+Άρθρο 60 Δοκιμή συστημάτων ΤΝ υψηλού κινδύνου σε πραγματικές συνθήκες εκτός ρυθμιστικών δοκιμαστηρίων ΤΝ
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Άρθρο 60 Δοκιμή συστημάτων ΤΝ υψηλού κινδύνου σε πραγματικές συνθήκες εκτός ρυθμιστικών δοκιμαστηρίων ΤΝ

1. Επισκόπηση

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Τίτλος	Κανονισμός
Άρθρο 60 1.	Η δοκιμή των συστημάτων ΤΝ υψηλού κινδύνου σε πραγματικές συνθήκες εκτός ρυθμιστικών δοκιμαστηρίων ΤΝ μπορεί να διενεργείται από παρόχους ή μελλοντικούς παρόχους συστημάτων ΤΝ υψηλού κινδύνου που απαριθμούνται στο παράρτημα III ή καλύπτονται από την ενωσιακή νομοθεσία εναρμόνισης που παρατίθεται στο παράρτημα I τμήμα Α, σύμφωνα με το παρόν άρθρο και το σχέδιο δοκιμής σε πραγματικές συνθήκες που αναφέρεται στο παρόν άρθρο, με την επιφύλαξη των απαγορεύσεων του άρθρου 5. Η Επιτροπή προσδιορίζει, μέσω εκτελεστικών πράξεων, τα λεπτομερή στοιχεία του σχεδίου δοκιμής σε πραγματικές συνθήκες. Οι εν λόγω εκτελεστικές πράξεις εκδίδονται σύμφωνα με τη διαδικασία εξέτασης που αναφέρεται στο άρθρο 98 παράγραφος 2. Η παρούσα παράγραφος δεν θίγει το ενωσιακό ή το εθνικό δίκαιο ως προς τη δοκιμή σε πραγματικές συνθήκες συστημάτων ΤΝ υψηλού κινδύνου που σχετίζονται με προϊόντα τα οποία καλύπτονται από την ενωσιακή νομοθεσία εναρμόνισης που παρατίθεται στο παράρτημα I.
Άρθρο 60 2.	Οι πάροχοι ή οι μελλοντικοί πάροχοι μπορούν να διενεργούν τη δοκιμή συστημάτων ΤΝ υψηλού κινδύνου που αναφέρονται στο παράρτημα III ή καλύπτονται από την ενωσιακή νομοθεσία εναρμόνισης που παρατίθεται στο παράρτημα I τμήμα Α σε πραγματικές συνθήκες ανά πάσα στιγμή πριν από τη διάθεση στην αγορά ή τη θέση σε λειτουργία του συστήματος ΤΝ υψηλού κινδύνου, είτε μόνοι τους είτε σε συνεργασία με έναν ή περισσότερους φορείς εφαρμογής ή μελλοντικούς φορείς εφαρμογής.
Άρθρο 60 3.	Η δοκιμή συστημάτων ΤΝ υψηλού κινδύνου σε πραγματικές συνθήκες δυνάμει του παρόντος άρθρου δεν θίγει την ενδεχόμενη δεοντολογική εξέταση που απαιτείται από το ενωσιακό ή το εθνικό δίκαιο.
