+Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV)
---+AI TEVV Trustworthiness Assessment
---+AI TEVV Tools
---+AI TEVV Trustworthiness Demonstration
---+AI TEVV Safety Demonstration
---+AI TEVV Security & Resiliency Assessment
---+AI TEVV Transparency & Accountability Assessment
---+AI TEVV Privacy Assessment
---+AI TEVV Fairness & Bias Assessment
---+AI & Autonomous Technologies Model Validation
---+AI TEVV Comparable Deployment Settings
---+AI TEVV Empirically Validated Methods
---+AI TEVV Benchmarking Content Provenance
---+AI TEVV Model Collapse Mitigations
---+AI TEVV Third-Party Risk Management

Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV)

Description

Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security)
∙ AI governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Formal AI TEVV process for AI systems
∙ AI governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function
∙ AI testing tools (e.g., IBM OpenScale, Great Expectations)
∙ AI governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Enterprise AI TEVV program
∙ NIST AI RMF Measure function
∙ Third-party AI testing for high-risk systems
∙ AI testing integrated with CI/CD pipelines

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Enterprise AI TEVV program (NIST AI RMF Measure function)
∙ Independent AI testing and evaluation for high-risk systems
∙ AI TEVV integrated with MLOps and CI/CD
∙ EU AI Act conformity assessment (if applicable)

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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT.

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.

1. Přehled

AI TEVV Trustworthiness Assessment

Description

Mechanisms exist to evaluate Artificial Intelligence (AI) and Autonomous Technologies (AAT) for trustworthy behavior and operation including security, anonymization and disaggregation of captured and stored data for approved purposes.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Controls Validation Testing (CVT)
∙ AI trustworthiness checklist (accuracy, reliability, explainability)
∙ AI governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Controls Validation Testing (CVT)
∙ Formal AI trustworthiness assessment
∙ AI governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Controls Validation Testing (CVT)
∙ Structured AI trustworthiness assessment (NIST AI RMF trustworthiness dimensions)
∙ AI governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Controls Validation Testing (CVT)
∙ Formal AI trustworthiness assessment with independent review
∙ NIST AI RMF trustworthiness dimensions (accurate, explainable, interpretable, etc.)
∙ AI governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Controls Validation Testing (CVT)
∙ Enterprise AI trustworthiness assessment program
∙ Third-party AI trustworthiness audits
∙ NIST AI RMF trustworthiness dimensions at enterprise scale
∙ EU AI Act transparency and accuracy requirements

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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 evaluate AAT for trustworthy behavior and operation including security, anonymization and disaggregation of captured and stored data for approved purposes.

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.
AI TEVV Tools

Description

Mechanisms exist to document test sets, metrics and details about the tools used during Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 document test sets, metrics and details about the tools used during Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices.

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.
AI TEVV Trustworthiness Demonstration

Description

Mechanisms exist to demonstrate the Artificial Intelligence (AI) and Autonomous Technologies (AAT) to be deployed are:
(1) Valid;
(2) Reliable; and
(3) Operate as intended, based on approved designs.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 demonstrate the AAT to be deployed are:
(1) Valid;
(2) Reliable; and
(3) Operate as intended, based on approved designs.

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.
AI TEVV Safety Demonstration

Description

Mechanisms exist to demonstrate the Artificial Intelligence (AI) and Autonomous Technologies (AAT) to be deployed are safe, where residual risk does not exceed the organization's risk tolerance and can fail safely, particularly if made to operate beyond knowledge limits.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 demonstrate the AAT to be deployed are safe, residual risk does not exceed the organization's risk tolerance and can fail safely, particularly if made to operate beyond its knowledge limits.

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.
AI TEVV Security & Resiliency Assessment

Description

Mechanisms exist to evaluate the security and resilience of Artificial Intelligence (AI) and Autonomous Technologies (AAT) to be deployed.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 evaluate the security and resilience of AAT to be deployed.

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.
AI TEVV Transparency & Accountability Assessment

Description

Mechanisms exist to examine risks associated with transparency and accountability of Artificial Intelligence (AI) and Autonomous Technologies (AAT) to be deployed.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 examine risks associated with transparency and accountability of AAT to be deployed.

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.
AI TEVV Privacy Assessment

Description

Mechanisms exist to examine the data privacy risk of Artificial Intelligence (AI) and Autonomous Technologies (AAT) to be deployed.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 examine the data privacy risk of AAT to be deployed.

