DATA & AI

DATA GOVERNANCE

OBJECTIVES

Delivered a secure, transparent, and policy-aligned data ecosystem, ensuring trust, compliance, and protection across all digital and AI platforms through effective governance of:

  • Data Classification
  • Metadata Management
  • Access Control
  • Compliance & Audit Readiness
  • Data Lineage & Security Monitoring
  • Policy Enforcement

TEAM CAPABILITIES

  • Data Governance Leads: Define data management policies, standards, and stewardship frameworks.
  • Data Stewards: Ensure data quality, lineage tracking, and compliance documentation across domains.
  • Security & Compliance Team: Manage access reviews, risk assessments, and incident response.
  • Automation & Monitoring Support: Develop scripts and alerts for automated scanning, tagging, and threat detection.
  • AI Governance & Ethics Team: Evaluate AI use cases for compliance, transparency, and responsible deployment.

DELIVERABLES

  • Governance & Compliance Processes: Defined and implemented processes to ensure ethical, secure, and policy-aligned data and AI practices.
  • Unified Governance Framework: Established a centralized model covering data, AI, and automation platforms for consistent policy enforcement.
  • Data Classification & Tagging: Implemented automated classification (Public, Internal, Confidential) using Purview, Fabric integrations, and Atlas APIs.
  • Metadata & Lineage Visibility: Enabled end-to-end traceability to ensure datasets, reports, and AI models are auditable and compliant.
  • Access Control & Security Policies: Enforced role-based access (RBAC) and conditional controls aligned with security directives.
  • Continuous Compliance: Embedded GDPR, ISO 27001, and internal security checks to ensure compliance by design.
  • Security Oversight: Monitored data movement, API activity, and AI workloads to detect risks and enforce least-privilege access.
  • AI & ML Governance: Applied governance to AI models and training data, ensuring transparency, ethics, and explainability.

DATA & AI