+27. pants Augsta riska MI sistēmu ietekmes uz pamattiesībām novērtējums
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27. pants Augsta riska MI sistēmu ietekmes uz pamattiesībām novērtējums

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1. Pārskats

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27. pants 1.

Pirms tiek lietota augsta riska MI sistēma, kas minēta 6. panta 2. punktā, izņemot augsta riska MI sistēmas, ko paredzēts lietot III pielikuma 2. punktā uzskaitītā jomā, uzturētāji, kas ir publisko tiesību subjekti vai privātas vienības, kas sniedz sabiedriskos pakalpojumus, un III pielikuma 5. punkta b) un c) apakšpunktos minēto augsta riska MI sistēmu uzturētāji, veic novērtējumu par ietekmi uz pamattiesībām, kādu var radīt šādas sistēmas lietošana. Minētajā nolūkā uzturētāji veic novērtējumu, kurā ietver

a) 

to uzturētāja procesu aprakstu, kuros augsta riska MI sistēma tiks lietota saskaņā ar tai paredzēto nolūku;

b) 

laikposma un biežuma aprakstu, kādā paredzēts lietot katru augsta riska MI sistēmu;

c) 

to fizisku personu un grupu kategorijas, kuras, iespējams, skartu sistēmas izmantošana konkrētā kontekstā;

d) 

konkrētus kaitējuma riskus, kam, iespējams, būtu ietekme uz to fizisku personu vai personu grupu kategorijām, kas noteiktas, ievērojot šā punkta c) apakšpunktu, ņemot vērā informāciju, ko nodrošinātājs sniedzis, ievērojot 13. pantu;

e) 

aprakstu par cilvēka virsvadības pasākumu īstenošanu saskaņā ar lietošanas instrukciju;

f) 

pasākumus, kas veicami, ja minētie riski materializējas, tostarp iekšējās pārvaldības kārtību un sūdzību mehānismus.
27. pants 2.
Šā panta 1. punktā paredzētais pienākums attiecas uz augsta riska MI sistēmas pirmo lietošanu. Uzturētājs līdzīgos gadījumos var izmantot iepriekš veiktus novērtējumus par ietekmi uz pamattiesībām vai esošu novērtējumu, ko veicis nodrošinātājs. Ja augsta riska MI sistēmas lietošanas laikā uzturētājs uzskata, ka kāds no 1. punktā uzskaitītajiem elementiem ir mainījies vai ir novecojis, uzturētājs veic nepieciešamos pasākumus, lai atjauninātu informāciju.
27. pants 3.
Pēc tam, kad ir veikts šā panta 1. punktā minētais novērtējums, uzturētājs paziņo tirgus uzraudzības iestādei tā rezultātus, iesniedz kā paziņojuma daļu aizpildīto veidni, kas minēta šā panta 5. punktā. Gadījumā, kas minēts 46. panta 1. punktā, uzturētāji var tikt atbrīvoti no minētā paziņošanas pienākuma.
27. pants 4.
Ja kāds no šajā pantā paredzētajiem pienākumiem jau ir izpildīts ar novērtējumu par ietekmi uz datu aizsardzību, kas veikts, ievērojot Regulas (ES) 2016/679 35. pantu vai Direktīvas (ES) 2016/680 27. pantu, uzturētājs, veicot šā panta 1. punktā minēto novērtējumu par ietekmi uz pamattiesībām, var iekļaut savstarpējas atsauces uz minētā novērtējuma par ietekmi uz datu aizsardzību attiecīgajām iedaļām vai tā attiecīgās daļas iekļaut novērtējumā par ietekmi uz pamattiesībām.
27. pants 5.
MI birojs izstrādā anketas veidni, tostarp izmantojot automatizētu rīku, lai atvieglotu uzturētājiem šajā pantā paredzēto pienākumu izpildi vienkāršotā veidā. Šī veidne attiecīgā gadījumā sniedz uzturētājiem iespēju iekļaut savstarpējas atsauces uz attiecīgajām iedaļām novērtējumā par ietekmi uz datu aizsardzību vai iekļaut attiecīgās tā daļas novērtējumā par ietekmi uz pamattiesībām, ievērojot 4. punktu.

1.1 Atsauces

1.2 Identificētās prasības

1.3 Saistītie standarti

2. Identificētās prasības

Prasības

3. Saistītie standarti

Standarti
SCF AI & Autonomous Technologies Impact Assessment

Description

Mechanisms exist to assess the impact(s) of proposed Artificial Intelligence (AI) and Autonomous Technologies (AAT) on individuals, groups, communities, organizations and society (e.g., Fundamental Rights Impact Assessment (FRIA)).

Possible Solutions & Considerations

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

∙ Basic AI impact assessment (documented impact on people, processes, decisions)
∙ AI governance program
∙ NIST AI RMF Map function

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

∙ Formal AI impact assessment for each deployed system
∙ AI governance program
∙ NIST AI RMF Map function

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

∙ AI impact assessment process aligned to NIST AI RMF
∙ AI governance program
∙ Algorithmic Impact Assessment (AIA) for higher-risk systems

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

∙ Formal AI impact assessment program with independent review for high-risk systems
∙ NIST AI RMF Map function
∙ Algorithmic Impact Assessment (AIA) process
∙ EU AI Act conformity assessment requirements (if applicable)

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

∙ Enterprise AI impact assessment framework (NIST AI RMF, EU AI Act)
∙ Third-party conformity assessments for high-risk AI (EU AI Act Article 43)
∙ Algorithmic Impact Assessment (AIA) program
∙ Board-level AI impact reporting

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

Artificial Intelligence and Autonomous Technology (AAT) 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 AAT domain capabilities are formally documented and centrally-managed by the entity.
▪ Standardized Operating Procedures (SOP) associated with AAT 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 AAT domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD).
▪ Artificial Intelligence (AI)-related controls are primarily administrative and preventative in nature (e.g., policies, standards and procedures & guidelines).
▪ Asset management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.

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 the impact(s) of proposed AAT on individuals, groups, communities, organizations and society (e.g., Fundamental Rights Impact Assessment (FRIA)).

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.
SCF 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.
SCF 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.
SCF 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.
SCF 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.
SCF 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.
SCF 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.
SCF 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.
SCF 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.
SCF AI TEVV Reporting

Description

Mechanisms exist to report the status and results of Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) to relevant stakeholders, including governing bodies, as required.

Possible Solutions & Considerations

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

∙ Report AI testing results to management

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

∙ Document and communicate AI test results to stakeholders

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

∙ Formal AI TEVV reporting process
∙ Standardized reporting templates

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

∙ AI TEVV reporting program
∙ Regular stakeholder briefings
∙ Findings tracking

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

∙ Enterprise AI TEVV reporting framework
∙ Automated test result dashboards
∙ Board-level AI risk reporting

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 report the status and results of Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) to relevant stakeholders, including governing bodies, as required.

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.