Data Security Posture Management (DSPM) is becoming foundational to modern cloud and AI security. As organizations embed AI into everything from analytics to customer experiences, sensitive data now moves through complex, distributed pipelines that span AWS, Azure, GCP, and SaaS environments. Training datasets, vector stores, and model outputs often contain regulated or proprietary information—and traditional data loss prevention tools can’t see or secure it.
DSPM provides the continuous, code-to-cloud visibility required to manage this new data sprawl. It automatically discovers where data lives, classifies its sensitivity, tracks how it flows through applications and AI systems, and enforces the right access and compliance controls. By extending the same agentless, risk-based visibility that transformed cloud infrastructure security to data and AI workloads, DSPM ensures innovation doesn’t come at the expense of protection. In a world where AI depends on data, securing that data is securing the future of your business.
In this article, we’ll explore the core use cases of DSPM that help organizations secure data wherever it resides or moves:
Shadow data discovery across multi-cloud environments
Intelligent data classification and sensitivity mapping
Real-time data flow visualization and lineage tracking
Automated compliance monitoring and reporting
Risk-based access governance
Proactive data breach prevention through attack path analysis
AI and machine learning workload protection
Cost optimization through data hygiene and lifecycle management
Choosing a DSPM Solution? Start Here.
Our buyer’s guide breaks down the must-have features, evaluation criteria, and an RFP template to help you make the right choice.
Use case #1: Shadow data discovery across multi-cloud environments
Shadow data discovery finds what you don’t know exists – unmanaged buckets, orphaned snapshots, forgotten test databases, and datasets lingering in development or AI pipelines. As data creation accelerates across AWS, Azure, GCP, and SaaS-connected stores, DSPM continuously inventories every dataset and pinpoints where sensitive records actually reside. This eliminates blind spots created by self-service provisioning and forms the foundation for classification, access governance, and risk reduction.
Modern organizations struggle with shadow data because developers and data scientists can spin up new resources and pipelines independently. This agility fuels innovation but creates sprawling, unmonitored data footprints. Customer records might live in an unused S3 bucket, or training data might persist in a test environment long after a model is deployed.
A DSPM platform continuously scans your entire cloud estate to build a live inventory of all data assets – structured and unstructured. Automated discovery replaces manual audits that can’t keep pace with cloud velocity. Once you know where all data lives, you can start securing it intelligently.
The discovery process includes:
Scanning cloud storage: Identifying buckets, blob stores, and file shares across all providers.
Analyzing databases: Finding managed and self-hosted SQL, NoSQL, and analytics systems.
Checking compute resources: Examining ephemeral data on VMs, containers, and serverless functions.
Mapping data flows: Understanding how data moves between services, pipelines, and applications.
Organizations that implement shadow data discovery frequently uncover sensitive datasets they didn’t know existed—including those feeding AI models or stored in transient pipelines. This foundational visibility is what enables everything else DSPM delivers.
Use case #2: Intelligent data classification and sensitivity mapping
Once data is discovered, the next step is understanding what it contains and how sensitive it is. Data classification automatically categorizes information based on sensitivity, regulatory requirements, and business impact – critical context for prioritizing risk. You can’t protect what you don’t understand.
Manual classification breaks down at cloud scale. With petabytes of data across thousands of data stores, DSPM tools use machine learning and pattern recognition to automatically detect sensitive information and tag it appropriately.
DSPM platforms identify and label key data types such as:
Personally identifiable information (PII): Names, addresses, SSNs, and email addresses.
Protected health information (PHI): Medical records, treatment details, and patient identifiers.
Financial data: Credit cards, bank accounts, or payment tokens.
Intellectual property: Proprietary code, model artifacts, and trade secrets.
These automated labels drive downstream policy enforcement – ensuring high-sensitivity data receives stronger protections while minimizing friction for lower-risk assets.
Advanced DSPM solutions also correlate classification results with runtime context – like whether sensitive data is stored in a public bucket or tied to over-privileged identities. This connection moves data classification from static labeling to actionable insight.
