Jeyancie Consulting

Practice area · AI Data Governance

Govern the data entering, moving through and emerging from AI.

AI adoption is expanding faster than most organizations can understand and govern the data it uses. Jeyancie Consulting helps you scale AI with stronger data controls, clear accountability and repeatable governance.

Discuss your AI program Start with an assessment

The AI data lifecycle

Every stage is a point where sensitive data can be exposed, retained or reused.

  1. Stage 1

    Identify

    Which use cases touch sensitive or regulated data.

  2. Stage 2

    Source

    Where training and retrieval data comes from, and who owns it.

  3. Stage 3

    Authorize

    Approved use, rights and access boundaries.

  4. Stage 4

    Train & retrieve

    Provenance, quality, freshness and retrieval scope.

  5. Stage 5

    Generate & share

    Prompts, responses, outputs and destinations.

  6. Stage 6

    Monitor & retain

    Telemetry, retention, deletion and review.

What Jeyancie governs

Four areas where AI data risk concentrates.

Use-case & data risk

Inventory, risk tiering, sensitivity, purpose, access, provider use and downstream impact.

Training data & RAG

Provenance, authorization, source quality, retrieval boundaries, freshness and deletion.

Prompt & response protection

Sensitive-data movement, DLP for prompts and outputs, traceability, external destinations and retention.

Third-party & executive governance

Provider data use, privacy and rights, residency, exceptions, metrics and risk decisions.

Scope boundary. Jeyancie focuses on AI data governance and protection. Model and runtime security, secure engineering and technical model guardrails remain with your AI and application security teams unless separately scoped.

Services

Two ways to engage.

Assess

AI Data Governance & Risk Assessment

Gain visibility into where AI creates data risk, and which use cases, providers and sources need stronger governance.

When to engage
AI adoption is expanding faster than your ability to understand and govern the data being used.
What you receive
  • AI use-case and data risk inventory
  • AI data governance maturity assessment
  • Training data and RAG source review
  • Prioritized findings and control-gap analysis
  • Governance recommendations and executive readout
  • Implementation roadmap

OutcomeVisibility into where AI creates data risk, and a clear plan for what to govern first.

Design & Transform

AI Data Governance & Protection Transformation

Design the frameworks, then put them into practice across priority AI use cases, data sources, providers and environments.

When to engage
You understand the AI data risks and need approved governance and protection frameworks working across priority use cases.
What you receive
  • AI Data Governance Framework
  • AI Data Protection Control Framework
  • AI data classification and handling standard
  • Training data and RAG governance standard
  • Prompt and response protection strategy
  • KPI and KRI framework and roadmap
  • Use-case onboarding, exception workflows and control validation

OutcomeAI can scale with stronger data controls and repeatable governance across use cases, sources and providers.

Inside the assessment

Scored on evidence, not assumptions.

Enterprise governance maturity, the risk of each AI use case and the severity of each finding are measured separately, so priorities stay clear and defensible.

  • Governance, accountability and decision rights
  • AI use-case and data inventory
  • Data sensitivity, classification and handling
  • Training data, RAG and source governance
  • Access and entitlement governance
  • Prompt, response and data movement protection
  • Third-party, privacy, retention and residency
  • Monitoring, response, exceptions and assurance
  • Metrics, adoption and continuous improvement

Focused reviews

Not every need is enterprise-wide.

Targeted reviews address a single decision point, from a new AI tool to an incident.

AI use-case review

One use case, provider, retrieval solution or material expansion of an existing use.

RAG data governance review

Source repositories, permissions, provenance, vector stores, retrieval scope and output exposure.

AI data protection control review

DLP, SSE and CASB, DSPM, classification, identity and logging for AI data flows.

Pre-production review

Data risk and required controls before a pilot, production deployment or broader rollout.

Post-incident review

Causes, control gaps and remediation after an AI-related exposure, audit issue or policy violation.

Enterprise assessment

Governance capability across the organization, from operating model to metrics and assurance.

Next step

Not sure where to start? Begin with an assessment.

Jeyancie Consulting will help identify your highest-priority risks and determine the right path forward.

Schedule an Executive Consultation