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 SEC Cybersecurity Disclosure 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 SEC Cybersecurity Disclosure 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)
View all control domains →
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
SEC disclosure requires describing cybersecurity risk management processes; AI-assisted development introduces risks that should be disclosed if material.
What This Module Adds
- AI risk disclosure language for 10-K filings
- Secure AI development as risk management evidence
- Disclosure of AI-related cybersecurity incidents
Human Review & Agent Gates
Board oversight requirements extend to AI systems making decisions that could have material impact; human gates demonstrate governance maturity.
What This Module Adds
- Board-level AI risk oversight documentation
- AI decision escalation for materiality-sensitive operations
- Materiality assessment framework for AI-driven incidents
Model Provider Vendor Risk
Item 106(b)(2) requires disclosing third-party risk oversight; AI model providers represent a material third-party cybersecurity risk category.
What This Module Adds
- AI vendor risk assessment in 10-K disclosure narrative
- Model provider concentration risk evaluation
- AI supply chain disclosure for risk management section
Prompt & Response Logging
Incident investigation and the 4-business-day materiality determination require comprehensive audit trails including AI system interactions.
What This Module Adds
- AI interaction forensics capability for incident response
- Log retention aligned with SEC disclosure timelines
- Automated anomaly detection in AI system interactions
RAG & Vector Store Controls
Data exfiltration from AI systems including RAG stores could constitute a material cybersecurity incident requiring 8-K disclosure.
What This Module Adds
- Vector store breach detection and response procedures
- Materiality thresholds for AI data exposure incidents
- Incident scoping methodology for RAG and AI data systems
Training & Inference Data Governance
Data governance for AI systems is part of the overall cybersecurity risk management strategy disclosed under Item 106(b).
What This Module Adds
- AI data risk integration into enterprise risk management framework
- Training data breach scenario planning and materiality assessment
- Intellectual property exposure risk evaluation for AI systems
Warehouse & Analytics Governance
Enterprise data warehouses are critical assets whose compromise could be material; governance demonstrates risk management maturity for 10-K disclosure.
What This Module Adds
- Data warehouse inclusion in enterprise risk management scope
- Breach materiality assessment for centralized data repositories
- Analytics governance as a board reporting and oversight topic
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