- Microsoft Purview’s DSPM capabilities are not included in standard Microsoft 365 or Azure subscriptions. The features most security teams expect require Microsoft 365 E5 Compliance or a standalone Purview add-on, and even then, coverage outside Microsoft-native data stores is limited.
- Purview now ships three distinct DSPM surfaces: the new unified DSPM experience, DSPM classic, and DSPM for AI. Each covers different data types and risk signals. Treating them as interchangeable leads to gaps in coverage.
- For organizations running data in Azure alongside AWS, GCP, Snowflake, or on-premises systems, third-party DSPM tools cover terrain that Purview does not reach without significant additional licensing.
- The licence audit comes before the tool evaluation. Know exactly which Purview tier you own before pricing any third-party alternative, because the overlap may be larger or smaller than you expect.
- Wiz, Cyera, Varonis, BigID, Securiti, Sentra, Netskope, and Zscaler each address different parts of the Azure data security problem. None of them replaces Purview’s Microsoft-native depth for M365 data.
The best DSPM tools for Azure depend on which Purview tier you already own and where your sensitive data actually lives. For organizations whose data is mostly inside Microsoft 365 and Azure-native services, the right starting point is a Purview licence audit, not a third-party purchase. For organizations with significant data in non-Microsoft clouds, SaaS platforms, or self-managed databases, third-party DSPM for Azure from vendors like Wiz, Cyera, or Varonis fills coverage gaps that Purview cannot reach regardless of licence tier.
What Do Teams Usually Get Wrong About Purview DSPM Before They Start Shopping?
The common assumption is that Purview comes with Azure and therefore covers Azure data security posture. It does not work that way. Microsoft 365 E3, which most enterprises already hold, gives access to basic data classification and sensitivity labelling through Purview. The DSPM features, specifically the risk dashboards, cross-cloud data discovery, and the unified DSPM experience, sit behind the Microsoft 365 E5 Compliance licence or the Microsoft Purview add-on for customers who hold E3.
Compounding this, Microsoft has restructured its DSPM surface area into three distinct experiences over the past two years. A team that implemented Purview eighteen months ago and has not revisited it is likely running DSPM classic, which has a different feature set from both the current unified DSPM experience and DSPM for AI. These are not interchangeable interfaces to the same underlying engine. The data they surface, the policies they support, and the risk signals they generate differ in meaningful ways.
This article maps the Purview tiers to what they actually cover, then assesses eight third-party tools across the capabilities that matter for Azure environments: data discovery breadth, classification accuracy, identity-to-data risk correlation, remediation workflow, and cross-cloud consistency.
What Does Microsoft’s Native DSPM Actually Cover, and at Which Licence Tier?
Microsoft now maintains three separate DSPM surfaces inside Microsoft Purview. Understanding what each covers is the prerequisite for any third-party evaluation.
The Unified DSPM Experience
The current unified DSPM experience, documented by Microsoft as the primary DSPM surface in Purview, is designed to locate, assess, and prioritize high-risk data across both Microsoft and non-Microsoft services. According to Microsoft’s documentation, it provides a central hub for data risk visibility and connects to Microsoft Defender for Cloud for infrastructure context. This experience is available to customers with Microsoft 365 E5 Compliance licensing or the Purview add-on. It pulls signals from sensitivity labels, data classification results, and Defender for Cloud’s CSPM findings to surface a combined data and infrastructure risk posture.
DSPM Classic
DSPM classic, documented separately by Microsoft, is the earlier iteration. According to Microsoft’s learn.microsoft.com documentation, it provides cross-cloud data and user risk monitoring through a dashboard view that surfaces overshared data, data exposure risks, and policy violations. DSPM classic connects to Microsoft 365 data sources and supports some Azure data services, but its non-Microsoft coverage is narrower than the unified experience. Teams running DSPM classic are operating a feature set that Microsoft is actively superseding.
DSPM for AI
According to Microsoft’s documentation, DSPM for AI is purpose-built for Microsoft Copilot and other AI applications running on Microsoft infrastructure. It provides a central management location to secure data for AI apps and proactively manage AI-related data risks. Its scope is limited to AI interactions and the data those interactions touch; the documentation does not enumerate which non-Copilot AI applications fall within that scope. It does not function as a general-purpose data discovery or classification engine. Organizations concerned about AI data exposure in Microsoft environments should look at it, but it does not substitute for either of the other two surfaces.
Defender for Cloud DSPM
Microsoft Defender for Cloud includes data security posture management as part of its Defender CSPM plan, which is a paid tier on top of the free foundational CSPM. Defender for Cloud DSPM focuses on Azure-native data resources: Azure Blob Storage, Azure SQL, Azure Cosmos DB, and similar services. It discovers sensitive data in those resources, maps exposure paths, and integrates those findings into the attack path analysis that Defender CSPM generates. This is a distinct surface from Purview DSPM, and the two generate overlapping but not identical findings. Organizations that own Defender CSPM have meaningful Azure data posture coverage that many teams do not realize they already have.
