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 NIST AI RMF 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 NIST AI RMF 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 Governance

NIST AI RMF MAP function requires understanding deployment context; AI-assisted development is itself an AI deployment requiring governance under the framework.

What This Module Adds

  • Governance framework for internal AI coding tools (copilots, code generators)
  • Risk assessment methodology for copilot-generated code
  • Developer AI usage policies with acceptable use boundaries
  • Code provenance tracking for AI-generated contributions

Human Review & Agent Gates

AI RMF GOVERN function explicitly requires human oversight mechanisms proportional to risk level, and agentic AI systems amplify oversight challenges.

What This Module Adds

  • Gate design patterns for agentic AI workflows
  • Escalation trigger calibration for autonomous AI actions
  • Oversight fatigue mitigation strategies
  • Decision authority matrices for AI-assisted vs. AI-autonomous operations

Model Provider & Vendor Risk

AI RMF GOVERN 6 addresses third-party AI risks including supply chain concentration, vendor dependency, and foundation model provider assessment.

What This Module Adds

  • Foundation model provider assessment framework
  • API dependency risk analysis and fallback planning
  • Model card evaluation criteria for vendor-provided models
  • Vendor concentration risk mapping across AI supply chain

Prompt & Response Logging

AI RMF MEASURE function requires monitoring AI system behavior; interaction logging is the foundational infrastructure for measurement, audit, and incident investigation.

What This Module Adds

  • Prompt injection detection and monitoring
  • Response quality monitoring with automated scoring
  • User feedback integration into model evaluation
  • Log retention and privacy-compliant archival

RAG & Vector Store Controls

AI RMF MAP function covers data quality and provenance; RAG systems introduce retrieval-specific risks including hallucination amplification, knowledge base poisoning, and citation accuracy failures.

What This Module Adds

  • Retrieval quality assurance testing and metrics
  • Knowledge base poisoning detection and prevention
  • Citation accuracy validation for RAG-generated responses
  • Access control governance for vector store content

Training & Inference Data Governance

AI RMF MAP 2 explicitly addresses data governance requirements including representativeness, bias, and provenance — this module provides the operational depth to implement those requirements.

What This Module Adds

  • Data documentation standards (datasheets for datasets)
  • Labeling workforce governance and quality assurance
  • Synthetic data quality assurance and validation
  • Inference-time data handling and privacy controls

Warehouse & Analytics Governance

AI RMF GOVERN function requires organizational governance structures that extend to the data infrastructure supporting AI systems, including feature stores, analytics pipelines, and data platforms.

What This Module Adds

  • Feature store governance and version control
  • Analytics pipeline lineage tracking for AI inputs
  • AI-specific data access controls and authorization
  • Data platform readiness assessment for AI workloads

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