+60 straipsnis Didelės rizikos DI sistemų bandymas realiomis sąlygomis už apribotų bandomųjų DI reglamentavimo aplinkų ribų
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60 straipsnis Didelės rizikos DI sistemų bandymas realiomis sąlygomis už apribotų bandomųjų DI reglamentavimo aplinkų ribų

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1. Apžvalga

,
60 straipsnis 1.
Nedarant poveikio 5 straipsnyje nustatytiems draudimams, didelės rizikos DI sistemų, kurios išvardytos III priede arba kurioms taikomi I priedo A skirsnyje išvardyti Sąjungos derinamieji teisės aktai, tiekėjai arba galimi tiekėjai, laikydamiesi šio straipsnio ir šiame straipsnyje nurodyto bandymo realiomis sąlygomis plano, gali atlikti didelės rizikos DI sistemų bandymą realiomis sąlygomis už apribotų bandomųjų DI reglamentavimo aplinkų ribų.

Komisija įgyvendinimo aktais nurodo išsamius bandymo realiomis sąlygomis plano elementus. Tie įgyvendinimo aktai priimami laikantis 98 straipsnio 2 dalyje nurodytos nagrinėjimo procedūros.

Šia dalimi nedaromas poveikis Sąjungos ar nacionalinės teisės aktams dėl didelės rizikos DI sistemų, susijusių su gaminiais, kuriems taikomi I priede išvardyti teisės aktai, bandymo realiomis sąlygomis.
60 straipsnis 2.
Tiekėjai arba galimi tiekėjai, patys vieni arba kartu su vienu ar daugiau diegėjų ar galimų diegėjų, gali didelės rizikos DI sistemų, kurios išvardytos III priede arba kurioms taikomi I priedo A skirsnyje išvardyti Sąjungos derinamieji teisės aktai, bandymą realiomis sąlygomis atlikti bet kuriuo metu prieš pateikdami didelės rizikos DI sistemą rinkai arba prieš pradedant ją naudoti.
60 straipsnis 3.
Didelės rizikos DI sistemų bandymas realiomis sąlygomis pagal šį straipsnį nedaro poveikio etikos aspektų vertinimui, kurio reikalaujama pagal Sąjungos ar nacionalinę teisę.
60 straipsnis 4.

Tiekėjai arba galimi tiekėjai bandymą realiomis sąlygomis gali atlikti tik tuo atveju, jei tenkinamos visos šios sąlygos:

a) 

tiekėjas arba galimas tiekėjas yra parengęs bandymo realiomis sąlygomis planą ir jį yra pateikęs valstybės narės, kurioje turi būti atliekamas bandymas realiomis sąlygomis, rinkos priežiūros institucijai;

b) 

valstybės narės, kurioje turi būti atliekamas bandymas realiomis sąlygomis, rinkos priežiūros institucija yra patvirtinusi bandymą realiomis sąlygomis ir bandymo realiomis sąlygomis planą; jeigu rinkos priežiūros institucija per 30 dienų nepateikia atsakymo, laikoma, kad bandymas realiomis sąlygomis ir bandymo realiomis sąlygomis planas yra patvirtinti; jei nacionalinės teisės aktuose nėra numatytas tylus pritarimas, bandymui realiomis sąlygomis turi būti gautas leidimas;

c) 

tiekėjas arba galimas tiekėjas, išskyrus III priedo 1, 6 ir 7 punktuose nurodytų didelės rizikos DI sistemų teisėsaugos, migracijos, prieglobsčio ir sienų kontrolės valdymo srityse ir didelės rizikos DI sistemų, nurodytų III priedo 2 punkte, tiekėjus ar galimus tiekėjus, bandymą realiomis sąlygomis yra užregistravęs pagal 71 straipsnio 4 dalį su Sąjungos masto unikaliu identifikaciniu numeriu ir su IX priede nurodyta informacija; III priedo 1, 6 ir 7 punktuose nurodytų didelės rizikos DI sistemų teisėsaugos, migracijos, prieglobsčio ir sienų kontrolės valdymo srityse tiekėjas arba galimas tiekėjas bandymą realiomis sąlygomis yra užregistravęs ES duomenų bazės neviešoje dalyje pagal 49 straipsnio 4 dalies d punktą su Sąjungos masto unikaliu identifikaciniu numeriu ir su ten nurodyta informacija; didelės rizikos DI sistemų, nurodytų III priedo 2 punkte, tiekėjus ar galimus tiekėjus, bandymą realiomis sąlygomis yra užregistravęs pagal 49 straipsnio 5 dalį;

d) 

tiekėjas arba galimas tiekėjas, atliekantis bandymą realiomis sąlygomis, yra įsisteigęs Sąjungoje arba bandymo realiomis sąlygomis tikslais yra paskyręs Sąjungoje įsisteigusį teisinį atstovą;

e) 

duomenys, surinkti ir tvarkomi bandymo realiomis sąlygomis tikslais, trečiosioms valstybėms perduodami tik tuo atveju, jei įgyvendinamos tinkamos ir taikytinos apsaugos priemonės pagal Sąjungos teisę;

f) 

