Security Questions to Ask AI Vendors: A Financial Services Guide

Financial institutions face a critical challenge when evaluating AI platforms: how do you separate marketing promises from genuine security capabilities? With regulatory frameworks like FINRA, SOX, and GLBA governing every aspect of your operations, the wrong AI vendor choice could expose your institution to significant compliance violations and security breaches.

This guide provides the essential questions every financial services organization should ask AI vendors before making a commitment. These aren't generic enterprise questions, they're specifically tailored to the unique security and compliance requirements of regulated financial institutions.

Security Foundation Questions

"What security framework have you certified against, and can you provide evidence of ongoing compliance monitoring?"

SOC 2 Type 2 certification should be your baseline requirement. This certification demonstrates that a vendor has implemented and maintained critical security controls over time, including:

• Principle of least access implementation
• Encryption everywhere (data in transit and at rest)
• Quarterly access reviews with documentation
• Sensitive data handling protocols

Look for vendors who go beyond SOC 2 Type 2 with additional certifications like ISO 27001 or HIPAA, depending on your specific regulatory environment. More importantly, ask for evidence of ongoing compliance monitoring—not just a one-time certification.

"Does your platform support self-hosted model providers, and can I switch between them without rebuilding my workflows?"

The early days of AI were plagued by concerns about proprietary data being used to train public models. Modern solutions eliminate this concern entirely through self-hosted model options:

• AWS Bedrock for hosting models within your AWS environment
• Google Vertex AI for deployment in your Google Cloud infrastructure
• Azure OpenAI for running OpenAI models in your Azure tenant

With these approaches, model providers contractually guarantee your data won't be used for training purposes, and you maintain full control over data residency.

"What's your approach to on-premises deployment, and at what scale does it become economically viable?"

While cloud-based platforms with proper security controls are sufficient for most financial services use cases, certain situations may require on-premises deployment:

• Extremely sensitive use cases involving classified information
• Specific regulatory requirements in certain jurisdictions
• Large-scale deployments where economics justify the complexity

Understand the trade-offs: on-premises deployments typically involve seven-figure costs, complex update cycles, and reduced agility in adopting new features.

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Architecture and Access Control Questions

"Do you use logical or physical data separation, and what penetration testing do you conduct to validate security?"

Understanding how your AI platform separates customer data is critical for assessing security risk:

• Physical Separation: Each customer gets their own database instance, making cross-tenant data leaks physically impossible. This approach is expensive and typically reserved for the most sensitive deployments.
• Logical Separation: All customers share database infrastructure, but data is separated through application-level controls. This is the standard approach for modern SaaS platforms and is not inherently less secure than physical separation.

The key is ensuring logical separation includes proper access controls, regular penetration testing, and comprehensive audit logs.

"How do you implement role-based access controls, and can you integrate with our existing identity provider?"

Security doesn't end at the platform level. Your AI tools must have granular permission structures built in from day one:

• User roles with distinct permissions (admins, builders, end-users)
• Resource-level sharing controls for data sources, assistants, and workflows
• Group-based provisioning that automatically assigns users based on role or department
• SAML/SSO integration to sync permissions with your existing identity provider

RBAC scales with your organization, eliminating the need to manually manage permissions for every individual as you grow.

"Are data sources independent components, or are they tied to specific workflows?"

Many AI platforms tightly couple data sources with specific workflows, creating management nightmares as you scale. This architecture creates several problems:

• Every new workflow requires re-uploading or reconnecting data
• No centralized view of what data has been connected to AI systems
• Difficult to audit which workflows have access to sensitive information
• Impossible to quickly revoke access when someone leaves or changes roles

Look for platforms with decoupled data source architectures where:

• Data sources exist as independent, reusable components
• You connect a data source once and use it across multiple assistants and workflows
• Centralized access control and audit trails exist for all data
• You can easily see which workflows are using which data sources

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Regulatory Compliance Questions

"How does your platform support FINRA recordkeeping requirements and enable human oversight of AI-generated outputs?"