Άρθρο 60 4.	Οι πάροχοι ή οι μελλοντικοί πάροχοι μπορούν να διενεργούν τη δοκιμή σε πραγματικές συνθήκες μόνο εφόσον πληρούνται όλες οι ακόλουθες προϋποθέσεις: α) ο πάροχος ή ο μελλοντικός πάροχος έχει καταρτίσει σχέδιο δοκιμής σε πραγματικές συνθήκες και το έχει υποβάλει στην αρχή εποπτείας της αγοράς στο κράτος μέλος όπου πρόκειται να διεξαχθεί η δοκιμή σε πραγματικές συνθήκες· β) η αρχή εποπτείας της αγοράς στο κράτος μέλος όπου πρόκειται να διεξαχθεί η δοκιμή σε πραγματικές συνθήκες έχει εγκρίνει τη δοκιμή σε πραγματικές συνθήκες και το σχέδιο δοκιμής σε πραγματικές συνθήκες· όταν η αρχή εποπτείας της αγοράς δεν έχει δώσει απάντηση εντός 30 ημερών, η δοκιμή σε πραγματικές συνθήκες και το σχέδιο δοκιμής σε πραγματικές συνθήκες θεωρείται ότι έχουν εγκριθεί· όταν το εθνικό δίκαιο δεν προβλέπει σιωπηρή έγκριση, η δοκιμή σε πραγματικές συνθήκες εξακολουθεί να υπόκειται στη λήψη άδειας· γ) ο πάροχος ή ο μελλοντικός πάροχος, με εξαίρεση τους παρόχους ή τους μελλοντικούς παρόχους συστημάτων ΤΝ υψηλού κινδύνου που αναφέρονται στο παράρτημα ΙΙΙ σημεία 1, 6 και 7 στους τομείς της επιβολής του νόμου, της μετανάστευσης, του ασύλου και της διαχείρισης των συνοριακών ελέγχων και των συστημάτων ΤΝ υψηλού κινδύνου που αναφέρονται στο παράρτημα ΙΙΙ σημείο 2 έχει καταχωρίσει τη δοκιμή σε πραγματικές συνθήκες σύμφωνα με το άρθρο 71 παράγραφος 4 με μοναδικό ανά την Ένωση ενιαίο αριθμό αναγνώρισης και με τις πληροφορίες που ορίζονται στο παράρτημα ΙΧ· ο πάροχος ή ο μελλοντικός πάροχος συστημάτων ΤΝ υψηλού κινδύνου που αναφέρονται στο παράρτημα ΙΙΙ σημεία 1, 6 και 7 στους τομείς της επιβολής του νόμου, της μετανάστευσης, του ασύλου και της διαχείρισης των συνοριακών ελέγχων έχει καταχωρίσει τη δοκιμή σε πραγματικές συνθήκες στο ασφαλές μη δημόσιο τμήμα της βάσης δεδομένων της ΕΕ σύμφωνα με το άρθρο 49 παράγραφος 4 στοιχείο δ), με μοναδικό ανά την Ένωση ενιαίο αριθμό αναγνώρισης και με τις πληροφορίες που ορίζονται εκεί· ο πάροχος ή ο μελλοντικός πάροχος συστημάτων ΤΝ υψηλού κινδύνου που αναφέρονται στο παράρτημα ΙΙΙ σημείο 2 έχει καταχωρίσει τη δοκιμή σε πραγματικές συνθήκες σύμφωνα με το άρθρο 49 παράγραφος 5· δ) ο πάροχος ή ο μελλοντικός πάροχος που διενεργεί τη δοκιμή σε πραγματικές συνθήκες είναι εγκατεστημένος στην Ένωση ή έχει ορίσει νόμιμο εκπρόσωπο ο οποίος είναι εγκατεστημένος στην Ένωση· ε) τα δεδομένα που συλλέγονται και υποβάλλονται σε επεξεργασία για τον σκοπό της δοκιμής σε πραγματικές συνθήκες διαβιβάζονται