Getting Started with Sensitive Data Scanning

Purpose of this Guide

One of the most important foundations of a strong data security posture is understanding your sensitive data: what it is, where it lives, who can access it, and how it is used or shared. Sensitive Data Scanning (SDS) is designed to give organizations this visibility quickly and at scale, without relying on manual tagging or exhaustive upfront governance work.

This guide provides a practical, step-by-step framework to help organizations successfully launch and operationalize a sensitive data scanning program. It is written for teams that may be new to SDS and want clear strategic intent paired with concrete, tactical actions.

The goal is not to achieve perfect coverage on day one. The goal is to establish visibility, reduce uncertainty, and create a repeatable motion that improves over time.

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Four Questions This Guide Answers

• Where should I start scanning?
• How much data should I scan initially?
• How do I choose the right classifiers?
• What should I do with the results?

Each section below provides recommended practices, common pitfalls, and a clear deliverable.

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1. Where Should I Start Scanning?

A common initial reaction to sensitive data scanning is feeling overwhelmed by the sheer volume of data across the organization. Rather than starting with systems or platforms, it is more effective to begin with intent and work inward toward the data most likely to create risk.

Start with Business Objectives

Begin by clearly defining why you are implementing sensitive data scanning. Common objectives include:

• Meeting regulatory or audit requirements
• Reducing the risk of regulatory fines or data breaches
• Controlling how sensitive data is used in analytics or AI applications
• Gaining visibility into data sprawl and unmanaged copies of sensitive data

Your objectives will directly influence what you scan first and how deeply you scan it.

Move to Data Subjects and High-Risk Entities

Once objectives are defined, focus on the data subjects and entities most likely to contain sensitive data. For most organizations, a natural priority order is:

• Customers or clients
• Users or account holders
• Employees
• Vendors and partners
• Prospects and leads

These entities typically hold regulated, personal, or financial data by design and therefore represent the highest risk starting points.

Rather than attempting to scan everything at once, select one or two high-priority entities and identify where they are stored across production systems, analytics platforms, and downstream copies.

Practical Considerations

• Do not assume Master Data Management (MDM) systems are the only source of truth. Sensitive data often exists across CRMs, data warehouses, analytics platforms, document stores, and SaaS applications.
• Watch for duplication. Production datasets are frequently copied into downstream systems for reporting, testing, or experimentation. These “clone of clones” environments often carry the same sensitivity with fewer controls.

Once core entities are covered, expand incrementally using:

• Input from data stewards and domain experts
• Profiling and analyzing results from SDS
• Conversations with data architecture, security, and GRC teams

Step Deliverable: Set defined business objectives that will be accomplished or aided by SDS and a prioritized list of entities and data domains to target for discovery and classification.

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2. How Much Data Should I Scan Initially?

Sensitive data scanning does not need to start at 100% coverage. A more effective and sustainable approach is to think in phases, expanding coverage as confidence and clarity increase.

A general and pragmatic approach is to think in phases:

• Initial Discovery (10–20%): Sample enough data to identify sensitive fields, schemas, and validate patterns discovered.
• Expansion (50%): Ensure coverage across critical systems and environments.
• Full Coverage (100%): Required when regulatory, legal, or risk posture demands it.

As you move into the Expansion and Full Coverage phases, it is important to align scanning depth and frequency with your primary objective. Two common goals help orient scanning strategy:

Discover Lurking Sensitive Data

The goal is to ensure sensitive data does not exist in unexpected or unmanaged locations.

• Emphasis is placed on breadth and depth, not frequency
• Less frequent but more thorough scans are common
• Full scans are appropriate for datasets with inconsistent schemas, mixed data types, or prior profiling signals indicating variability
• Sampling is effective for surfacing unknown sensitive fields and patterns, but should be treated as directional insight, not confirmation

Data Loss Prevention

The goal is to prevent sensitive data from being shared, copied, or exposed inappropriately as data changes over time.

• Focus is on high-change, high-risk data assets
• More frequent scans (daily, weekly, or monthly) are appropriate where data is actively updated
• Incremental scanning techniques can enable higher frequency with lower compute and operational cost

Sampling is often sufficient to detect the presence and structure of sensitive data, especially during early discovery. However, organizations subject to regulations such as GDPR or GLBA should recognize that the presence of even a single regulated data subject can trigger broader compliance obligations.

Legal and compliance teams should be treated as partners in determining when sampling is appropriate and when full coverage is required, particularly as scanning results inform enforcement or reporting decisions.

Step Deliverable: A defined list of data assets to scan, including scan details, and the coverage phase.

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3. How Do I Decide Which Classifiers to Use?

Chosen classifiers should align with your organization’s goals and regulatory requirements. Classifiers identify sensitive data that fall into two categories:

Direct Sensitivity

These data elements are sensitive on their own and should be prioritized first:

• Social Security Numbers
• Tax IDs
• Credit card numbers
• Email addresses and login identifiers

Indirect or Contextual Sensitivity

Some data elements become sensitive only when combined, such as:

• Full physical addresses
• Location combined with identity
• Medical codes combined with identifiers
• Financial attributes combined with customer identity

Early scanning efforts should focus on high-confidence direct identifiers. Contextual and composite sensitivity can be layered in later as your scanning maturity improves.

Step Deliverable: An agreed-upon set of initial classifiers, with a roadmap for expansion.

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5. What Should I Do With the Results?

At its core, SDS answers one critical question:

Can this data be accessed or shared safely, and under what conditions?

Typical next steps after results are returned include:

• Documenting classifications in a data catalog or inventory
• Defining access and sharing rules by data class
• Identifying policy violations or unexpected data locations
• Creating remediation tasks or tickets for security and data owners

Over time, sensitive data scanning results should feed broader initiatives such as data access governance, privacy impact assessments, and security posture management.

Step Deliverable: Documented classifications in data catalog and a prioritized list of actions based on risk and exposure.

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Personas Involved in Sensitive Data Scanning

Sensitive Data Scanning is rarely owned by a single role. Common participants include:

• Privacy and Data Protection Analysts
• Data Security Architects
• GRC and Compliance Teams
• Internal and External Auditors
• Data Stewards and Domain Experts

These personas typically contribute in three ways:

• Scoping: Determining what data to scan and what matters most
• Execution: Running scans and managing configurations
• Analysis: Reviewing results and driving remediation

Some organizations centralize these responsibilities; others distribute them across teams. Both models can succeed with clear ownership and collaboration.

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Closing Thoughts

SDS is not a one-time project. It is a capability that improves continuously as environments evolve and data usage expands.

Organizations that invest early in SDS are better positioned to support analytics, AI, and data sharing initiatives safely and confidently. If SDS feels optional today, it will not remain so for long.

Start small, focus on visibility, and build from there.