+27 artikla Suuririskisten tekoälyjärjestelmien perusoikeusvaikutusten arviointi
---+27 artikla 1.
---+27 artikla 2.
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---+27 artikla 5.
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27 artikla Suuririskisten tekoälyjärjestelmien perusoikeusvaikutusten arviointi
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1. Yleiskatsaus
| Otsikko |
Sääntely |
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27 artikla 1.
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Käyttöönottajien, jotka ovat julkisoikeudellisia laitoksia tai julkisia palveluja tarjoavia yksityisiä yhteisöjä, ja liitteessä III olevassa 5 kohdan b ja c alakohdassa tarkoitettujen suuririskisten tekoälyjärjestelmien käyttöönottajien on tehtävä arviointi tällaisen järjestelmän käytön mahdollisista perusoikeusvaikutuksista ennen 6 artiklan 2 kohdassa tarkoitetun suuririskisen tekoälyjärjestelmän käyttöönottoa, lukuun ottamatta suuririskisiä tekoälyjärjestelmiä, jotka on tarkoitettu käytettäviksi liitteessä III olevassa 2 kohdassa luetellulla alueella. Tätä varten käyttöönottajien on suoritettava arviointi, joka sisältää seuraavat:
a)
kuvaus käyttöönottajan prosesseista, joissa suuririskistä tekoälyjärjestelmää käytetään käyttötarkoituksensa mukaisesti;
b)
kuvaus ajanjaksosta, jonka kuluessa kutakin suuririskistä tekoälyjärjestelmää on tarkoitus käyttää, ja kuinka usein;
c)
niiden luonnollisten henkilöiden ja ryhmien luokat, joihin järjestelmän käyttö todennäköisesti vaikuttaa;
d)
erityiset sellaisen haitan riskit, joka todennäköisesti kohdistuu tämän kohdan c alakohdan mukaisesti yksilöityihin luokkiin kuuluviin luonnollisiin henkilöihin tai henkilöryhmiin, ottaen huomioon tarjoajan 13 artiklan mukaisesti antamat tiedot;
e)
kuvaus ihmisen suorittamien valvontatoimenpiteiden toteuttamisesta käyttöohjeiden mukaisesti;
f)
toimenpiteet, jotka on toteutettava kyseisten riskien toteutuessa, mukaan lukien sisäistä hallintoa ja valitusmekanismeja koskevat järjestelyt.
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27 artikla 2.
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Edellä 1 kohdassa säädettyä velvoitetta sovelletaan suuririskisen tekoälyjärjestelmän ensimmäiseen käyttökertaan. Käyttöönottaja voi samanlaisissa tapauksissa tukeutua aiemmin tehtyyn perusoikeusvaikutusten arviointiin tai tarjoajan tekemään olemassa olevaan vaikutustenarviointiin. Jos käyttöönottaja katsoo suuririskisen tekoälyjärjestelmän käytön aikana, että jokin 1 kohdassa luetelluista elementeistä on muuttunut tai ei ole enää ajan tasalla, käyttöönottajan on toteutettava tarvittavat toimenpiteet tietojen päivittämiseksi.
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27 artikla 3.
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Kun tämän artiklan 1 kohdassa tarkoitettu arviointi on suoritettu, käyttöönottajan on ilmoitettava markkinavalvontaviranomaiselle sen tuloksista, ja palautettava tämän artiklan 5 kohdassa tarkoitettu täytetty lomake ilmoituksen osana. Käyttöönottajat voidaan 46 artiklan 1 kohdassa tarkoitetussa tapauksessa vapauttaa tästä ilmoitusvelvoitteesta.
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27 artikla 4.
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Jos jokin tässä artiklassa säädetyistä velvoitteista on jo täytetty asetuksen (EU) 2016/679 35 artiklan tai direktiivin (EU) 2016/680 27 artiklan nojalla suoritetun tietosuojaa koskevan vaikutustenarvioinnin avulla, käyttöönottaja voi tämän artiklan 1 kohdassa tarkoitettua perusoikeuksia koskevaa vaikutustenarviointia tehdessään sisällyttää siihen ristiinviittauksia kyseisen tietosuojaa koskevan vaikutustenarvioinnin asiaankuuluviin jaksoihin tai sisällyttää sen asiaankuuluvia osia perusoikeuksia koskevaan vaikutustenarviointiin.
