+60. pants Augsta riska MI sistēmu testēšana reālos apstākļos ārpus MI “regulatīvajām smilškastēm”
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60. pants Augsta riska MI sistēmu testēšana reālos apstākļos ārpus MI “regulatīvajām smilškastēm”
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1. Pārskats
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Regulējums |
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60. pants 1.
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Augsta riska MI sistēmu testēšanu reālos apstākļos ārpus MI regulatīvajām smilškastēm var veikt III pielikumā uzskaitīto vai I pielikuma A iedaļā uzskaitīto Savienības saskaņošanas tiesību aktu aptverto augsta riska MI sistēmu nodrošinātāji vai potenciālie nodrošinātāji, ievērojot šā panta prasības un šajā pantā minēto testēšanas reālos apstākļos plānu un neskarot 5. pantā paredzētos aizliegumus. Komisija ar īstenošanas aktiem precizē testēšanas reālos apstākļos plāna detalizētus elementus. Minētos īstenošanas aktus pieņem saskaņā ar pārbaudes procedūru, kas minēta 98. panta 2. punktā. Šis punkts neskar Savienības vai valsts tiesību aktus par tādu augsta riska MI sistēmu testēšanu reālos apstākļos, kas saistītas ar produktiem, uz kuriem attiecas I pielikumā uzskaitītie Savienības saskaņošanas tiesību akti.
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60. pants 2.
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Nodrošinātāji vai potenciālie nodrošinātāji III pielikumā minēto vai I pielikuma A iedaļā uzskaitīto Savienības saskaņošanas tiesību aktu aptverto augsta riska MI sistēmu testēšanu reālos apstākļos var veikt jebkurā laikā pirms augsta riska MI sistēmas laišanas tirgū vai nodošanas ekspluatācijā paši vai partnerībā ar vienu vai vairākiem uzturētājiem vai potenciālajiem uzturētājiem.
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60. pants 3.
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Augsta riska MI sistēmu testēšana reālos apstākļos saskaņā ar šo pantu neskar jebkādu ētiskuma izvērtēšanu, kas prasīta Savienības vai valsts tiesību aktos.
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60. pants 4.
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Nodrošinātāji vai potenciālie nodrošinātāji var veikt testēšanu reālos apstākļos tikai tad, ja ir izpildīti visi turpmāk minētie nosacījumi:
a)
nodrošinātājs vai potenciālais nodrošinātājs ir izstrādājis testēšanas reālos apstākļos plānu un iesniedzis to tirgus uzraudzības iestādei dalībvalstī, kurā paredzēts veikt testēšanu reālos apstākļos;
b)
tirgus uzraudzības iestāde dalībvalstī, kurā paredzēts veikt testēšanu reālos apstākļos, ir apstiprinājusi testēšanu reālos apstākļos un testēšanas reālos apstākļos plānu; ja tirgus uzraudzības iestāde nav sniegusi atbildi 30 dienu laikā, uzskata, ka testēšana reālos apstākļos un testēšanas reālos apstākļos plāns ir apstiprināts; ja valsts tiesību akti neparedz automātisku apstiprinājumu, testēšanai reālos apstākļos aizvien ir nepieciešama atļauja;
c)
nodrošinātājs vai potenciālais nodrošinātājs, izņemot III pielikuma 1., 6. un 7. punktā minēto augsta riska MI sistēmu tiesībaizsardzības, migrācijas, patvēruma un robežkontroles pārvaldības jomā un III pielikuma 2. punktā minēto augsta riska MI sistēmu nodrošinātājus vai potenciālos nodrošinātājus, ir reģistrējis testēšanu reālos apstākļos saskaņā ar 71. panta 4. punktu ar unikālu Savienības mēroga vienoto identifikācijas numuru un iesniedzis IX pielikumā norādīto informāciju; III pielikuma 1., 6. un 7. punktā minēto augsta riska MI sistēmu nodrošinātājs vai potenciālais nodrošinātājs tiesībaizsardzības, migrācijas, patvēruma un robežkontroles pārvaldības jomā ir reģistrējis testēšanu reālos apstākļos ES datubāzes drošajā nepubliskajā iedaļā saskaņā ar 49. panta 4. punkta d) apakšpunktu ar unikālu Savienības mēroga vienoto identifikācijas numuru un tajā norādīto informāciju; III pielikuma 2. punktā minētais augsta riska AI sistēmu nodrošinātājs vai potenciālais nodrošinātājs ir reģistrējis testēšanu reālos apstākļos saskaņā ar 49. panta 5. punktu;
d)
nodrošinātājs vai potenciālais nodrošinātājs, kas veic testēšanu reālos apstākļos, ir iedibināts Savienībā vai ir iecēlis juridisko pārstāvi, kurš ir iedibināts Savienībā;
e)
datus, kas savākti un apstrādāti testēšanas vajadzībām reālos apstākļos, pārsūta uz trešām valstīm tikai tad, ja ir īstenoti atbilstīgi un piemērojami aizsardzības pasākumi, kas paredzēti Savienības tiesību aktos;
f)
testēšana reālos apstākļos neilgst ilgāk, kā nepieciešams, lai sasniegtu tās mērķus, un jebkurā gadījumā ne ilgāk kā sešus mēnešus, ko var pagarināt vēl par sešiem mēnešiem, ja nodrošinātājs vai potenciālais nodrošinātājs par to iepriekš paziņo tirgus uzraudzības iestādei, pievienojot paskaidrojumu, kādēļ nepieciešams šāds pagarinājums;
g)
testēšanas reālos apstākļos subjekti, kas ir personas, kuras pieder pie neaizsargātas grupām to vecuma vai garīgās invaliditātes dēļ, ir pienācīgi aizsargāti;
