+Data & Asset Classification
---+Highest Classification Level
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Data & Asset Classification
Description
Mechanisms exist to ensure data and assets are categorized in accordance with applicable statutory, regulatory and contractual requirements.
Possible Solutions & Considerations
Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2
∙ Data classification program
∙ IT Asset Management (ITAM) program
Small Business (10-49 staff) / BLS Firm Size Classes 3-4
∙ Data classification program
∙ IT Asset Management (ITAM) program
Medium Business (50-249 staff) / BLS Firm Size Classes 5-6
∙ Data classification program
∙ IT Asset Management (ITAM) program
∙ Data governance program
Large Business (250-999 staff) / BLS Firm Size Classes 7-8
∙ Data classification program
∙ IT Asset Management (ITAM) program
∙ Data governance program
Enterprise (> 1,000 staff) / BLS Firm Size Class 9
∙ Data classification program
∙ IT Asset Management (ITAM) program
∙ Data governance program
SCR-CMM
Level 0 Not Performed
Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.
Level 1 Performed Informally
SCR-CMM Level 1 criteria definitions are not available for this control:
▪ A reasonable person would conclude this control requires a structured process.
▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality.
Level 2 Planned Tracked
Data Classification & Handling (DCH) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity.
▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are documented and maintained by process owners.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD).
▪ Data classification and handling-related controls are primarily administrative and preventative in nature (e.g., policies, standards, procedures & guidelines).
▪ Data classification and handling management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.
▪ A formalized data classification scheme exists to identify categories of data, based on protection requirements from applicable laws, regulations and/or contractual obligations.
▪ TAASD are categorized according to data classification and business criticality.
▪ Data classification and handling criteria govern requirements protect sensitive/regulated regardless of where it is stored, processed and/or transmitted.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to categorize data in accordance with organizational policies and standards.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to document where sensitive/regulated data is stored, transmitted and/or processed.
Level 3 Well Defined
Data Classification & Handling (DCH) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function.
▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are well-documented and kept current by process owners.
▪ A Governance, Risk & Compliance (GRC) team, or similar function, is appropriately staffed and supported to implement and maintain DCH domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of data classification and handling operations (e.g., GRC platform).
▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls).
▪ An implemented and operational capability exists to ensure data and assets are categorized in accordance with applicable statutory, regulatory and contractual requirements.
Level 4 Quantitatively Controlled
Data Classification & Handling (DCH) capabilities, in addition to being standardized across the entity and centrally managed to ensure consistency across Technology Assets, Applications, Services and/or Data (TAASD), efforts are metrics driven to provide sufficient insight for decision makers to predict optimal performance, ensure continued operations and/or identify areas for improvement. Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Applicable SCR-CMM Level 3 (Well Defined) capabilities are implemented and operational.
▪ Metrics reporting includes quantitative analysis of Key Performance Indicators (KPIs).
▪ Metrics reporting includes quantitative analysis of Key Risk Indicators (KRIs).
▪ Scope of metrics, KPIs and KRIs covers organization-wide cybersecurity and data protection controls, including functions performed by third-parties.
▪ Organizational leadership maintains a formal process to objectively review and respond to metrics, KPIs and KRIs (e.g., monthly or quarterly review).
▪ Based on metrics analysis, process improvement recommendations are submitted for review and are handled in accordance with change control processes.
▪ Business and technical stakeholders are involved in reviewing and approving proposed changes to evolve capabilities.
Level 5 Continuously Improving
Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions:
▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control.
▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies.
▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
1. Overview
| Summary |
Standard |
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Highest Classification Level
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Description
Mechanisms exist to ensure that Technology Assets, Applications and/or Services (TAAS) are classified according to the highest level of data sensitivity that is stored, transmitted and/or processed.
Possible Solutions & Considerations
Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2
∙ Data classification program
∙ IT Asset Management (ITAM) program
Small Business (10-49 staff) / BLS Firm Size Classes 3-4
∙ Data classification program
∙ IT Asset Management (ITAM) program
Medium Business (50-249 staff) / BLS Firm Size Classes 5-6
∙ Data classification program
∙ IT Asset Management (ITAM) program
∙ Data governance program
Large Business (250-999 staff) / BLS Firm Size Classes 7-8
∙ Data classification program
∙ IT Asset Management (ITAM) program
∙ Data governance program
Enterprise (> 1,000 staff) / BLS Firm Size Class 9
∙ Data classification program
∙ IT Asset Management (ITAM) program
∙ Data governance program
SCR-CMM
Level 0 Not Performed
Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.