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.
AI TEVV Fairness & Bias Assessment

Description

Mechanisms exist to examine fairness and bias of Artificial Intelligence (AI) and Autonomous Technologies (AAT) to be deployed.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 examine fairness and bias of AAT to be deployed.

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.
AI & Autonomous Technologies Model Validation

Description

Mechanisms exist to validate the Artificial Intelligence (AI) and Autonomous Technologies (AAT) model.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 validate the AAT model.

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.
AI TEVV Comparable Deployment Settings

Description

Mechanisms exist to evaluate Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related performance against the assurance criteria demonstrated for conditions prior to deployment.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies governance program

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Information Assurance (IA) Program
∙ Artificial Intelligence (AI) / autonomous technologies 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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 evaluate AAT-related performance or the assurance criteria demonstrated for conditions similar to deployment settings.

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.
AI TEVV Empirically Validated Methods

Description

Mechanisms exist to evaluate claims of Artificial Intelligence (AI) and Autonomous Technologies (AAT) model capabilities using empirically validated methods.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Verify AI tool claims against published documentation

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Evaluate AI capabilities using published benchmarks

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Formal AI capability evaluation using empirically validated benchmarks

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ AI evaluation program using industry-standard benchmarks
∙ Independent verification process

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Enterprise AI evaluation framework
∙ Dedicated AI red team
∙ Third-party AI audits
∙ Empirical validation protocols

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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 evaluate claims of AAT model capabilities using empirically validated methods.

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.
AI TEVV Benchmarking Content Provenance

Description

Mechanisms exist to benchmark the verifiable lineage and origin of content used by Artificial Intelligence (AI) and Autonomous Technologies (AAT) according to industry-recognized standards.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Document training data sources for AI tools used

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Verify and document content provenance for AI training data

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Formal content provenance policy for AI systems
∙ Data lineage documentation

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Content provenance framework
∙ Cryptographic verification where possible
∙ Data lineage tracking tools

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Enterprise content provenance platform
∙ C2PA or similar standards adoption
∙ Automated lineage verification

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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 benchmark the verifiable lineage and origin of content used by AAT according to industry-recognized standards.

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.
AI TEVV Model Collapse Mitigations

Description

Mechanisms exist to mitigate concerns of model collapse by:
(1) Assessing the proportion of synthetic to non-synthetic training data; and
(2) Verifying training data is not overly homogenous or Artificial Intelligence (AI) and Autonomous Technologies (AAT) system-produced.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Limit use of purely synthetic training data

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Document training data composition (synthetic vs. real)

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Formal policy on synthetic data usage in AI training
∙ Data diversity requirements

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ AI training data governance program
∙ Monitoring of training data diversity

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Enterprise AI data governance platform
∙ Automated data composition monitoring
∙ Model collapse detection mechanisms

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

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 mitigate concerns of model collapse by:
(1) Assessing the proportion of synthetic to non-synthetic training data; and
(2) Verifying training data is not overly homogenous or AAT system-produced.

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.
AI TEVV Third-Party Risk Management

Description

Mechanisms exist to assess, approve and continuously monitor third-party Artificial Intelligence (AI) and Autonomous Technologies (AAT):
(1) Components;
(2) Application Programming Interfaces (APIs); and/or
(3) Services used by AI agents for security, privacy and compliance.

Possible Solutions & Considerations

Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2

∙ Review third-party AI components before use

Small Business (10-49 staff) / BLS Firm Size Classes 3-4

∙ Vendor assessment for third-party AI components and APIs

Medium Business (50-249 staff) / BLS Firm Size Classes 5-6

∙ Third-party AI component risk assessment process
∙ Ongoing monitoring policy

Large Business (250-999 staff) / BLS Firm Size Classes 7-8

∙ Formal TPRM program for AI vendors
∙ API security testing
∙ Continuous monitoring of third-party AI

Enterprise (> 1,000 staff) / BLS Firm Size Class 9

∙ Enterprise TPRM platform (e.g., OneTrust, BitSight)
∙ Automated third-party AI monitoring
∙ Supply chain security for AI components

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

Artificial Intelligence and Autonomous Technology (AAT) domain capabilities are ad hoc and inconsistent. Capability criteria associated with this control may include:
▪ Policies, standards & procedures associated with AAT domain capabilities provide limited coverage due to the depth and breadth of the existing documentation.
▪ AAT-related processes are expected to follow the organization's existing processes (e.g., incident response, asset management, change control and risk assessments).
▪ No formal Governance, Risk & Compliance (GRC) team exists to provide AAT oversight, where the Chief Information Officer (CIO), or similar function, governs technology decisions what is acceptable for AAT within the organization.

