+Artikel 60 Testning av AI-system med hög risk under verkliga förhållanden utanför regulatoriska sandlådor för AI
---+Artikel 60 1.
---+Artikel 60 2.
---+Artikel 60 3.
---+Artikel 60 4.
---+Artikel 60 5.
---+Artikel 60 6.
---+Artikel 60 7.
---+Artikel 60 8.
---+Artikel 60 9.

Artikel 60 Testning av AI-system med hög risk under verkliga förhållanden utanför regulatoriska sandlådor för AI

1. Översikt

Rubrik	Reglering
Artikel 60 1.	Testning av AI-system med hög risk under verkliga förhållanden utanför regulatoriska sandlådor för AI får utföras av leverantörer eller potentiella leverantörer av AI-system med hög risk som förtecknas i bilaga III eller som omfattas av unionens harmoniseringslagstiftning som förtecknas i avsnitt A i bilaga I, i enlighet med denna artikel och den plan för testning under verkliga förhållanden som avses i denna artikel, utan att det påverkar förbuden enligt artikel 5. Kommissionen ska genom genomförandeakter specificera de närmare inslagen i planen för testning under verkliga förhållanden. Dessa genomförandeakter ska antas i enlighet med det granskningsförfarande som avses i artikel 98.2. Denna punkt påverkar inte tillämpningen av unionsrätten eller nationell rätt om testning under verkliga förhållanden av AI-system med hög risk som är relaterade till produkter som omfattas av unionens harmoniseringslagstiftning som förtecknas i bilaga I.
Artikel 60 2.	Leverantörer eller potentiella leverantörer får på egen hand eller i partnerskap med en eller flera tillhandahållare eller potentiella tillhandahållare utföra testning under verkliga förhållanden av AI-system med hög risk som avses i bilaga III eller som omfattas av unionens harmoniseringslagstiftning som förtecknas i avsnitt A i bilaga I när som helst innan AI-systemet med hög risk släpps ut på marknaden eller tas i bruk.
Artikel 60 3.	Testning av AI-system med hög risk under verkliga förhållanden enligt denna artikel påverkar inte etisk granskning som krävs enligt nationell rätt eller unionsrätten.
Artikel 60 4.	Leverantörer eller potentiella leverantörer får utföra testningen under verkliga förhållanden endast om samtliga följande villkor är uppfyllda: a) Leverantören eller den potentiella leverantören har utarbetat en plan för testning under verkliga förhållanden och lämnat in den till marknadskontrollmyndigheten i den medlemsstat där testningen under verkliga förhållanden ska utföras. b) Marknadskontrollmyndigheten i den medlemsstat där testningen under verkliga förhållanden ska utföras har godkänt testningen under verkliga förhållanden och planen för testning under verkliga förhållanden. Om marknadskontrollmyndigheten inte har lämnat något svar inom 30 dagar ska testningen under verkliga förhållanden och planen för testning under verkliga förhållanden betraktas som godkänd. Om det i nationell rätt inte föreskrivs något tyst godkännande, ska testningen under verkliga förhållanden fortfarande förutsätta ett tillstånd. c) Leverantören eller den potentiella leverantören, med undantag för leverantörer eller potentiella leverantörer av AI-system med hög risk som avses i punkterna 1, 6 och 7 i bilaga III på områdena brottsbekämpning, migration, asyl och gränskontrollförvaltning samt AI-system med hög risk som avses i punkt 2 i bilaga III, har registrerat testning under verkliga förhållanden i enlighet med artikel 71.4 med ett unikt unionsomfattande identifieringsnummer och med den information som anges i bilaga IX. Leverantören eller den potentiella leverantören av AI-system med hög risk som avses i punkterna 1, 6 och 7 i bilaga III på områdena brottsbekämpning, migration, asyl och gränskontrollförvaltning, har registrerat testning under verkliga förhållanden i den säkra icke-offentliga delen av EU-databasen enligt artikel 49.4 d med ett unikt unionsomfattande identifieringsnummer och med den information som anges däri. Leverantören eller den potentiella leverantören av AI-system med hög risk som avses i punkt 2 i bilaga III har registrerat testning under verkliga förhållanden i enlighet med artikel 49.5. d) Den leverantör eller potentiella leverantör som utför testningen under verkliga förhållanden är etablerad i unionen eller har utsett ett juridiskt ombud som är etablerat i unionen. e) Uppgifter som samlats in och behandlats för testning under verkliga förhållanden ska överföras till tredjeländer endast om lämpliga och