Related papers: SCITUS: A Multi-Jurisdictional Framework for Adapting NIST AI RMF to the Canadian Regulatory Context

SCITUS: A Multi-Jurisdictional Framework for Adapting NIST AI RMF to the Canadian Regulatory Context

URL: http://arxiv.org/abs/2607.15051v1
Date: Thu, 16 Jul 2026 14:29:52 GMT
Title: SCITUS: A Multi-Jurisdictional Framework for Adapting NIST AI RMF to the Canadian Regulatory Context
Abstract summary: We present a comprehensive framework adapting NIST AI RMF 1.0 to Canadian federal and provincial AI regulations simultaneously.<n>SCITUS integrates seven trustworthy-AI characteristics enhanced for Canadian requirements.<n>We argue that systematic adaptation of NIST AI RMF to multi-jurisdictional requirements offers significant advantages over jurisdiction-by-jurisdiction compliance.
Score: 0.1269104766024433
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Canadian organizations deploying artificial intelligence systems face a fragmented regulatory landscape spanning federal requirements (the Treasury Board Directive on Automated Decision-Making) and divergent provincial regulations across Ontario, Quebec, Alberta, Manitoba, and British Columbia. The death of Bill C-27 (Artificial Intelligence and Data Act) in January 2025 - and the federal government's June 2026 confirmation that it will pursue targeted instruments rather than omnibus AI legislation - leaves organizations without unified compliance guidance. Global frameworks such as NIST AI RMF 1.0, the EU AI Act, and ISO/IEC 42001 provide valuable guidance but lack systematic methodologies for adaptation to multi-jurisdictional national contexts. We present SCITUS (Systematic Canadian Integration for Trustworthy and Unified Standards), a comprehensive framework adapting NIST AI RMF 1.0 to Canadian federal and provincial AI regulations simultaneously. SCITUS integrates seven trustworthy-AI characteristics enhanced for Canadian requirements, four core governance functions, a novel multi-jurisdictional compliance mapping methodology, and a versioned control catalog that has evolved from 31 controls (v1.0, June 2025) to 57 controls (v2.0, July 2026) in response to regulatory developments - including Canada's first regulatory findings on generative-AI training data - and the documented 2026 agentic-AI threat landscape. We demonstrate applicability through scenarios spanning federal government, provincial healthcare, and the private sector, and argue that systematic adaptation of NIST AI RMF to multi-jurisdictional requirements offers significant advantages over jurisdiction-by-jurisdiction compliance and provides a replicable model for other federal systems.

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