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 State Privacy Laws 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 State Privacy Laws 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

Multiple state laws require privacy by design; AI-assisted development must integrate privacy requirements across all applicable state jurisdictions to prevent code-level violations of minimization, purpose limitation, and consent requirements.

What This Module Adds

  • Multi-state privacy requirement checks integrated into AI code review pipelines to flag new data collection that may trigger obligations in any applicable state
  • Automated scanning of AI-generated code for personal data processing patterns that could require consent, opt-out mechanisms, or data protection assessments
  • State-specific privacy notice impact analysis when AI-suggested features introduce new processing activities or data categories
  • Cross-jurisdictional data flow detection to identify when AI-generated code routes personal data to processors or third parties subject to different state requirements

Human Review & Agent Gates

State profiling opt-out provisions under Virginia (VCDPA), Colorado (CPA), Connecticut (CTDPA), and Texas (TDPSA) require human review capabilities for automated decisions that produce legal or similarly significant effects on consumers.

What This Module Adds

  • Human review triggers aligned with state-specific profiling definitions — Virginia, Colorado, Connecticut, and Texas each define profiling slightly differently
  • Escalation workflows ensuring automated decisions with legal or similarly significant effects are reviewable by qualified personnel before finalization
  • Opt-out of profiling mechanism integrated with universal opt-out and consumer rights request workflows
  • Audit trail for agent-driven decisions recording the automated logic, data inputs, human review determination, and consumer notification
  • Cross-state profiling impact assessment documenting which automated decisions trigger profiling opt-out rights in which states

Model Provider & Vendor Risk

State processor requirements under Virginia (VCDPA), Colorado (CPA), Connecticut (CTDPA), Texas (TDPSA), and Oregon (OCPA) create contractual obligations that extend to AI model providers processing personal data on behalf of controllers.

What This Module Adds

  • AI model provider classification as processor or sub-processor under each applicable state law, with DPA coverage ensuring all required provisions are met
  • Data flow mapping for model API calls to determine which personal data elements are transmitted to model providers and which state laws apply
  • Sub-processor cascade management when model providers engage their own infrastructure sub-processors for inference or fine-tuning
  • Contractual restrictions on model providers using personal data for training purposes, aligned with purpose limitation requirements across all applicable states

Prompt & Response Logging

State access rights and data inventory obligations require organizations to account for all processing activities involving personal data, including AI interactions where personal data appears in prompts or model responses.

What This Module Adds

  • Logging scope assessment determining which AI prompts and responses contain personal data subject to state consumer rights
  • Retention policies for AI interaction logs aligned with data minimization requirements across applicable states
  • Consumer access and deletion right coverage for personal data captured in prompt-response logs
  • De-identification and redaction strategies for AI logs to reduce the compliance surface of retained interaction data
  • Data inventory integration ensuring AI interaction logs are captured in the organization's processing activity records

RAG & Vector Store Controls

Deletion rights across all state comprehensive privacy laws require technical capability to identify and remove personal data from vector stores, where traditional deletion mechanisms may not apply to embedded representations.

What This Module Adds

  • Vector store personal data inventory identifying which embeddings contain or are derived from personal data subject to state privacy laws
  • Deletion capability for vector embeddings that contain personal data, including re-indexing or tombstoning strategies that satisfy state deletion requirements
  • Cross-state deletion standard alignment ensuring vector store deletion mechanisms meet the most protective applicable state requirement
  • Retrieval scope controls preventing RAG systems from surfacing personal data of consumers who have exercised opt-out or deletion rights

Training & Inference Data Governance

Purpose limitation provisions across state privacy laws restrict repurposing personal data for model training without compatible purpose justification, and data minimization requirements constrain the volume and retention of training data.

What This Module Adds

  • Purpose compatibility analysis for using personal data in model training, documented against each applicable state's purpose limitation standard
  • Training data minimization ensuring only the minimum personal data necessary is used, with de-identification applied where feasible
  • Consumer notice requirements for AI training use cases — processing personal data for model training must be disclosed in privacy notices
  • Opt-out right coverage for consumers who object to their personal data being used for model training, where training constitutes sale or targeted advertising
  • Inference-time data governance ensuring model inputs containing personal data are processed consistent with the original collection purpose

Warehouse & Analytics Governance

Data minimization requirements across state privacy laws constrain analytics warehouse retention and secondary use of personal data, while consumer rights obligations require analytics systems to support access, deletion, and opt-out requests.

What This Module Adds

  • Warehouse retention policies aligned with the strictest applicable state minimization and retention limitation requirements
  • Secondary use controls preventing analytics on personal data beyond the original or compatible disclosed purpose
  • Consumer rights integration ensuring data warehouses can fulfill access, deletion, and portability requests across all applicable states
  • De-identification and aggregation strategies reducing the volume of identifiable personal data retained in analytics systems

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