h)
ja nodrošinātājs vai potenciālais nodrošinātājs organizē testēšanu reālos apstākļos sadarbībā ar vienu vai vairākiem uzturētājiem vai potenciālajiem uzturētājiem, pēdējie minētie ir informēti par visiem testēšanas aspektiem, kas ir būtiski, pieņemot lēmumu par dalību, un tiem ir sniegta attiecīgā MI sistēmas lietošanas instrukcija, kas minēta 13. pantā; nodrošinātājs vai potenciālais nodrošinātājs un potenciālais uzturētājs noslēdz vienošanos, kurā precizē savas lomas un pienākumus, lai nodrošinātu atbilstību noteikumiem par testēšanu reālos apstākļos saskaņā ar šo regulu un citiem piemērojamiem Savienības un valsts tiesību aktiem;
i)
subjekti, kas piedalās testēšanā reālos apstākļos, ir devuši informētu piekrišanu saskaņā ar 61. pantu, vai tiesībaizsardzības gadījumos, ja informētas piekrišanas iegūšana kavētu MI sistēmas testēšanu, pašai testēšanai un testēšanas rezultātiem reālos apstākļos nav nekādas negatīvas ietekmes uz subjektiem un pēc testa veikšanas viņu personas datus dzēš;
j)
testēšanu reālos apstākļos efektīvi pārrauga nodrošinātājs vai potenciālais nodrošinātājs, kā arī uzturētājs vai potenciālais uzturētājs ar tādu personu starpniecību, kuras ir atbilstoši kvalificētas attiecīgajā jomā un kurām ir vajadzīgās spējas, apmācība un pilnvaras savu uzdevumu veikšanai;
k)
MI sistēmas prognozes, ieteikumus vai lēmumus var faktiski reversēt un neņemt vērā.
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60. pants 5.
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Subjekti, kas piedalās testēšanā reālos apstākļos, vai attiecīgi to likumiski izraudzītais pārstāvis var bez jebkādām nelabvēlīgām sekām un nesniedzot nekādu pamatojumu jebkurā laikā izstāties no testēšanas, atsaucot savu informēto piekrišanu, un var prasīt savu personas datu tūlītēju un neatgriezenisku dzēšanu. Informētas piekrišanas atsaukšana neietekmē jau veiktās darbības.
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60. pants 6.
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Saskaņā ar 75. pantu dalībvalstis piešķir savām tirgus uzraudzības iestādēm pilnvaras pieprasīt, lai nodrošinātāji un potenciālie nodrošinātāji sniegtu informāciju, veikt neizziņotas attālinātas vai uz vietas veiktas inspekcijas un veikt pārbaudes par testēšanas veikšanu reālos apstākļos un ar to saistītajām augsta riska MI sistēmām. Tirgus uzraudzības iestādes izmanto minētās pilnvaras, lai nodrošinātu testēšanas reālos apstākļos drošu izstrādi.
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60. pants 7.
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Par jebkuru nopietnu incidentu, kas identificēts testēšanas reālos apstākļos gaitā, ziņo valsts tirgus uzraudzības iestādei saskaņā ar 73. pantu. Nodrošinātājs vai potenciālais nodrošinātājs pieņem tūlītējus ietekmes mazināšanas pasākumus vai, ja tas nav iespējams, aptur testēšanu reālos apstākļos līdz brīdim, kad notiek šāda mazināšana, vai citādi to izbeidz. Nodrošinātājs vai potenciālais nodrošinātājs izveido procedūru MI sistēmas tūlītējai atsaukšanai pēc šādas testēšanas reālos apstākļos pabeigšanas.
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60. pants 8.
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Nodrošinātāji vai potenciālie nodrošinātāji paziņo valsts tirgus uzraudzības iestādei dalībvalstī, kurā paredzēts veikt testēšanu reālos apstākļos, par testēšanas reālos apstākļos apturēšanu vai izbeigšanu un par galīgajiem iznākumiem.
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60. pants 9.
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Nodrošinātājs vai potenciālais nodrošinātājs saskaņā ar piemērojamiem atbildību reglamentējošiem Savienības un valsts tiesību aktiem ir atbildīgi par jebkādu kaitējumu, kas nodarīts to testēšanā reālos apstākļos.
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1.1 Atsauces
1.2 Identificētās prasības
1.3 Saistītie standarti
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) (SCF)
- AI TEVV Reporting (SCF)
- AI TEVV Reporting (SCF)
- Serious Incident Reporting For AI & Autonomous Technologies (SCF)
2. Identificētās prasības
3. Saistītie standarti
Standarti
| Avots |
Prasība |
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SCF
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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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.
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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.
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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.
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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.
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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SCF
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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.
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