σε τρίτες χώρες μόνο εφόσον εφαρμόζονται κατάλληλες και ισχύουσες εγγυήσεις δυνάμει του ενωσιακού δικαίου· στ) η δοκιμή σε πραγματικές συνθήκες δεν διαρκεί περισσότερο από όσο είναι αναγκαίο για την επίτευξη των στόχων της και, σε κάθε περίπτωση, όχι πάνω από έξι μήνες, διάστημα που μπορεί να παραταθεί για επιπλέον περίοδο έξι μηνών, με την επιφύλαξη προηγούμενης κοινοποίησης από τον πάροχο ή τον μελλοντικό πάροχο στην αρχή εποπτείας της αγοράς, συνοδευόμενης από επεξήγηση της ανάγκης για την παράταση αυτή· ζ) τα υποκείμενα της δοκιμής σε πραγματικές συνθήκες που είναι πρόσωπα που ανήκουν σε ευάλωτες ομάδες λόγω της ηλικίας ή της αναπηρίας τους προστατεύονται δεόντως· η) όταν ένας πάροχος ή μελλοντικός πάροχος οργανώνει τη δοκιμή σε πραγματικές συνθήκες σε συνεργασία με έναν ή περισσότερους φορείς εφαρμογής ή μελλοντικούς φορείς εφαρμογής, οι τελευταίοι έχουν ενημερωθεί για όλες τις πτυχές της δοκιμής που βαρύνουν στην απόφασή τους να συμμετάσχουν και έχουν λάβει τις σχετικές οδηγίες χρήσης του συστήματος ΤΝ που αναφέρονται στο άρθρο 13· ο πάροχος ή ο μελλοντικός πάροχος και ο φορέας εφαρμογής ή ο μελλοντικός φορέας εφαρμογής συνάπτουν συμφωνία στην οποία καθορίζονται οι ρόλοι και οι ευθύνες τους με σκοπό την εξασφάλιση της συμμόρφωσης με τις διατάξεις για τις δοκιμές σε πραγματικές συνθήκες δυνάμει του παρόντος κανονισμού και άλλου εφαρμοστέου κανόνα ενωσιακού και εθνικού δικαίου· θ) τα υποκείμενα της δοκιμής σε πραγματικές συνθήκες έχουν δώσει συναίνεση έπειτα από ενημέρωση σύμφωνα με το άρθρο 61 ή, στην περίπτωση του τομέα επιβολής του νόμου, όταν η επιδίωξη συναίνεσης έπειτα από ενημέρωση θα εμπόδιζε το σύστημα ΤΝ να υποβληθεί σε δοκιμή, η ίδια η δοκιμή και το αποτέλεσμα της δοκιμής σε πραγματικές συνθήκες δεν έχουν αρνητικές επιπτώσεις στα υποκείμενα και τα σχετικά δεδομένα προσωπικού χαρακτήρα διαγράφονται μετά τη διενέργεια της δοκιμής· ι) η δοκιμή σε πραγματικές συνθήκες εποπτεύεται αποτελεσματικά από τον πάροχο ή τον μελλοντικό πάροχο, καθώς και από τους φορείς εφαρμογής ή τους μελλοντικούς φορείς εφαρμογής, μέσω προσώπων που διαθέτουν τα κατάλληλα προσόντα στον σχετικό τομέα, καθώς και την αναγκαία ικανότητα, κατάρτιση και εξουσία για την εκτέλεση των καθηκόντων τους· ια) οι προβλέψεις, συστάσεις ή αποφάσεις του συστήματος ΤΝ μπορούν κατ’ ουσίαν να αναιρεθούν και να αγνοηθούν.
