7 Proven Steps to Uncover Shadow Data with DSPM Tools
Shadow data—sensitive information saved outside sanctioned systems such as personal cloud drives, ad hoc backups, or email attachments—evades standard controls and is a leading cause of silent data exposure.
The fastest way to identify it is by deploying DSPM to continuously discover, classify, and remediate sensitive data across cloud, SaaS, and on-prem environments. In this blog post, we provide seven proven steps security leaders can follow to find and fix shadow data with confidence.
As you progress, centralize data governance on a unified, zero trust security foundation like the Kiteworks Private Data Network to streamline encryption, access, and auditability at scale.
Executive Summary
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Main idea: Deploy DSPM to continuously discover, classify, and remediate shadow data across cloud, SaaS, and on-prem, anchored by a zero trust architecture foundation like the Kiteworks Private Data Network.
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Why you should care: Shadow data silently expands risk and regulatory compliance exposure; a structured DSPM program with automated remediation and continuous monitoring reduces breach likelihood, audit friction, and operational effort.
Key Takeaways
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Shadow data thrives outside sanctioned systems. DSPM integrates with cloud, SaaS, and data stores to surface unmanaged data, making hidden risks visible and actionable.
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Identity and data flow mapping expose risky replicas. Overlay users, service accounts, and datasets to find stale backups, unsanctioned syncs, and over-privileged access.
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Continuous monitoring enforces zero trust. Detect anomalies in near real time and map controls to regulations like GDPR, HIPAA, and CCPA.
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Automated remediation shrinks exposure windows. Enforce least privilege, encrypt or quarantine data, and orchestrate approvals with full audit trails.
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A unified platform simplifies governance and audits. Kiteworks centralizes policy, encryption, and logging, with a CISO Dashboard for clear risk and compliance visibility.
You Trust Your Organization is Secure. But Can You Verify It?
1. Integrate and Discover Data Across Cloud and SaaS Environments
Integrating DSPM tools with major cloud services, data stores, and SaaS applications enables holistic visibility over all organizational data sources—essential for uncovering unmanaged or shadow data. For program structure and scope, see DSPM framework implementation guidance from Spin.AI. Shadow data is best defined as sensitive information stored outside sanctioned systems, often in personal cloud drives or email attachments, posing serious risk, according to research on shadow data risks from Netwrix.
A practical integration flow:
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Connect to enterprise cloud platforms (AWS, Azure, Google Cloud) using least-privilege roles.
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Integrate repositories, SaaS apps, object/file storage, and data warehouses.
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Pull content (file/database scans), context (metadata, location, sharing), and ownership (users, service accounts) to drive precise cloud data classification and discovery.
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Use agentless, automated scanning to maximize coverage and minimize operational friction; see agentless DSPM scanning comparison from Sentra.
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Establish baselines for data visibility and SaaS security across tenants.
Tip: Validate capabilities against DSPM essential features that matter in regulated environments, including end-to-end encryption, zero trust data exchange controls, and unified auditability.
2. Map Identities and Data Flows to Reveal Shadow Data
IAM mapping overlays users, service accounts, and datasets to reveal over-privileged access; data flow mapping traces transformations across ETL processes, APIs, and integrations—see identity and data flow mapping in DSPM from Relyance. Tracking pipelines and integrations often exposes hidden replicas, stale backups, or unsanctioned syncs where shadow data accumulates.
Common data flow sources to inspect with identity overlays:
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Data flow source |
Example identities |
Typical shadow data indicator |
What to check |
|---|---|---|---|
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Backups & snapshots |
Backup services, storage admins |
Orphaned copies with broad read access |
Retention policies, access controls, encryption state |
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Test/dev environments |
CI/CD bots, developers |
Production PII/PHI in non-production |
Masking/synthetic data use, network egress, privilege levels |
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App connectors & iPaaS |
Integration service accounts |
Silent replication to third-party SaaS |
OAuth scopes, token rotation, data minimization |
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Analytics/ETL pipelines |
Data engineers, BI tools |
Untracked exports to object storage |
Data lineage, bucket policies, lifecycle rules |
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Email/drive syncs |
End users, departmental IT |
Sensitive files in personal drives |
Sharing settings, external collaborators, link exposure |
Embedding data lineage with privileged access views surfaces shadow IT patterns early—before they spread.
3. Implement Continuous Monitoring for Anomalies and Compliance
Continuous monitoring is the real-time, ongoing analysis of data access patterns and flows to detect anomalies, enforce policy, and ensure regulatory compliance. IBM guidance on continuous DSPM monitoring notes that modern platforms continuously scan and track data locations to make sensitive information visible and protected across complex estates.
Use continuous monitoring to:
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Detect out-of-policy access or anomalous data flows (e.g., sudden mass downloads, unexpected cross-region moves).
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Provide near-real-time compliance monitoring and reporting mapped to GDPR, HIPAA, and CCPA.
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Enforce zero trust data protection by verifying every entity’s access continuously; see Zero Trust data protection use cases from CrowdStrike.
In Kiteworks, these controls are anchored by a unified policy plane and centralized logging within the Kiteworks Private Data Network, simplifying evidence for audits and incident response investigations.
4. Assess Risks and Mitigate Vulnerabilities Proactively
Risk assessment is the process of evaluating vulnerabilities, misconfigurations, and unauthorized access that could lead to data breaches or compliance violations. Tenable’s overview of DSPM highlights how dashboards prioritize issues by severity to accelerate detection and response—vital when shadow data expands the attack surface in minutes, not months.
Prioritize and resolve:
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Open or misconfigured storage buckets (public ACLs, lax cross-account sharing).
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Overexposed backup files and snapshots (stale, unencrypted, widely readable).