Level 2 Planned Tracked

SCR-CMM Level 2 criteria definitions are not available for this control:
▪ A reasonable person would conclude a well-defined and standardized process is required.
▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization.
▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts).

Level 3 Well Defined

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT 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 AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance 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 AAT 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 assess, approve and continuously monitor third-party AAT:
(1) Components;
(2) Application Programming Interfaces (APIs); and/or
(3) Services used by AI agents for security, privacy and compliance.

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.

1.1 Odkazy

1.2 Identifikované požadavky

1.3 Související regulace

2. Identifikované požadavky

Požadavky

3. Související regulace

Regulace
EULAW Článek 9 Systém řízení rizik
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EULAW Článek 11 Technická dokumentace
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EULAW Článek 16 Povinnosti poskytovatelů vysoce rizikových systémů AI

Článek 16

Povinnosti poskytovatelů vysoce rizikových systémů AI

Poskytovatelé vysoce rizikových systémů AI:

a) 

zajišťují, aby jejich vysoce rizikové systémy AI splňovaly požadavky stanovené v oddíle 2;

b) 

uvedou na vysoce rizikovém systému AI, nebo není-li to možné, na obalu nebo v dokumentaci, která je k vysoce rizikovému systému AI přiložena, své jméno, zapsaný název společnosti nebo zapsanou ochrannou známku a adresu, na které je lze kontaktovat;

c) 

mají zaveden systém řízení kvality, který je v souladu s článkem 17;

d) 

vedou dokumentaci uvedenou v článku 18;

e) 

pokud jsou vysoce rizikové systémy AI pod jejich kontrolou, zajišťují automatické generování protokolů těmito systémy, jak je uvedeno v článku 19;

f) 

zajistí, aby byl u daného vysoce rizikového systému AI před jeho uvedením na trh nebo do provozu proveden příslušný postup posuzování shody, jak je uvedeno v článku 43;

g) 

vypracovávají EU prohlášení o shodě v souladu s článkem 47;

h) 

umístí na vysoce rizikový systém AI, nebo není-li to možné, na jeho obal nebo dokumentaci, která je k němu přiložena, označení CE, aby vyjádřili soulad s tímto nařízením podle článku 48;

i) 

dodržují povinnosti registrace uvedené v čl. 49 odst. 1;

j) 

přijímají nezbytná nápravná opatření a poskytují informace, jak je požadováno v článku 20;

k) 

na odůvodněnou žádost příslušného vnitrostátního orgánu prokáží soulad daného vysoce rizikového systému AI s požadavky stanovenými v oddíle 2;

l) 

zajistí, aby vysoce rizikový systém AI splňoval požadavky na přístupnost v souladu se směrnicemi (EU) 2016/2102 a (EU) 2019/882.
EULAW Článek 17 Systém řízení kvality
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EULAW Článek 17 Systém řízení kvality
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EULAW Článek 23 Povinnosti dovozců
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EULAW Článek 23 Povinnosti dovozců
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 27 Posouzení dopadu vysoce rizikových systémů AI na základní práva
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EULAW Článek 55 Povinnosti poskytovatelů obecných modelů AI se systémovým rizikem
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EULAW Článek 55 Povinnosti poskytovatelů obecných modelů AI se systémovým rizikem
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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EULAW Článek 60 Testování vysoce rizikových systémů AI v reálných podmínkách mimo regulační sandboxy pro AI
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Propojené úkoly