Use case #3. Real-time data flow visualization and tracking
Once data is discovered and classified, the next challenge is understanding how it moves. Data lineage and flow visualization reveal every point where information is stored, processed, or transferred – across applications, cloud services, and AI pipelines. Without this visibility, teams can’t tell when sensitive data crosses environments or when it leaves approved boundaries.
DSPM platforms map these data flows in real time by tracking API calls, service interactions, and cloud-native events. This gives security teams a visual view of how data travels from source to storage to processing – and highlights when movement deviates from policy. You can see, for example, when production data is copied into a development environment or when information flows into an AI training dataset stored in a different region.
This visibility helps answer critical questions:
Is sensitive data being copied to unprotected or external locations?
Are developers or AI systems inadvertently exposing customer data in logs or model outputs?
Is data moving between geographic regions with differing compliance requirements?
DSPM tracks:
Application-to-database connections: Which workloads and services access which data.
Cross-service transfers: How data moves between containers, microservices, or storage systems.
External API calls: When data leaves your environment through integrations or webhooks.
This end-to-end view of data lineage accelerates incident investigation, compliance audits, and risk assessment. When something goes wrong, you know exactly where sensitive data originated, where it traveled, and where it now resides – crucial for both security and governance.
Use case #4. Automated compliance monitoring and reporting
Compliance doesn’t happen once a year – it must be maintained continuously. DSPM automates this process by comparing your actual data posture against frameworks like GDPR, HIPAA, PCI DSS, and SOC 2 in real time. Instead of relying on static audits that surface issues months later, DSPM provides continuous assurance that your data handling practices meet required standards.
The platform scans your cloud environment for common policy violations—unencrypted data, missing access logs, or retention periods that exceed legal limits – and alerts teams when something drifts out of compliance. Built-in reporting templates then generate audit-ready documentation on demand.
Automated compliance delivers measurable benefits:
Continuous monitoring: Real-time visibility into compliance status across all clouds and data stores.
Audit trail generation: Automatic tracking of who accessed what data and when.
Policy violation alerts: Immediate notifications when configurations break compliance rules.
Evidence collection: Pre-built reports that simplify auditor reviews.
When integrated within a CNAPP, DSPM compliance insights can also be correlated with identity, workload, and configuration risks—providing a unified compliance view across your entire cloud estate. For security and GRC teams, this reduces manual workload while strengthening governance.
Use case #5. Risk-based data access governance
Knowing where sensitive data lives isn’t enough – you also need to know who can access it and why. Traditional access controls often grant broad permissions that accumulate over time, leaving sensitive data exposed. DSPM introduces risk-based access governance, which analyzes permissions, behavior, and usage patterns to enforce least privilege at scale.
Modern DSPM tools evaluate both static access configurations and dynamic user activity to uncover risks such as:
Dormant accounts: Users who retain access but haven’t logged in for months.
Excessive permissions: Individuals or service accounts with admin-level rights they never use.
Anomalous behavior: Sudden spikes in data queries or large-scale downloads.
Shared credentials: Multiple users accessing data through the same account.
By combining data sensitivity with access context, DSPM surfaces which identities present the highest risk to your most valuable information.
When integrated with Cloud Infrastructure Entitlement Management (CIEM) capabilities, these insights create a closed loop—linking data access directly to identity posture. The result is smarter, context-aware access decisions that protect sensitive data without disrupting productivity.
Use case #6. Proactive data breach prevention through attack path analysis
Traditional tools detect isolated issues – an exposed database here, a misconfigured permission there—but rarely show how they connect to create real exploit paths. DSPM changes that. It identifies how multiple weaknesses can chain together to put sensitive data at risk, transforming data protection from reactive to proactive.
Through continuous analysis, DSPM correlates signals from cloud configurations, network exposures, identity permissions, and data locations. For example, it might reveal that an unencrypted database containing customer PII is accessible by an over-privileged service account and exposed to the public internet. Individually, these might seem low severity; together, they form a clear attack path to sensitive data.
Attack path analysis examines:
Network exposure: Which data stores are reachable from the internet or external networks.
Identity risk: Which accounts have excessive or unused permissions to sensitive datasets.
Encryption status: Whether data is protected at rest and in transit.
Vulnerability context: Known flaws in systems or containers that host sensitive data.
