Data Security Posture Management for AI Using Microsoft Purview

YASH DSPM for AI is a purpose-built Data Security Posture Management offering that helps organizations discover, classify, and protect AI-relevant data across its lifecycle—from data sourcing and experimentation to model training and production inference.

Built on Microsoft Purview, the service enables organizations to map sensitive data to AI pipelines, reduce data over-exposure, and enforce policy guardrails that align security, governance, and responsible AI objectives.

Key Highlights

• Unified data inventory and lineage for AI datasets and pipelines
• AI-aware data classification and auto-labeling
• Exposure and entitlement risk reduction
• Guardrails for AI training, MLOps, and inference pipelines
• Integrated posture dashboards and remediation workflows
• Ongoing DSPM operations and governance support

Description

YASH establishes continuous DSPM for AI using Microsoft Purview as the foundation for AI data governance. The offering connects data platforms, application repositories, and AI systems to create end-to-end visibility into sensitive data flows supporting AI workloads.

By embedding policy controls, lineage tracking, and pre-deployment checks into AI pipelines, YASH helps organizations reduce data risk, improve accountability, and maintain trust in AI systems—without slowing innovation.

Assessment Phase

Activities

• Inventory AI-relevant data sources, feature stores, notebooks, and model endpoints
• Baseline discovery and classification of sensitive data
• Analyze data exposure, access paths, and cross-tenant sharing
• Define AI data categories, labels, and policy requirements
• Identify high-risk data flows and “toxic combinations”

Benefits

• Clear visibility into AI data risk and exposure
• Faster identification of sensitive data used by models
• Strong foundation for responsible and secure AI adoption

Deliverables

• AI data posture assessment report
• Lineage views highlighting sensitive data flows
• DSPM policy design and remediation roadmap

Implementation Phase

Activities

• Configure Microsoft Purview scans and auto-labeling
• Implement DLP, egress, and access controls for AI workloads
• Integrate policy checks into CI/CD and MLOps pipelines
• Build posture dashboards, alerts, and remediation workflows
• Enable secure AI patterns for data science and engineering teams

Benefits

• Reduced risk of data leakage in AI pipelines
• Improved governance without disrupting development velocity
• Consistent enforcement of AI data security policies

Deliverables

• Operational DSPM controls and dashboards
• CI/CD policy gates and runbooks
• Standard operating procedures and knowledge transfer

BAU (Business-As-Usual) Phase

Activities

• Continuous monitoring of AI data exposure and drift
• Periodic lineage, access, and entitlement reviews
• Incident analysis and control tuning
• Evidence management for audits and model risk reviews

Benefits

• Sustained AI data security posture
• Reduced operational risk as AI usage scales
• Improved audit and compliance readiness

Deliverables

• Periodic DSPM posture and trend reports
• Updated policies and lineage snapshots
• Improvement backlog and adoption KPIs