Άρθρο 60 5.	Οποιαδήποτε υποκείμενα της δοκιμής σε πραγματικές συνθήκες ή οι νομίμως ορισμένοι εκπρόσωποί τους, κατά περίπτωση, μπορούν, χωρίς να υποστούν εξ αυτού καμία ζημία και χωρίς να χρειάζεται να παράσχουν οποιαδήποτε αιτιολόγηση, να αποσυρθούν από τη δοκιμή ανά πάσα στιγμή, αφού ανακαλέσουν τη συναίνεσή τους έπειτα από ενημέρωση, και μπορούν να ζητήσουν την άμεση και μόνιμη διαγραφή των δεδομένων προσωπικού χαρακτήρα που τα αφορούν. Η ανάκληση της συναίνεσης έπειτα από ενημέρωση δεν επηρεάζει τις δραστηριότητες που έχουν ήδη διεξαχθεί.
Άρθρο 60 6.	Σύμφωνα με το άρθρο 75, τα κράτη μέλη αναθέτουν στις οικείες αρχές εποπτείας της αγοράς την εξουσία να ζητούν από τους παρόχους και τους μελλοντικούς παρόχους να παρέχουν πληροφορίες, να διενεργούν αιφνιδιαστικές εξ αποστάσεως ή επιτόπιες επιθεωρήσεις και να διενεργούν ελέγχους σχετικά με τη διενέργεια δοκιμών σε πραγματικές συνθήκες και τα σχετικά συστήματα ΤΝ υψηλού κινδύνου. Οι αρχές εποπτείας της αγοράς χρησιμοποιούν τις εν λόγω εξουσίες για να διασφαλίσουν την ασφαλή ανάπτυξη των δοκιμών σε πραγματικές συνθήκες.
Άρθρο 60 7.	Κάθε σοβαρό περιστατικό που διαπιστώνεται κατά τη διάρκεια της δοκιμής σε πραγματικές συνθήκες αναφέρεται στην εθνική αρχή εποπτείας της αγοράς σύμφωνα με το άρθρο 73. Ο πάροχος ή ο μελλοντικός πάροχος λαμβάνει άμεσα μέτρα μετριασμού ή, ελλείψει αυτών, αναστέλλει τη δοκιμή σε πραγματικές συνθήκες, έως ότου επέλθει ο εν λόγω μετριασμός, ή, διαφορετικά, την τερματίζει. Ο πάροχος ή ο μελλοντικός πάροχος καθορίζει διαδικασία για την άμεση ανάκληση του συστήματος ΤΝ μετά τον εν λόγω τερματισμό της δοκιμής σε πραγματικές συνθήκες.
Άρθρο 60 8.	Οι πάροχοι ή οι μελλοντικοί πάροχοι κοινοποιούν στην εθνική αρχή εποπτείας της αγοράς στο κράτος μέλος όπου πρόκειται να διενεργηθεί η δοκιμή σε πραγματικές συνθήκες την αναστολή ή τον τερματισμό της δοκιμής σε πραγματικές συνθήκες και τα τελικά αποτελέσματα.
Άρθρο 60 9.	Ο πάροχος ή ο μελλοντικός πάροχος είναι υπεύθυνοι, σύμφωνα με το εφαρμοστέο ενωσιακό και εθνικό δίκαιο περί ευθύνης, για κάθε ζημία που προκαλείται κατά τη διάρκεια της δοκιμής τους σε πραγματικές συνθήκες.