Vazby
is related to Annual
is related to relative Control Weighting = 05
is related to Process
is related to Protect
is related to SCRM Focus Tier 2 OPERATIONAL
is related to SCRM Focus Tier 3 TACTICAL
blocks Loss of Accountability / Attribution
blocks Excessive or Unauthorized Privilege
blocks Unauthorized Privilege Elevation
blocks Unauthorized Access
blocks Loss, Theft or Damage of Assets
blocks Unauthorized Modification / Loss of System Integrity
blocks Business / Service Interruption
blocks Data Loss / Integrity Compromise
blocks Reduced Operational Productivity
blocks System / Data Compromise (Technical Vector)
blocks System / Data Compromise (Non-Technical Vector)
blocks Revenue Loss
blocks Contractual Default / Termination
blocks Loss of Competitive Advantage
blocks Reputational Harm / Loss of Stakeholder Trust
blocks Regulatory / Legal / Contractual Financial Liability
blocks Exposure from Unaddressed Vulnerabilities
blocks System / Service Compromise
blocks Failure to Support Business / Mission Objectives
blocks Unaddressed Security / Compliance Exposure
blocks Unperformed / Unowned Control Responsibilities
blocks Uncontrolled Internal Security / Privacy Exposure
blocks Uncontrolled Third-Party Security / Privacy Exposure
blocks Lack of oversight of internal controls
blocks Lack of oversight of third-party controls
blocks Harm from Illegal, Abusive or Unsafe Content / Conduct
blocks Loss of Investigative / Evidentiary Capability
blocks Delayed Containment / Increased Incident Impact
blocks Incident Persistence / Recurrence
blocks Incident Response / Recovery Cost
blocks Undetected / Delayed Detection of Adverse Activity
blocks Third-Party Cybersecurity Compromise / Exposure
blocks Third-Party Physical Security Disruption / Exposure
blocks Sub-Tier / Fourth-Party Supply Chain Exposure
blocks Third-Party Legal / Compliance Exposure
blocks Harm from Unauthorized / Unintended Product or Service Use
blocks Third-Party Dependency Failure
is related to relative Control Weighting = 10
is related to Detect
is related to SCRM Focus Tier 1 STRATEGIC
blocks Loss of Authorization / Certification / License
blocks Inability to Demonstrate Due Diligence / Due Care
blocks Unreliable Assurance / Decision Information
blocks Recovery Failure / Unacceptable Recovery Delay
blocks AI Output / Decision Integrity Failure
blocks AI Data Confidentiality / Integrity / Provenance Loss
blocks Unauthorized / Harmful Autonomous Action
blocks Unintended or Unsafe Technology Behavior
blocks AI Bias / Discriminatory Outcome
blocks Loss of Confidentiality
blocks Loss of Data Integrity
blocks Loss of Data / Service Availability
blocks Injury / Loss of Life
blocks Physical / Environmental Damage
blocks Drought & Water Shortage
blocks Earthquakes
blocks Fire & Wildfires
blocks Floods
blocks Hurricanes & Tropical Storms
blocks Landslides & Debris Flow
blocks Pandemic (Disease) Outbreaks
blocks Severe Weather
blocks Space Weather
blocks Thunderstorms & Lightning
blocks Tornadoes
blocks Tsunamis
blocks Volcanoes
blocks Winter Storms & Extreme Cold
blocks Malicious Cyber Activity
blocks Dysfunctional Management Practices
blocks Human Error
blocks Technical / Mechanical Failure
blocks Statutory / Regulatory / Contractual Change
blocks Data Quality Degradation / Data Poisoning
blocks Artificial Intelligence & Autonomous Technologies (AAT) System Failure or Unintended Behavior
blocks Fraud, Corruption and/or Intentional Criminal Conduct
blocks Conflict of Interest (COI) / Undue Influence / Insider Misconduct
blocks Product Defect / Design Failure
blocks Workforce Shortage / Loss of Critical Skills / Labor Disruption
blocks Adversarial Artificial Intelligence (AI) Attack
is blocked by AAT-28 Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) – Assessment Procedure
  • Secure Controls Framework -

    The Secure Controls Framework® (SCF)

    "The SCF is the Common Controls Framework™ (CCF), the world's most comprehensive cybersecurity and data privacy metaframework - it is also free to use. The entire concept is building secure, compliant and resilient capabilities in the most efficient and cost-effective manner possible.

    The SCF is more than just a unified control catalog, since its included content creates a playbook for Governance, Risk & Compliance (GRC) capabilities. Used globally by organizations of every size, the SCF is a robust and scalable solution for security, compliance and resilience controls. As a comprehensive security framework, the SCF maps 1,400+ controls across 200+ laws, regulations, and industry frameworks so you can implement once and comply everywhere.

    Like it or not, cybersecurity is a protracted war on an asymmetric battlefield, where the threats are everywhere and as defenders we have to make the effort to work together to help improve cybersecurity and data privacy practices, since we all suffer when massive data breaches occur or when cyber attacks have physical impacts. Hackers share information on attack methods with other hackers, so why shouldn’t the good guys share information on how to best protect an organization? We decided to take action and make a difference, since we feel it is too important to wait for someone else to fix the problems that exist.

    The SCF is made up of volunteers, mainly specialists within the cybersecurity profession, who focus on GRC and the cybersecurity side of data privacy. These are auditors, engineers, architects, incident responders, consultants and other specialists who live and breathe these topics on a daily basis. The end product is "expert-derived content" that makes up the SCF." https://securecontrolsframework.com/ 

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