bandymas realiomis sąlygomis trunka ne ilgiau, nei būtina jo tikslams pasiekti, ir bet kuriuo atveju ne ilgiau kaip šešis mėnesius; šis laikotarpis gali būti pratęstas dar šešių mėnesių laikotarpiu, jei tiekėjas ar galimas tiekėjas apie tai iš anksto praneša rinkos priežiūros institucijai ir pateikia tokio pratęsimo poreikio paaiškinimą;

g) 

bandymo realiomis sąlygomis subjektai, kurie yra asmenys dėl savo amžiaus, fizinės ar psichinės negalios priskiriami pažeidžiamoms grupėms, yra tinkamai apsaugoti;

h) 

jeigu tiekėjas arba galimas tiekėjas bandymą realiomis sąlygomis organizuoja bendradarbiaudamas su vienu ar daugiau diegėjų arba galimų diegėjų, pastarieji yra informuoti apie visus bandymo aspektus, kurie yra aktualūs jų sprendimui dalyvauti, ir jiems yra pateikti 13 straipsnyje nurodyti atitinkami nurodymai dėl DI sistemos naudojimo; tiekėjas arba galimas tiekėjas ir diegėjas ar galimas diegėjas sudaro susitarimą, kuriame nurodomi jų vaidmenys ir atsakomybė siekiant užtikrinti, kad būtų laikomasi nuostatų dėl bandymo realiomis sąlygomis pagal šį reglamentą ir kitus taikytinus Sąjungos ir nacionalinės teisės aktus;

i) 

bandymo realiomis sąlygomis subjektai yra davę informuoto asmens sutikimą pagal 61 straipsnį arba, teisėsaugos atveju, kai prašant informuoto asmens sutikimo būtų užkirstas kelias DI sistemos bandymui, pats bandymas ir bandymo realiomis sąlygomis rezultatai nedaro neigiamo poveikio subjektams, o jų asmens duomenys užbaigus bandymą yra ištrinami;

j) 

bandymą realiomis sąlygomis tiekėjas arba galimas tiekėjas, taip pat diegėjai arba galimi diegėjai veiksmingai prižiūri, pasitelkdami asmenis, kurie turi tinkamą kvalifikaciją atitinkamoje srityje ir turi savo užduotims atlikti reikiamų gebėjimų, yra tam parengti ir turi atitinkamus įgaliojimus;

k) 

DI sistemos sugeneruotos predikcijos, rekomendacijos ar sprendimai gali būti veiksmingai atšaukti arba į juos gali būti neatsižvelgiama.
60 straipsnis 5.
Bandymo realiomis sąlygomis subjektai arba atitinkamai jų teisėtai paskirtai atstovai, nepatirdami jokios žalos ir neprivalėdami pateikti jokio pagrindimo, gali bet kuriuo metu pasitraukti iš bandymo atšaukdami savo informuoto asmens sutikimą ir gali reikalauti, kad jų asmens duomenys būtų nedelsiant ištrinti. Informuoto asmens sutikimo atšaukimas neturi įtakos jau atliktai veiklai.
60 straipsnis 6.
Pagal 75 straipsnį valstybės narės savo rinkos priežiūros institucijoms suteikia įgaliojimus reikalauti, kad tiekėjai ir galimi tiekėjai pateiktų informaciją, atlikti nuotolinius neplaninius patikrinimus arba patikrinimus vietoje ir patikrinimus, kaip vyksta bandymas realiomis sąlygomis, taip pat patikrinti susijusias didelės rizikos DI sistemas. Rinkos priežiūros institucijos naudojasi tais įgaliojimais, kad užtikrintų saugų bandymų realiomis sąlygomis plėtojimą.
60 straipsnis 7.
Apie visus rimtus incidentus, nustatytus atliekant bandymą realiomis sąlygomis, pagal šio reglamento 73 straipsnį pranešama nacionalinei rinkos priežiūros institucijai. Tiekėjas arba galimas tiekėjas imasi neatidėliotinų rizikos sumažinimo priemonių arba, kai tai neįmanoma, sustabdo bandymą realiomis sąlygomis, kol tokia rizika bus sumažinta, arba kitu atveju jį nutraukia. Tiekėjas arba galimas tiekėjas nustato procedūrą, pagal kurią DI sistema būtų nedelsiant atšaukta, jei toks bandymas realiomis sąlygomis būtų nutrauktas.
60 straipsnis 8.
Tiekėjai arba galimi tiekėjai valstybės narės, kurioje turi būti atliekamas bandymas realiomis sąlygomis, nacionalinei rinkos priežiūros institucijai praneša apie bandymo realiomis sąlygomis sustabdymą arba nutraukimą ir galutinius rezultatus.
60 straipsnis 9.
Tiekėjai arba galimi tiekėjai pagal taikytinus Sąjungos ir nacionalinės teisės aktus, kuriais reglamentuojama atsakomybė, yra atsakingi už žalą, padarytą jų atliekamo bandymo realiomis sąlygomis metu.

1.1 Nuorodos

1.2 Nustatyti reikalavimai

1.3 Susiję standartai

2. Nustatyti reikalavimai

Reikalavimai

3. Susiję standartai

Standartai
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.