Data Security & Purview Assessment

Find where sensitive data is exposed, overshared, unlabeled, or moving without effective controls across Microsoft 365, endpoints, AI apps, and other in-scope data platforms.

Where is sensitive data exposed?

This assessment is for security, compliance, privacy, and Microsoft 365 leaders preparing for Microsoft 365 Copilot or other AI adoption; responding to data leakage or oversharing; facing audit findings; or unsure whether sensitivity labels and DLP policies are protecting the right data. It is most useful when the organization has Purview capabilities but lacks a clear, risk-based operating model.
We review the Microsoft Purview capabilities and data locations relevant to your goals: Data Security Posture Management, Information Protection, sensitivity labels and policies, Data Loss Prevention across Exchange, SharePoint, OneDrive, Teams, endpoints, and supported browsers; Insider Risk Management; Data Security Investigations; and AI data interactions. Azure, Microsoft Fabric, and supported third-party data sources can be included when licensed and in scope.
You receive a Purview Data Security Posture Report, Sensitive Data Exposure Map, classification and labeling coverage matrix, DLP and insider-risk control review, prioritized findings, and a 90-day remediation roadmap. Sample findings may include unlabeled sensitive files, broadly shared sites, DLP rules creating noise, endpoint channels outside policy coverage, inconsistent label publishing, or sensitive content exposed to AI tools.
Our process

Turn data visibility into enforceable controls

We trace sensitive data from discovery and classification through access, sharing, movement, alerts, and response. Each finding is tied to a business scenario, affected data location, control gap, user impact, owner, and recommended action so protection can improve without blocking normal work.

Define sensitive data and business risk

We align the assessment to the data that matters most: regulated records, intellectual property, financial information, customer data, and AI training or grounding content. Inputs include current data classifications, regulatory obligations, sensitivity-label taxonomy, business owners, priority repositories, and recent incidents.
Data priorities

Measure discovery and labeling coverage

We review DSPM posture metrics and objectives, data discovery results, sensitive information types, trainable classifiers, label publishing and auto-labeling policies, activity explorer, and asset-level exposure. We check whether important data can be found, classified, labeled, and traced across the agreed locations.
Exposure review

Test DLP and insider-risk controls

We examine DLP policy scope, rule logic, simulation results, endpoint and browser channels, alerts, overrides, exclusions, and incident workflow. Where applicable, we review Insider Risk Management signals, policies, privacy controls, role separation, and escalation procedures with HR and legal boundaries respected.
Control validation

Prioritize protection and AI readiness

We rank findings by data sensitivity, exposure, user activity, control coverage, operational impact, effort, and dependency. The roadmap separates quick policy corrections from label redesign, DLP pilots, DSPM rollout, insider-risk program work, and Microsoft 365 Copilot or AI data-protection readiness.
Remediation roadmap