Open vs. Closed AI: What Advisory Firm Leaders Need to Know

• Open AI tools should only be used for general tasks like research and drafting, never for client names, account details, financial plans, or proprietary firm data
• Closed AI models provide data security and control but require firms to validate output accuracy and still need oversight for client-facing communications
• Firms must establish AI policies before deployment that specify approved tools, data input boundaries, required review processes, and oversight responsibility—paying for AI access does not automatically make it a closed model

Summary: Open vs. Closed AI for Financial Advisory Firms

Financial advisory firms must understand the critical distinction between open and closed AI systems before implementing artificial intelligence across operations. This differentiation directly impacts data security, regulatory compliance, and risk management.

Key Distinctions:

Open AI tools are widely available, often free, and trained on large public datasets. They excel at general tasks like research, brainstorming, and summarizing public information. However, they are inappropriate for sensitive data including client names, account details, financial plans, tax documents, or proprietary firm information.

Closed AI models allow firms to own both input and output, with data remaining within the firm's controlled environment. This makes them suitable for sensitive business information and client data. The tradeoff is limited perspective, as closed models only draw from firm-provided information, potentially creating an echo chamber effect.

Compliance and Policy Requirements:

Despite the absence of specific SEC AI guidance, firms remain liable under existing regulations covering client privacy, supervision, advertising, books and records, and fiduciary responsibility. "No AI rules yet" is not a regulatory defense.

Minimum Policy Requirements:

• Approved AI tools list
• Clear data input permissions mapped to open vs. closed models
• Review protocols for AI-assisted output before external use
• Designated oversight responsibility and documentation procedures

Critical Note: Paying for an AI tool doesn't make it a closed model. Paid subscriptions to consumer platforms remain open environments if data trains the underlying model.

Bottom Line: Firms should match AI tools to specific use cases, establish clear policies before deployment, and prioritize responsible implementation over convenience or cost considerations.