, retention policy) - Technical metadata (e.g. encoding, checksum) - Monitor Data Provenance and Location The origin, ownership, and geolocation of each data instance should be documented. This helps to under- stand where and how data is stored and processed, especially in distributed or OT systems. - Continuously Discover and Analyse Ad Hoc Data Processes should be in place to identify new instances of sensitive data not captured by initial inventories. This helps address gaps in data flow mapping and supports ongoing data protection. - Consider Using the Traffic Light Protocol (TLP) TLP should be considered as a method to classify and share cybersecurity-related data, especially in incident response and threat intelligence contexts. |
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A general note, for any purpose. |
<div><p>The goal of this control is to ensure that inventories of data and their associated metadata are maintained for all data types explicitly selected or designated by policy or guidelines (for example, customer data, financial infor- mation, or production data). This supports secure data management, regulatory compliance, and operational integrity across ICT and OT environments. To achieve this goal:</p><ul><li>Define a Data Classification Scheme A classification scheme should be established to categorise data types based on sensitivity and usage. This scheme should guide how data is handled, protected, and retained.</li><li>Apply Security Measures by Classification Level Appropriate securitycontrols should be implemented foreach classification level.These mayinclude encryp- tion, role-based access controls, and tailored data retention policies.</li><li>Identify and Protect Sensitive Data Types Inventories should include sensitive data such as:<ul><li>Personally identifiable information (PII)</li><li>Financial and health information</li><li>Confidential business data</li><li>Intellectual property</li><li>Government and personnel records OT environments should also include operational data critical to safety and reliability.</li></ul></li><li>Track Metadata for Each Data Instance Metadata should be maintained to support data governance. This includes:<ul><li>Descriptive metadata (e.g. title, author, keywords)</li><li>Structural metadata (e.g. format, schema)</li><li>Administrative metadata (e.g. access rights, retention policy)</li><li>Technical metadata (e.g. encoding, checksum)</li></ul></li><li>Monitor Data Provenance and Location The origin, ownership, and geolocation of each data instance should be documented. This helps to under- stand where and how data is stored and processed, especially in distributed or OT systems.</li><li>Continuously Discover and Analyse Ad Hoc Data Processes should be in place to identify new instances of sensitive data not captured by initial inventories. This helps address gaps in data flow mapping and supports ongoing data protection.</li><li>Consider Using the Traffic Light Protocol (TLP) TLP should be considered as a method to classify and share cybersecurity-related data, especially in incident response and threat intelligence contexts.</li></ul></div> |
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A general note, for any purpose. |
The goal of this control is to ensure that inventories of data and their associated metadata are maintained for all data types explicitly selected or designated by policy or guidelines (for example, customer data, financial infor- mation, or production data). This supports secure data management, regulatory compliance, and operational integrity across ICT and OT environments. To achieve this goal: - Define a Data Classification Scheme A classification scheme should be established to categorise data types based on sensitivity and usage. This scheme should guide how data is handled, protected, and retained. - Apply Security Measures by Classification Level Appropriate securitycontrols should be implemented foreach classification level.These mayinclude encryp- tion, role-based access controls, and tailored data retention policies. - Identify and Protect Sensitive Data Types Inventories should include sensitive data such as: - Personally identifiable information (PII) - Financial and health information - Confidential business data - Intellectual property - Government and personnel records OT environments should also include operational data critical to safety and reliability. - Track Metadata for Each Data Instance Metadata should be maintained to support data governance. This includes: - Descriptive metadata (e.g. title, author, keywords) - Structural metadata (e.g. format, schema) - Administrative metadata (e.g. access rights, retention policy) - Technical metadata (e.g. encoding, checksum) - Monitor Data Provenance and Location The origin, ownership, and geolocation of each data instance should be documented. This helps to under- stand where and how data is stored and processed, especially in distributed or OT systems. - Continuously Discover and Analyse Ad Hoc Data Processes should be in place to identify new instances of sensitive data not captured by initial inventories. This helps address gaps in data flow mapping and supports ongoing data protection. - Consider Using the Traffic Light Protocol (TLP) TLP should be considered as a method to classify and share cybersecurity-related data, especially in incident response and threat intelligence contexts. |
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A notation, also known as classification code, is a string of characters such as "T58.5" or "303.4833" used to uniquely identify a concept within the scope of a given concept scheme. |
ID.AM-07.2 |
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skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint properties. |
Data inventory and metadata management |
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A resource has no more than one value of skos:prefLabel per language tag, and no more than one value of skos:prefLabel without language tag. |
Inventories of data and associated metadata shall be maintained for designated data types. |
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Relates a resource (for example a concept) to a concept scheme in which it is included. |
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Relates a resource (for example a concept) to a concept scheme in which it is included. |
http://cyfun.data.gift/data/CyFun2025_delta_BASIC_to_IMPORTANT |
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Relates a resource (for example a concept) to a concept scheme in which it is included. |
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Relates a resource (for example a concept) to a concept scheme in which it is included. |
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The number of triples associated with the subject. |
19 |
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Specifies the dataset the subject is part of. |
Resultaten 1 - 21 of 21
Inverse links to the subject.
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Relates a concept to a concept that is more specific in meaning. |
Resultaten 1 - 1 of 1