If your AI systems touch securities decisions, broker-dealer recordkeeping, model-risk management, consumer financial data, or NY DFS cybersecurity governance, you're governed by more than one regulator. SEC AI Guidance, SOX ITGC extensions, SR 11-7 model validation, GLBA Safeguards, and NY DFS Part 500 all overlap. This page lists what actually binds, sourced directly from Praxient's regulatory library.
The SEC's AI Guidance, SOX's ITGC framework, the Fed's SR 11-7 model-risk management expectations, GLBA's Safeguards Rule, and NY DFS Part 500's cybersecurity governance obligation all predate the modern AI stack — they cover traditional controls and governance but say nothing about model training pipelines, inference-time decisioning, or the audit trail obligations that come with using AI in material financial decisions. That gap is where regulated risk now lives.
The provisions below are drawn from Praxient's seeded regulatory library (SEC AI Guidance, SOX ITGC, SR 11-7 model-risk management, GLBA Safeguards Rule, NY DFS Part 500 cybersecurity). They are not a substitute for legal advice — they are a curated snapshot of the obligations a financial-services AI governance program has to address, rendered inline so you can see what binds before deciding whether to book the $500 strategy session.
In one focused $500 session, we will map your highest-risk AI workflows to the obligations that apply and identify the most practical next steps.
Sourced live from Praxient's seeded library of active regulations and provisions, scoped to the financial-services vertical.
The free scorecard tags your readiness by category. The $500 strategy session walks through which of these provisions apply to the AI you already have in production.