tillämpliga skyddsåtgärder enligt unionsrätten vidtas. f) Testningen under verkliga förhållanden varar inte längre än vad som är nödvändigt för att uppnå dess mål och under inga omständigheter längre än sex månader, med möjlighet till förlängning med ytterligare sex månader om leverantören eller den potentiella leverantören gör en förhandsanmälan till marknadskontrollmyndigheten, som ska åtföljas av en förklaring av behovet av en sådan förlängning. g) Försökspersoner i testningen under verkliga förhållanden som tillhör sårbara grupper på grund av ålder eller funktionsnedsättning skyddas på lämpligt sätt. h) Om en leverantör eller potentiell leverantör organiserar testningen under verkliga förhållanden i samarbete med en eller flera tillhandahållare eller potentiella tillhandahållare, har dessa informerats om alla aspekter av testningen som är relevanta för deras beslut att delta och fått den relevanta bruksanvisningen för det AI-system som avses i artikel 13. Leverantören eller den potentiella leverantören och tillhandahållaren eller den potentiella tillhandahållaren ska ingå ett avtal som anger deras roller och ansvar i syfte att säkerställa överensstämmelse med bestämmelserna om testning under verkliga förhållanden enligt denna förordning och enligt annan tillämplig unionsrätt och nationell rätt. i) Försökspersonerna i testningen under verkliga förhållanden har gett sitt informerade samtycke i enlighet med artikel 61, eller, när det gäller brottsbekämpning, om en begäran om informerat samtycke skulle hindra AI-systemet att testas, får själva testningen och resultatet av testningen under verkliga förhållanden inte ha någon negativ inverkan på försökspersonerna, och deras personuppgifter ska raderas när testningen har utförts. j) Testningen under verkliga förhållanden övervakas effektivt av leverantören eller den potentiella leverantören och av tillhandahållare eller potentiella tillhandahållare genom personer som har lämpliga kvalifikationer inom det berörda området och som har nödvändig kapacitet, utbildning och auktoritet för att utföra sina uppgifter. k) AI-systemets förutsägelser, rekommendationer eller beslut kan effektivt upphävas eller ignoreras.
Artikel 60 5.	Försökspersoner inom testningen under verkliga förhållanden, eller deras lagligen utsedda företrädare, beroende på vad som är lämpligt, får, utan att detta medför nackdelar och utan att behöva lämna någon motivering, när som helst avsluta sitt deltagande i testningen genom att dra tillbaka sitt informerade samtycke och får begära omedelbar och permanent radering av sina personuppgifter. Tillbakadragandet av det informerade samtycket påverkar inte den verksamhet som redan utförts.
Artikel 60 6.	I enlighet med artikel 75 ska medlemsstaterna ge sina marknadskontrollmyndigheter befogenhet att kräva att leverantörer och potentiella leverantörer tillhandahåller information, att utföra oanmälda inspektioner på distans eller på plats och att utföra kontroller av utförandet av testningen under verkliga förhållanden och tillhörande AI-system med hög risk. Marknadskontrollmyndigheterna ska använda dessa befogenheter för att säkerställa en säker utveckling av testning under verkliga förhållanden.
Artikel 60 7.	Alla allvarliga incidenter som identifieras under testningen under verkliga förhållanden ska rapporteras till den nationella marknadskontrollmyndigheten i enlighet med artikel 73. Leverantören eller den potentiella leverantören ska vidta omedelbara riskbegränsningsåtgärder eller, om så inte sker, tillfälligt avbryta testningen under verkliga förhållanden tills sådan riskreducering äger rum eller i annat fall avsluta den. Leverantören eller den potentiella leverantören ska fastställa ett förfarande för snabb återkallelse av AI-systemet när testningen under verkliga förhållanden avslutas på detta sätt.
Artikel 60 8.	Leverantörer eller potentiella leverantörer ska underrätta den nationella marknadskontrollmyndigheten i den medlemsstat där testningen under verkliga förhållanden ska utföras om det tillfälliga avbrottet i eller avslutandet av testningen under verkliga förhållanden och om de slutliga resultaten.
Artikel 60 9.	Leverantören och den potentiella leverantören ska vara ansvariga enligt tillämplig unionsrätt och nationell rätt om ansvar för eventuella skador som orsakas under deras deltagande i testningen under verkliga förhållanden.

