The SEC RIA AI Framework

The RIA AI Framework:

How to Make AI Work in Your Firm—Without Violating SEC Rules

Artificial intelligence is revolutionizing the financial advisory industry—but for SEC-registered RIAs, it brings as much regulatory complexity as it does opportunity. The RIA AI Framework from MTradecraft is your roadmap to navigating this new landscape with confidence.

Built specifically for the compliance, IT, and executive teams of SEC-regulated firms, this guide outlines practical, defensible steps for incorporating AI tools like ChatGPT, Microsoft Copilot, and other generative models without violating SEC expectations or putting client data at risk.

Download the definitive guide to safely and strategically integrating AI into your SEC-registered advisory firm.

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Non Members Can Purchase for $100

Inside, you’ll find:

  • A clear governance model for responsible AI adoption
  • Step-by-step checklists for access control, data protection, and monitoring
  • Practical strategies to avoid prompt injection, data leaks, and staff misuse
  • Regulatory alignment tips tailored to Rule 206(4)-7 and emerging SEC expectations
  • Real-world examples to highlight risks—and show how smart firms avoid them

Whether you’re exploring AI for client communication, research, or compliance automation, this framework will help you reduce risk, boost efficiency, and build trust—with regulators, clients, and your team.

Whitepaper Table of Contents:

Executive Summary — Frames why AI matters now for RIAs, the regulatory stakes, and how the framework converts SANS/NIST guidance into practical controls; urges firms to act early, treat the guide as a living resource, and pair compliance with operational efficiency.

  • 1) Governance, Risk & Compliance (GRC) — Establish a formal AI Risk Management Policy tied to 206(4)-7 and NIST AI RMF, define approved use cases, maintain an AI Bill of Materials, assign cross-functional roles, and map AI usage to fiduciary/privacy/supervision risks so safeguards are clear and defensible.
  • 2) Access Controls — Enforce least-privilege on models/APIs/vector DBs, require MFA, log prompts/admin actions, and segment environments; the goal is to prevent misuse or tampering while keeping controls audit-defensible.
  • 3) Data Protection — Prohibit training on raw, non-anonymized client data, use redaction/differential privacy for prompts, and harden vector databases with encryption, segregation, and change logging to reduce leakage and uphold fiduciary duties.
  • 4) Deployment Strategy — Prefer on-prem or isolated cloud for sensitive workflows, avoid public SaaS LLMs for compliance/client/investment decisions, lock down IDE/AI assistants, and contractually define data usage/retention/training-reuse with third parties.
  • 5) Inference Security — Mitigate prompt injection and jailbreaks with input sanitization, strict role/prompt boundaries, output filtering, and anomaly monitoring (e.g., blocking unauthorized recommendations).
  • 6) Monitoring & Auditing — Route AI logs to SIEM/SOC, track refusals/failed inferences/API spikes, set alerts for policy breaches, and conduct red-team/pen-tests to detect drift, misuse, and abuse.
  • 7) Staff Awareness & Training — Add AI-specific modules and tabletop exercises (prompt-injection, hallucinations, AI-phishing), clarify permitted vs. prohibited uses, and leverage turnkey training where helpful to reduce mistakes and evidence a culture of security.
  • 8) Model Lifecycle Management — Track lineage/dependencies in registries, gate and log all fine-tuning/deploys, maintain rollbacks, and restrict distribution of business-critical models to preserve integrity and control.
  • 9) Regulatory Alignment — Bake AI into your annual risk assessment, document controls in the P&P manual, maintain a vendor register with due diligence, and include AI in mock audits to meet growing SEC scrutiny.
  • Conclusion — Treat AI like any other critical function: integrate with risk assessments, document thoroughly, and run it with clear governance so adoption is safe, compliant, and operationally resilient.