1.1 Παραπομπές

1.2 Εντοπισμένες απαιτήσεις

1.3 Σχετικά πρότυπα

2. Εντοπισμένες απαιτήσεις

Απαιτήσεις
Πηγή	Απαίτηση

3. Σχετικά πρότυπα

Πρότυπα
Πηγή	Απαίτηση
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) Description Mechanisms exist to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Information Assurance (IA) Program ∙ AI TEVV checklist for AI tools (test accuracy, validate outputs, verify security) ∙ AI governance program Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV process for AI systems ∙ AI governance program Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Information Assurance (IA) Program ∙ Formal AI TEVV framework aligned to NIST AI RMF Measure function ∙ AI testing tools (e.g., IBM OpenScale, Great Expectations) ∙ AI governance program Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program ∙ NIST AI RMF Measure function ∙ Third-party AI testing for high-risk systems ∙ AI testing integrated with CI/CD pipelines Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Information Assurance (IA) Program ∙ Enterprise AI TEVV program (NIST AI RMF Measure function) ∙ Independent AI testing and evaluation for high-risk systems ∙ AI TEVV integrated with MLOps and CI/CD ∙ EU AI Act conformity assessment (if applicable) SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to implement Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) practices to enable AAT-related security, resilience and compliance-related conformity testing throughout the lifecycle of the AAT. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	AI TEVV Reporting Description Mechanisms exist to report the status and results of Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) to relevant stakeholders, including governing bodies, as required. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Report AI testing results to management Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Document and communicate AI test results to stakeholders Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Formal AI TEVV reporting process ∙ Standardized reporting templates Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ AI TEVV reporting program ∙ Regular stakeholder briefings ∙ Findings tracking Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Enterprise AI TEVV reporting framework ∙ Automated test result dashboards ∙ Board-level AI risk reporting SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to report the status and results of Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) to relevant stakeholders, including governing bodies, as required. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	AI TEVV Reporting Description Mechanisms exist to report the status and results of Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) to relevant stakeholders, including governing bodies, as required. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Report AI testing results to management Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Document and communicate AI test results to stakeholders Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Formal AI TEVV reporting process ∙ Standardized reporting templates Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ AI TEVV reporting program ∙ Regular stakeholder briefings ∙ Findings tracking Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Enterprise AI TEVV reporting framework ∙ Automated test result dashboards ∙ Board-level AI risk reporting SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality. Level 2 Planned Tracked SCR-CMM Level 2 criteria definitions are not available for this control: ▪ A reasonable person would conclude a well-defined and standardized process is required. ▪ At this level of maturity, the “requirements-driven” nature of performing the control is focused on a localized and/or regionalized implementation, not uniform and consistent across the organization. ▪ Requirements are narrowly scoped for applicability and are primarily derived from compliance obligations (e.g., laws, regulations and contracts). Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to report the status and results of Artificial Intelligence Test, Evaluation, Validation & Verification (AI TEVV) to relevant stakeholders, including governing bodies, as required. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
SCF	Serious Incident Reporting For AI & Autonomous Technologies Description Mechanisms exist to report any serious incident involving operational Artificial Intelligence (AI) and Autonomous Technologies (AAT) to relevant authorities as to when and where the serious incident occurred, in accordance with mandated reporting timelines. Possible Solutions & Considerations Micro-Small Business (<10 staff) / BLS Firm Size Classes 1-2 ∙ Report serious AI incidents to relevant authorities as required Small Business (10-49 staff) / BLS Firm Size Classes 3-4 ∙ Incident reporting procedure for serious AI incidents Medium Business (50-249 staff) / BLS Firm Size Classes 5-6 ∙ Formal serious AI incident reporting process ∙ Regulatory reporting contacts Large Business (250-999 staff) / BLS Firm Size Classes 7-8 ∙ AI incident response plan with regulatory reporting requirements ∙ Legal team involvement Enterprise (> 1,000 staff) / BLS Firm Size Class 9 ∙ Enterprise AI incident response program ∙ Automated regulatory notification workflows ∙ Legal and compliance integration SCR-CMM Level 0 Not Performed Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed. Level 1 Performed Informally Artificial Intelligence and Autonomous Technology (AAT) domain capabilities are ad hoc and inconsistent. Capability criteria associated with this control may include: ▪ Policies, standards & procedures associated with AAT domain capabilities provide limited coverage due to the depth and breadth of the existing documentation. ▪ AAT-related processes are expected to follow the organization's existing processes (e.g., incident response, asset management, change control and risk assessments). ▪ No formal Governance, Risk & Compliance (GRC) team exists to provide AAT oversight, where the Chief Information Officer (CIO), or similar function, governs technology decisions what is acceptable for AAT within the organization. Level 2 Planned Tracked Artificial Intelligence and Autonomous Technology (AAT) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are documented and maintained by process owners. ▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD). ▪ Artificial Intelligence (AI)-related controls are primarily administrative and preventative in nature (e.g., policies, standards and procedures & guidelines). ▪ Asset management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel. Level 3 Well Defined Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to report any serious incident involving operational AAT to relevant authorities as to when and where the serious incident occurred, in accordance with mandated reporting timelines. Level 4 Quantitatively Controlled Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define. Level 5 Continuously Improving Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.

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