1.1 Referenser

1.2 Identifierade krav

1.3 Relaterade standarder

2. Identifierade krav

Krav
Källa	Krav

3. Relaterade standarder

Standarder
Källa	Krav
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	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.
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.
SCF	Serious Incident Reporting For AI & Autonomous Technologies Description Mechanisms exist to report any serious incident involving operational Artificial Intelligence (AI) and Autonomous Technologies (AAT) to relevant authorities as to when and where the serious incident occurred, in accordance with mandated reporting timelines. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Report serious AI incidents to relevant authorities as required Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Incident reporting procedure for serious AI incidents Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Formal serious AI incident reporting process ∙ Regulatory reporting contacts Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ AI incident response plan with regulatory reporting requirements ∙ Legal team involvement Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Enterprise AI incident response program ∙ Automated regulatory notification workflows ∙ Legal and compliance integration 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 report any serious incident involving operational AAT to relevant authorities as to when and where the serious incident occurred, in accordance with mandated reporting timelines. 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.

Builddate: 27.07.2026 00:00:00 -Doknr: CELEX:32024R1689
EULAW -
European Regulation published on http://eur-lex.europa.eu/

Artikel 60 Testning av AI-system med hög risk under verkliga förhållanden utanför regulatoriska sandlådor för AI

1. Översikt

1.1 Referenser

1.2 Identifierade krav

1.3 Relaterade standarder

2. Identifierade krav

3. Relaterade standarder

Description

Possible Solutions & Considerations

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

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

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

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

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

SCR-CMM

Level 0 Not Performed

Level 1 Performed Informally

Level 2 Planned Tracked

Level 3 Well Defined

Level 4 Quantitatively Controlled

Level 5 Continuously Improving

Description

Possible Solutions & Considerations

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

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

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

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

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

SCR-CMM

Level 0 Not Performed

Level 1 Performed Informally

Level 2 Planned Tracked

Level 3 Well Defined

Level 4 Quantitatively Controlled

Level 5 Continuously Improving

Description

Possible Solutions & Considerations

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

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

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

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

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

SCR-CMM

Level 0 Not Performed

Level 1 Performed Informally

Level 2 Planned Tracked

Level 3 Well Defined

Level 4 Quantitatively Controlled

Level 5 Continuously Improving

Description

Possible Solutions & Considerations

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

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

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

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

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

SCR-CMM

Level 0 Not Performed

Level 1 Performed Informally

Level 2 Planned Tracked

Level 3 Well Defined

Level 4 Quantitatively Controlled

Level 5 Continuously Improving

Description

Possible Solutions & Considerations

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

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

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

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

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

SCR-CMM

Level 0 Not Performed

Level 1 Performed Informally

Level 2 Planned Tracked

Level 3 Well Defined

Level 4 Quantitatively Controlled

Level 5 Continuously Improving

Description

Possible Solutions & Considerations

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