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Unencrypted sensitive records in test/dev databases (production PII/PHI copied to lower-trust zones).
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Excessive permissions on service accounts (standing admin roles, unused tokens).
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Shadow SaaS repositories created via self-serve sign-ups.
Adopt tailored risk scoring to focus remediation where impact is highest: data sensitivity, exposure breadth, external accessibility, and blast radius. For deployment guidance and controls mapping, see the Kiteworks DSPM data sheet.
5. Classify Shadow Data in Motion and at Rest
As covered in DSPM for data in motion, comprehensive solutions discover and classify cloud data in motion, not just data at rest—crucial for catching exfiltration risks as they emerge. Data classification is the automated process of identifying and labeling sensitive data—such as PII, PHI, financial records, or intellectual property—across storage locations, repositories, and data streams.
How DSPM delivers accurate classification:
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Scan all connected repositories at scale (structured and unstructured) with content, context, and ownership signals.
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Tag sensitive data types so they appear in dashboards, DLP rules, and access policies.
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Extend discovery to unmanaged data in rarely scanned paths (e.g., email attachments, personal drives, and other unmanaged data from shadow IT).
This strengthens reporting, tightens access control, and closes data compliance gaps before audits.
6. Automate Remediation to Reduce Exposure Quickly
Automated remediation is the process where security tools automatically respond to detected risks—such as security misconfiguration or exposed data—to minimize manual effort and reduce the window of exposure. Palo Alto Networks on DSPM tool capabilities emphasizes policy-driven workflows that enforce least privilege and remediate risks in real time.
Common automated responses:
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Encrypt or quarantine exposed files to contain blast radius.
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Change permissions or access control to least privilege.
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Move sensitive data to compliant storage with correct retention and data residency.
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Delete obsolete or unauthorized shadow copies after approvals.
Couple automated response with human-in-the-loop change control for high-risk actions and log every step for auditability.
7. Regularly Review and Update DSPM Strategies for Emerging Risks
Threats, architectures, and regulations evolve—your DSPM must, too. Establish a quarterly (or monthly) review cycle: reassess scope and connectors, tune policies, refresh data dictionaries, validate tagging accuracy, and align controls with new regulatory or business requirements. TechTarget’s DSPM definition and practices underscore an adaptive approach that keeps pace with expanding data sprawl.
Track KPIs to drive continuous improvement:
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Shadow data repositories discovered and remediated.
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Reduction in high-risk exposures and mean time to remediate (MTTR).
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Percentage of sensitive records covered by policy enforcement.
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Compliance posture improvements evidenced in dashboards and audit reports.
How Kiteworks Helps Organizations Identify and Protect Shadow Data
Kiteworks uniquely complements DSPM by consolidating sensitive content communications and repositories into a hardened Private Data Network that enforces zero-trust controls, consistent encryption, and unified data governance. It operationalizes DSPM findings by streamlining remediation, reducing sprawl, and delivering audit-ready evidence.
What sets Kiteworks apart:
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Private Data Network: Centralize secure file transfer, sharing, and repositories with end-to-end encryption and granular policy orchestration; see the Kiteworks Private Data Network.
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Actionable visibility: The CISO Dashboard provides unified risk, compliance, and activity metrics to prioritize remediation across channels.
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Policy and control plane: Enforce least-privilege access, retention, and data sovereignty while quarantining, encrypting, or moving sensitive data automatically.
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DSPM synergy: With Kiteworks Plus DSPM, organizations extend discovery to data in motion, orchestrate policy-driven responses, and maintain immutable logs for audits.
To learn more about protecting the shadow data uncovered by DSPM tools, schedule a custom demo today.
Frequently Asked Questions
Shadow data is sensitive information created or stored outside sanctioned systems—think personal cloud drives, email attachments, ad hoc backups, and unsanctioned SaaS. Because it escapes standard monitoring, AES 256 encryption, and access controls, it becomes invisible risk. Attackers, insiders, and misconfigurations can expose it, creating compliance gaps, larger breach blast radii, and incident response blind spots.
DSPM integrates with cloud, SaaS, and on-prem repositories to continuously scan content and metadata, correlating signals like data type, location, sharing, and ownership. It labels sensitive items (e.g., PII, PHI, IP) and maps access, lineage, and flows. This holistic view surfaces unmanaged copies, risky permissions, and exfiltration paths, enabling policy enforcement and automated remediation.
Common blind spots include personal cloud accounts, stale backups and snapshots, old test/dev environments containing production data, email attachments, analytics exports in object storage, and unsanctioned SaaS spun up by departments. These locations often lack masking, encryption, and data governance, making them prime accumulation points where sensitive data silently proliferates and evades standard controls.
Continuously. Real-time or near-real-time monitoring detects new data creation, movement, and exposure as they occur. At a minimum, daily discovery baselines should be paired with event-driven scans for changes, identity shifts, and policy updates. Combining continuous monitoring with automated remediation keeps risk windows short and supports ongoing regulatory compliance.
Track discovery, exposure, and response. Examples: number of shadow repositories found, percent reduction in overexposed records, mean time to remediate (MTTR), policy coverage of sensitive records, and reductions in publicly accessible buckets or excessive permissions. Tie metrics to compliance dashboards and audit logs to demonstrate sustained risk reduction and control effectiveness.
Additional Resources
- Brief Kiteworks + Data Security Posture Management (DSPM)
- Blog Post DSPM vs Traditional Data Security: Closing Critical Data Protection Gaps
- Blog Post DSPM ROI Calculator: Industry-Specific Cost Benefits
- Blog Post Why DSPM Falls Short and How Risk Leaders Can Mitigate Security Gaps
- Blog Post Essential Strategies for Protecting DSPM‑Classified Confidential Data in 2026