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

Financial Services

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