FINRA has made clear that its rules are technology-neutral and apply to AI systems. Key requirements include:

• Supervision (FINRA Rule 3110): Reasonably designed supervisory systems that address technology governance, model risk management, data privacy, and AI model reliability
• Recordkeeping: All AI-generated communications, recommendations, and decisions must be retained according to FINRA requirements
• Content Standards: AI-generated communications must comply with fair and balanced presentation requirements
• Human-in-the-Loop: Supervisory obligations effectively mandate human oversight for investment recommendations, customer communications, and compliance decisions

"What SOX-compliant controls are built into your platform, and how do you support segregation of duties?"

When AI becomes part of your financial reporting infrastructure, SOX IT General Controls apply:

• Access Controls: User access management with documented approval processes and regular reviews
• Change Management: Documented procedures for AI models and workflows with testing requirements
• Segregation of Duties: No single person should control the entire AI system lifecycle
• Data Security: Encryption, backup procedures, and protection against unauthorized modification
• Monitoring and Logging: Comprehensive audit logs with regular compliance review

"How do you help financial institutions comply with GLBA Safeguards Rule requirements, and what controls protect NPI?"

The Gramm-Leach-Bliley Act requires protection of customer Nonpublic Personal Information (NPI). Essential requirements include:

• Written Information Security Plan with risk assessments and safeguards
• Encryption requirements for NPI in transit and at rest
• Access controls restricting NPI to authorized personnel only
• Data sanitization and secure disposal procedures
• Third-party oversight with due diligence and monitoring requirements
• Incident response plans for data breaches

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AI-Specific Risk Questions

"What controls do you have in place to prevent AI from taking unauthorized actions, and how comprehensive are your audit logs?"

AI models have inherent limitations that create specific risks for financial services:

• Hallucinations: Models generating false or fabricated information
• Prompt Injection Attacks: Malicious instructions that cause models to ignore original instructions

Mitigation strategies should include:

• Human-in-the-loop requirements for critical decisions
• Approval workflows for any actions taken on behalf of users
• Comprehensive audit logs capturing full context
• Scoped assistants with explicit guardrails
• Internal use cases before customer-facing applications

"What are your data retention capabilities, and can you export comprehensive audit logs for regulatory examinations?"

Comprehensive logging and retention are fundamental to regulatory compliance:

Audit Trail Requirements: Every AI interaction should capture user information, timestamps, data accessed, models used, inputs/outputs, actions taken, and human review status.

Data Retention: Different regulations impose different retention periods (FINRA: 3-6 years, SEC: 5 years, SOX: 7 years, GLBA: duration of relationship plus applicable period).

Reporting Capabilities: Platforms must support searchable audit logs, export capabilities, customizable report templates, and real-time monitoring dashboards.

Governance and Control Questions

"How do you help organizations consolidate AI usage onto a single, governed platform?"

The biggest security risk comes from rogue AI usage happening across your organization right now, advisors using ChatGPT for client research, analysts using Claude for investment memos, operations teams using consumer AI tools with customer data.

The solution is providing a sanctioned platform that:

• Meets all security and compliance requirements
• Is easier to use than consumer AI tools
• Integrates with existing systems
• Provides role-specific capabilities

"What analytics do you provide to track adoption, usage, and ROI?"

Security and compliance extend beyond preventing bad outcomes to demonstrating value:

• Which users are actively using AI
• Which assistants and workflows drive the most value
• Model usage and associated costs
• Time saved on specific processes
• Adoption rates across departments

This data helps justify AI investment, identify high-value use cases, optimize model selection, and demonstrate compliance with governance policies.

Vendor-Specific Considerations

"Are you built for regulated industries from day one, or are you an 'enterprise-also' solution?"

Look for vendors who understand the unique requirements of financial services rather than generic enterprise platforms trying to adapt to regulated industries.

Key indicators include:

• Responsive support with SLAs appropriate for critical infrastructure
• Clear roadmap for compliance with emerging regulations
• References from other financial services customers
• Transparent pricing without hidden AI premium fees
• Right to audit vendor controls
• Clear data ownership and portability terms

Making the Right Choice

The institutions that will win with AI are those that move fastest while maintaining security, compliance, and control. By asking these specific questions, you can identify vendors who truly understand the regulatory landscape of financial services and can support your AI adoption journey without compromising on security or compliance.

Remember: the goal isn't to find a vendor who checks every box perfectly, but to find one who demonstrates a deep understanding of your regulatory requirements and has built their platform with financial services compliance in mind from day one.

The future of financial services is AI-native. Make sure you get there securely.