AI & Data Companies

AI and data companies face risks that standard control sets often miss: prompt injection, sensitive information disclosure, model and data poisoning, supply-chain vulnerabilities, and excessive agent autonomy.

Our approach: Standard FTC Safeguards Rule readiness first. AI and data-specific hardening second. The advisory modules below are optional enhancements on top of mandatory controls.

Standard Controls vs. AI/Data Enhancements

Standard FTC Safeguards Rule Readiness

Mandatory controls required for compliance:

  • Logical access and privileged access
  • Change management
  • Incident response
  • Risk management
  • Vendor management
  • Backup and availability
  • Logging and monitoring
  • Confidentiality and privacy (where applicable)

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AI/Data Advisory Enhancements

Optional modules justified by AI-risk frameworks:

  • Data lineage and training data governance
  • Prompt/response telemetry
  • RAG and retrieval governance
  • Model/provider vendor review
  • Agent approval gates
  • AI-assisted SDLC controls
  • Warehouse and analytics governance

Advisory Modules

Each module adds specific controls and documentation practices to address risks unique to AI and data-intensive products.

AI-Assisted SDLC Controls

§ 314.4(c)(4) secure development practices extend to AI-assisted coding tools used in financial service application development, where AI-generated code may handle customer information.

What This Module Adds

  • AI code review requirements for customer data handling logic
  • Automated security scanning of AI-generated output before merge
  • Secure coding standards for AI-assisted features touching customer information
  • Provenance tracking for AI-generated code artifacts

Human Review and Agent Approval Gates

Automated decisions affecting customer information require oversight consistent with the qualified individual's responsibilities under § 314.4(a) and the monitoring obligations of § 314.4(c).

What This Module Adds

  • Human approval gates for AI agents accessing customer information systems
  • Escalation procedures for anomalous AI behavior involving customer data
  • Audit trail requirements for all AI agent actions on customer information
  • Authorization controls for AI-initiated data exports or modifications
  • Review gates for automated changes to access controls or security configurations

Model/Provider Vendor Risk

§ 314.4(f) vendor oversight requirements apply to AI model providers that process, store, or have access to customer information through API calls, fine-tuning, or inference pipelines.

What This Module Adds

  • AI model provider security assessment covering data handling and retention practices
  • Data processing restrictions preventing customer information use for model training
  • Model provider exit planning with data portability and deletion verification
  • Subprocessor inventory for AI model providers and their infrastructure dependencies
  • Contractual prohibitions on customer information retention beyond inference

Prompt and Response Logging

§ 314.4(c) monitoring requirements for information systems extend to AI interaction logs, which serve as audit evidence for detecting unauthorized access to or exfiltration of customer information.

What This Module Adds

  • Customer information detection and redaction in prompts before transmission to AI providers
  • Response filtering to prevent customer data leakage through AI-generated output
  • Log retention policies aligned with risk assessment and regulatory retention requirements
  • Audit trail for all AI interactions involving customer information systems

RAG and Vector-Store Controls

Vector stores containing customer financial information are information systems under the Safeguards Rule and must meet § 314.4(c)(3) encryption and § 314.4(c)(1) access control requirements.

What This Module Adds

  • Access controls on vector stores containing customer information embeddings
  • Encryption requirements for vector databases at rest and in transit
  • Retrieval source allowlists preventing unauthorized data from entering the embedding pipeline
  • Chunk-level traceability linking retrieved content back to source documents and access permissions
  • Injection resistance testing for RAG pipelines processing customer information

Training and Inference Data Governance

Using customer information for AI model training or inference requires safeguards under § 314.4(b) risk assessment, including evaluation of whether the use creates new threats to customer data security.

What This Module Adds

  • Risk assessment for AI training datasets containing customer information
  • Data minimization requirements for inference pipelines processing customer data
  • Customer information anonymization and de-identification standards for AI use cases
  • Model output review to prevent memorization and leakage of customer information
  • Governance controls for fine-tuning models on customer financial data

Warehouse and Analytics Governance

Data warehouses aggregating customer information across financial products and business lines must meet all Safeguards Rule requirements, including access controls, encryption, and risk assessment coverage.

What This Module Adds

  • Access controls for analytics environments containing aggregated customer information
  • Data masking and row-level security for warehouse queries accessing customer data
  • Warehouse inclusion in the § 314.4(c)(2) data inventory and data flow documentation
  • Retention and disposal controls for warehouse copies of customer information
  • Monitoring and alerting for anomalous query patterns against customer data tables

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