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27 artikla 5.
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Tekoälytoimisto laatii mallin kyselylomakkeelle, myös automaattisen välineen avulla, jotta käyttöönottajia voidaan auttaa täyttämään tämän artiklan mukaiset velvoitteensa yksinkertaistetulla tavalla. Tässä mallissa on tapauksen mukaan annettava käyttöönottajille mahdollisuus sisällyttää siihen ristiinviittauksia tietosuojaa koskevan vaikutustenarvioinnin asiaankuuluviin jaksoihin tai sisällyttää sen asiaankuuluvia osia 4 kohdan mukaiseen perusoikeuksia koskevaan vaikutustenarviointiin.
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1.1 Viittaukset
1.2 Tunnistetut vaatimukset
1.3 Liittyvät standardit
- AI & Autonomous Technologies Impact Assessment (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)
2. Tunnistetut vaatimukset
Vaatimukset
| Lähde |
Vaatimus |
3. Liittyvät standardit
Standardit
| Lähde |
Vaatimus |
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SCF
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AI & Autonomous Technologies Impact Assessment
Description
Mechanisms exist to assess the impact(s) of proposed Artificial Intelligence (AI) and Autonomous Technologies (AAT) on individuals, groups, communities, organizations and society (e.g., Fundamental Rights Impact Assessment (FRIA)).
Possible Solutions & Considerations
Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2
∙ Basic AI impact assessment (documented impact on people, processes, decisions)
∙ AI governance program
∙ NIST AI RMF Map function
Small Business (10-49 staff) / BLS Firm Size Classes 3-4
∙ Formal AI impact assessment for each deployed system
∙ AI governance program
∙ NIST AI RMF Map function
Medium Business (50-249 staff) / BLS Firm Size Classes 5-6
∙ AI impact assessment process aligned to NIST AI RMF
∙ AI governance program
∙ Algorithmic Impact Assessment (AIA) for higher-risk systems
Large Business (250-999 staff) / BLS Firm Size Classes 7-8
∙ Formal AI impact assessment program with independent review for high-risk systems
∙ NIST AI RMF Map function
∙ Algorithmic Impact Assessment (AIA) process
∙ EU AI Act conformity assessment requirements (if applicable)
Enterprise (> 1,000 staff) / BLS Firm Size Class 9
∙ Enterprise AI impact assessment framework (NIST AI RMF, EU AI Act)
∙ Third-party conformity assessments for high-risk AI (EU AI Act Article 43)
∙ Algorithmic Impact Assessment (AIA) program
∙ Board-level AI impact reporting
SCR-CMM
Level 0 Not Performed
Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.
Level 1 Performed Informally
Artificial Intelligence and Autonomous Technology (AAT) domain capabilities are ad hoc and inconsistent. Capability criteria associated with this control may include:
▪ Policies, standards & procedures associated with AAT domain capabilities provide limited coverage due to the depth and breadth of the existing documentation.
▪ AAT-related processes are expected to follow the organization's existing processes (e.g., incident response, asset management, change control and risk assessments).
▪ No formal Governance, Risk & Compliance (GRC) team exists to provide AAT oversight, where the Chief Information Officer (CIO), or similar function, governs technology decisions what is acceptable for AAT within the organization.
Level 2 Planned Tracked
Artificial Intelligence and Autonomous Technology (AAT) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity.
▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are documented and maintained by process owners.
▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD).
▪ Artificial Intelligence (AI)-related controls are primarily administrative and preventative in nature (e.g., policies, standards and procedures & guidelines).
▪ Asset management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.
Level 3 Well Defined
Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist:
▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function.
▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners.
▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities.
▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform).
▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls).
▪ An implemented and operational capability exists to assess the impact(s) of proposed AAT on individuals, groups, communities, organizations and society (e.g., Fundamental Rights Impact Assessment (FRIA)).
Level 4 Quantitatively Controlled
Utilize SCR-CMM Level 3 criteria definitions:
▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control.
▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define.
Level 5 Continuously Improving
Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions:
▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control.
▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies.
▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
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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
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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
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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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