By understanding how risks combine, security teams can prioritize remediation where it has the greatest impact—fixing the few paths that truly expose critical data.
In a unified CNAPP, DSPM insights integrate directly with cloud misconfigurations, CIEM, and vulnerability data—providing full code-to-data attack path visibility so you can remediate issues at their root cause.
Use Case #7. AI and machine learning workload protection
As organizations adopt AI, the data fueling these systems introduces new security and privacy challenges. Training datasets often include sensitive information, embeddings can reveal private data through model inversion, and AI pipelines create complex, dynamic data flows across storage, compute, and SaaS tools.
DSPM extends protection to these AI workloads by continuously discovering and classifying data used in training, testing, and inference. It identifies whether regulated or proprietary data is being ingested into models, ensures proper access controls around model repositories, and monitors how data moves through MLOps pipelines.
AI workload protection includes:
Training data security: Detecting sensitive or unredacted data in datasets and vector stores.
Model access control: Restricting who can retrieve or modify AI models and ensuring traceability of actions.
Inference data protection: Safeguarding the inputs and outputs of model APIs from exposure or leakage.
Pipeline visibility: Mapping how data flows across preprocessing, training, and deployment stages.
By applying DSPM principles to AI, organizations gain visibility and control across the full AI lifecycle. Instead of creating new silos, DSPM extends existing data governance and risk-based policies to AI systems – enabling secure innovation and regulatory compliance for GenAI and ML workloads.
Use case #8. Cost optimization through data hygiene and lifecycle management
Data security and cost efficiency often go hand in hand. As cloud environments grow, so does the amount of redundant, obsolete, and trivial (ROT) data consuming expensive storage. DSPM helps organizations reduce both cost and risk by identifying data that no longer has business or compliance value.
Through automated scans, DSPM detects:
Orphaned storage: Snapshots, volumes, or buckets detached from any active system.
Duplicate data: Redundant copies of the same files across environments.
Expired data: Information beyond its required retention period.
Unused resources: Data stores that haven’t been accessed for long periods.
Eliminating ROT data reduces your attack surface and cloud storage spend simultaneously. Every unnecessary dataset is not only a cost liability but also a potential exposure point. By enforcing lifecycle policies – archiving, tiering, or deleting unused data – DSPM helps teams maintain a leaner, safer, and more compliant data environment.
When integrated with a CNAPP, these insights tie back to business context: which teams own the data, which workloads depend on it, and what security or compliance implications exist. That unified visibility turns cleanup into a strategic initiative – not just cost control, but proactive risk reduction.
Implementing DSPM with Wiz
Data today doesn’t stay in one place—it fuels cloud workloads, analytics pipelines, and increasingly, AI systems. Wiz unifies Data Security Posture Management (DSPM) with its broader CNAPP platform to give organizations a single, agentless view of where sensitive data lives, how it moves, who can access it, and how it’s used across both traditional and AI environments.
Wiz’s DSPM approach is built on three key pillars:
Unified visibility across data, identity, and AI pipelines
Wiz connects DSPM with CSPM, CIEM, and vulnerability management to surface complete attack paths—from a misconfigured data store to the service account or model pipeline that could expose it. This gives teams one correlated view of data risk across their entire environment.Agentless, continuous discovery and classification
Wiz continuously scans across AWS, Azure, GCP, SaaS, and AI systems – discovering shadow data, classifying sensitive records, and detecting policy drift without installing agents or impacting performance.Code-to-cloud and model-to-data traceability
Through Wiz Code, data exposure findings are traced back to the IaC or developer change that introduced them. Wiz also extends that traceability to AI workloads—mapping sensitive training data, vector databases, and model outputs within the same risk graph.
This unified view means Wiz doesn’t just help you find sensitive data – it helps you protect it wherever it’s used. From cloud storage to AI pipelines, from code to runtime, Wiz enables security teams to reduce real exploitable risk with the same agentless, contextual visibility that defines its CNAPP platform.
Wiz secures the data that powers your AI, your applications, and your business – everywhere it lives.
Get a 1:1 demo of your data risks
See how Wiz DSPM automatically discovers sensitive data, maps where it lives, and shows exactly how it could be accessed or exposed — all in minutes.
