Every standard the
framework builds on.
The SCL AI Requirements Framework is grounded in a defined set of standards, regulations, and reference documents. Each entry below links to the official publisher. No hosted copies. Authoritative sources only. The normative and informative partition below is the one in framework v3.7, Tables 1-9 and 1-10.
These are the external standards the framework builds on. For SCL's own measured verification results on public data, see the measured evidence.
[ Normative Standards ] 3
Documents on which conformity with the framework depends, each to the stated extent of its invocation in Table 1-9 and no further.
ISO/IEC
Normative
22989:2022
AI Concepts and Terminology
Source of foundational definitions used throughout the framework's glossary and core AI/ML terms.
ISO/IEC
Normative
5338:2023
AI System Lifecycle Processes
Lifecycle process model the framework's AI integration with software lifecycle is built against.
IEEE
Normative
1012-2024
System, Software, and Hardware Verification and Validation
Source of the four verification methods (Inspection, Analysis, Demonstration, Test) used in the framework's verification matrix.
[ Reference Documents ] 20
Informative references cited throughout the framework. Domain regulations, lifecycle standards, and supporting guidance that inform requirement interpretation.
NASA
Reference
NPR 7150.2D
NASA Software Engineering Requirements
Software lifecycle reference extended by the framework's AI-specific operations activities.
NASA
Reference
STD-8739.8
Software Assurance and Software Safety Standard
Safety analysis reference for traceable, logically inspectable safety cases. This is one of the gaps AI-8 Explainability addresses.
ISO/IEC
Reference
23053:2022
Framework for AI Systems Using Machine Learning
ML framework concepts including out-of-distribution and overfitting definitions used in AI-3 and AI-6.
ISO/IEC
Reference
TR 24027:2021
Bias in AI Systems and AI-Aided Decision Making
Bias assessment guidance and source of the fairness metrics referenced in AI-2 Addressing AI Bias.
NIST
Reference
AI 600-1
AI RMF: Generative AI Profile
Source of the framework's hallucination definition (confabulation) and generative AI risk treatment.
FAA
Reference
AI Roadmap
FAA Roadmap for AI Safety Assurance
FAA's acknowledged gap in AI certification criteria. The framework provides the precedent the roadmap will reference.
EU
Reference
GDPR
EU General Data Protection Regulation
Personal data processing requirements referenced throughout AI-10 Privacy and Data Protection sub-requirements.
HHS
Reference
HIPAA
HIPAA Privacy and Security Rules
45 CFR 164 Subparts C and E. Protected health information requirements applied to medical AI systems.
CA OAG
Reference
CCPA / CPRA
California Consumer Privacy Act
Cal. Civ. Code §§ 1798.100 et seq. Consumer privacy requirements for AI systems processing California resident data.
NIST
Reference
SP 800-122
Guide to Protecting the Confidentiality of PII
PII handling guidance referenced in the framework's data minimization and de-identification controls.
ISO
Reference
34503
Road Vehicles - Taxonomy for Operational Design Domain
ODD attribute taxonomy from the automotive domain that informs the framework's domain-agnostic ODD declaration in AI-1.0.
NIST
Reference
AI RMF 1.0
AI Risk Management Framework
Risk taxonomy referenced throughout the framework for AI-specific risk identification and management.
RTCA
Reference
DO-178C
Software Considerations in Airborne Systems and Equipment Certification
Aviation software standard that SCL's AI framework sits alongside to address AI failure modes DO-178C does not cover.
ISO
Reference
26262
Road Vehicles - Functional Safety
Automotive functional safety standard. The framework addresses AI failure modes ASIL decomposition cannot resolve.
ISO/IEC
Reference
27701:2019
Privacy Information Management Extension to ISO/IEC 27001
Source of privacy management controls referenced in AI-10 Privacy and Data Protection.
ISO/IEC
Reference
29100:2011
Privacy Framework
Defines privacy principles and terminology that AI-10 sub-requirements align with.
NIST
Reference
Privacy v1.0
NIST Privacy Framework v1.0
Enterprise privacy risk management framework referenced in AI-10's privacy risk taxonomy.
EU
Reference
AI Act
Regulation on Artificial Intelligence
EU AI risk tiering regulation. SCL classifications are cross-referenced with Article 6 high-risk categories.
UL
Reference
4600
Standard for Safety for the Evaluation of Autonomous Products
Autonomous product safety case reference, listed in framework Table 1-10.
EASA
Reference
Issue 2
EASA Artificial Intelligence Concept Paper Issue 2
Guidance for Level 1 and 2 machine learning applications. Source of the AI/ML constituent concept the framework adapts for the determination boundary in Section 1.2.1.
[ Pending Verification ] 6
Cited inline within the framework but not yet clause-verified against source. Retained as informative cross-references; will be re-verified when source documents become accessible. Not normative dependencies.
RTCA
Pending
DO-326A
Airworthiness Security Process Specification
Aviation security process reference cited in AI-7 (Adversarial Robustness) aviation rows.
RTCA
Pending
DO-330
Software Tool Qualification Considerations
Tool qualification reference cited in AI-1 (Operational and Data Foundations) aviation row.
RTCA
Pending
DO-355
Information Security Guidance for Continuing Airworthiness
Continuing airworthiness security reference cited in AI-7.6 aviation row.
RTCA
Pending
DO-356A
Airworthiness Security Methods and Considerations
Aviation security methods reference cited in AI-7 (Adversarial Robustness) aviation rows.
AAMI
Pending
HE75
Human Factors Engineering - Design of Medical Devices
Human factors reference cited in AI-9.8 (Workload Management) medical row.
SAE
Pending
ARP6983
Process Standard for Aeronautical Safety-Related Products Implementing AI
SAE ARP6983 / EUROCAE ED-324, cited in the Section 1.2.1 determination boundary discussion and in the AI-11 and AI-12 aviation rows.
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