Level 1 Performed Informally
SCR-CMM Level 1 criteria definitions are not available for this control:
▪ A reasonable person would conclude this control requires a structured process.
▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality.
Level 2 Planned Tracked
Data Classification & Handling (DCH) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity.
▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are documented and maintained by process owners.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD).
▪ Data classification and handling-related controls are primarily administrative and preventative in nature (e.g., policies, standards, procedures & guidelines).
▪ Data classification and handling management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.
▪ A formalized data classification scheme exists to identify categories of data, based on protection requirements from applicable laws, regulations and/or contractual obligations.
▪ TAASD are categorized according to data classification and business criticality.
▪ Data classification and handling criteria govern requirements protect sensitive/regulated regardless of where it is stored, processed and/or transmitted.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to categorize data in accordance with organizational policies and standards.
Level 3 Well Defined
Data Classification & Handling (DCH) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function.
▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are well-documented and kept current by process owners.
▪ A Governance, Risk & Compliance (GRC) team, or similar function, is appropriately staffed and supported to implement and maintain DCH domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of data classification and handling operations (e.g., GRC platform).
▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls).
▪ An implemented and operational capability exists to ensure that Technology Assets, Applications and/or Services (TAAS) are classified according to the highest level of data sensitivity that is stored, transmitted and/or processed.
Level 4 Quantitatively Controlled
Utilize SCR-CMM Level 3 criteria definitions:
▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control.
▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define.
Level 5 Continuously Improving
Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions:
▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control.
▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies.
▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
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1.1 References
1.2 Identified Requirements
1.3 Related Regulations
2. Identified Requirements
Requirements
| Source |
Requirement |
3. Related Regulations
Regulations
| Source |
Regulation |
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EULAW
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Article 17 Quality management system
Article 17
Quality management system
1. Providers of high-risk AI systems shall put a quality management system in place that ensures compliance with this Regulation. That system shall be documented in a systematic and orderly manner in the form of written policies, procedures and instructions, and shall include at least the following aspects:
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(a)
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a strategy for regulatory compliance, including compliance with conformity assessment procedures and procedures for the management of modifications to the high-risk AI system;
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(b)
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techniques, procedures and systematic actions to be used for the design, design control and design verification of the high-risk AI system;
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(c)
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techniques, procedures and systematic actions to be used for the development, quality control and quality assurance of the high-risk AI system;
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(d)
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examination, test and validation procedures to be carried out before, during and after the development of the high-risk AI system, and the frequency with which they have to be carried out;
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(e)
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technical specifications, including standards, to be applied and, where the relevant harmonised standards are not applied in full or do not cover all of the relevant requirements set out in Section 2, the means to be used to ensure that the high-risk AI system complies with those requirements;
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(f)
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systems and procedures for data management, including data acquisition, data collection, data analysis, data labelling, data storage, data filtration, data mining, data aggregation, data retention and any other operation regarding the data that is performed before and for the purpose of the placing on the market or the putting into service of high-risk AI systems;
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(g)
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the risk management system referred to in Article 9;
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(h)
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the setting-up, implementation and maintenance of a post-market monitoring system, in accordance with Article 72;
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(i)
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procedures related to the reporting of a serious incident in accordance with Article 73;
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(j)
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the handling of communication with national competent authorities, other relevant authorities, including those providing or supporting the access to data, notified bodies, other operators, customers or other interested parties;
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(k)
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systems and procedures for record-keeping of all relevant documentation and information;
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(l)
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resource management, including security-of-supply related measures;
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(m)
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an accountability framework setting out the responsibilities of the management and other staff with regard to all the aspects listed in this paragraph.
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2. The implementation of the aspects referred to in paragraph 1 shall be proportionate to the size of the provider’s organisation. Providers shall, in any event, respect the degree of rigour and the level of protection required to ensure the compliance of their high-risk AI systems with this Regulation.
3. Providers of high-risk AI systems that are subject to obligations regarding quality management systems or an equivalent function under relevant sectoral Union law may include the aspects listed in paragraph 1 as part of the quality management systems pursuant to that law.
4. For providers that are financial institutions subject to requirements regarding their internal governance, arrangements or processes under Union financial services law, the obligation to put in place a quality management system, with the exception of paragraph 1, points (g), (h) and (i) of this Article, shall be deemed to be fulfilled by complying with the rules on internal governance arrangements or processes pursuant to the relevant Union financial services law. To that end, any harmonised standards referred to in Article 40 shall be taken into account.
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Linked Issues
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