The Licence Tier Map
| Microsoft Product | Licence Required | Primary Data Scope | Non-Microsoft Cloud Coverage |
|---|---|---|---|
| Purview unified DSPM | M365 E5 Compliance or Purview add-on | M365, Azure-native services, limited non-MS | Limited, expanding |
| DSPM classic | M365 E5 Compliance | M365, some Azure services | Minimal |
| DSPM for AI | M365 E5 Compliance or Copilot add-on | Microsoft AI interactions only | None |
| Defender for Cloud DSPM | Defender CSPM (paid tier) | Azure-native data services | AWS, GCP in preview or limited GA |
Run the licence audit against this table before pricing anything else. Teams that already hold E5 Compliance and Defender CSPM have coverage that overlaps with a meaningful portion of what mid-tier third-party DSPM tools provide for Azure-native workloads.
Where Do Third-Party Tools Add Coverage in Azure?
Even at E5 Compliance with Defender CSPM, Purview has structural gaps for specific Azure deployment patterns. Three gaps come up repeatedly in evaluations.
First, data in non-Microsoft SaaS platforms that the organization reaches from Azure infrastructure. Purview’s classification engine does not scan Salesforce, Workday, Snowflake running outside Azure, or ServiceNow unless those connectors are configured and licensed separately, and even then the depth of classification is uneven. Second, self-managed databases on Azure VMs. Purview’s automatic discovery is strongest on platform-managed Azure services. A PostgreSQL instance running on an Azure virtual machine, or a MongoDB cluster on Azure Kubernetes Service, is a different story. Third, identity-to-data risk correlation at depth. Purview surfaces data exposure; it does not natively map which service accounts, human identities, or non-human identities can reach that exposed data and with what effective permissions.
That third gap is where the most operationally significant misconfigurations live. Sensitive data sitting in a storage account with a public endpoint is obvious. Sensitive data reachable by a service principal with excessive blob read permissions, through a chain of Azure role assignments that were never audited, is harder to find and more likely to appear in a real incident.
The 10 Best DSPM Tools for Microsoft Azure
1. Microsoft Purview DSPM (with Defender for Cloud)
Microsoft Purview with Defender for Cloud is the right starting point for any Azure-primary organization. The combination of Purview’s classification engine and Defender CSPM’s attack path analysis gives native depth that no third-party tool can replicate for Microsoft-managed services. Purview knows M365 data structures natively. Defender for Cloud understands Azure resource relationships natively.
The operational cost is configuration time and licence management. The unified DSPM experience requires deliberate setup: sensitivity labels need to be defined and trained, auto-labelling policies need scope decisions, and Defender CSPM’s data sensitivity settings need to be aligned with Purview’s classification taxonomy. Teams that deploy it without that configuration work get a dashboard with limited signal. Pricing for Defender CSPM is consumption-based and Microsoft does not publish a simple per-resource rate on a public pricing page; consult the Defender for Cloud pricing page for current estimates, as these figures change.
Best for: Organizations where the majority of sensitive data lives in M365 and Azure-native services, and where the team already holds E5 Compliance. Weakest for hybrid and multi-cloud environments with significant non-Microsoft SaaS.
2. Wiz DSPM
Wiz approaches data security posture as an extension of its cloud security graph, which means data findings are natively correlated with identity, network exposure, and workload context in the same graph. For Azure specifically, Wiz discovers data across Azure Blob Storage, Azure SQL, Cosmos DB, Azure Data Lake, and data inside Azure VMs and containers. Its strength is attack path analysis: a finding is not just “this storage account contains PII” but “this storage account contains PII, is publicly reachable, and is accessible by this managed identity, which also has contributor rights on this key vault.”
Wiz does not require agents, which reduces deployment friction significantly for large Azure environments. Coverage extends to AWS and GCP with the same data model, making it one of the stronger options for organizations running true multi-cloud. Wiz does not publish pricing publicly. The model is consumption-based and quoted per environment; contact Wiz sales for current figures.
Best for: Organizations already using Wiz for CSPM who want data context in the same platform without a second tool. The graph-based correlation genuinely adds operational value that is hard to replicate by bolting a separate DSPM tool onto a separate CSPM tool.
3. Cyera
Cyera describes itself as an AI Security Platform focused on protecting data and securing AI, with capabilities including DSPM, access governance, and agent security. For Azure, it scans structured and unstructured data across Azure storage services, databases, and connected SaaS platforms. Its classification engine is designed to handle context-dependent sensitive data, where the sensitivity of a record depends on the combination of fields present rather than any single field in isolation.
Cyera’s documentation describes an Access Trail capability positioned as a data access detective for humans and AI, which provides visibility into who and what is accessing sensitive data. For Azure environments where service principals and managed identities accumulate permissions over time without regular review, this type of access visibility is operationally useful. Cyera does not publish pricing. The model is consumption-based. For organizations evaluating Cyera, request a proof of value against a representative sample of production data to assess classification accuracy before committing.
Best for: Data-heavy organizations where the central problem is knowing what sensitive data exists and who can reach it, rather than organizations where infrastructure misconfiguration is the primary concern.
4. Varonis
Varonis has the longest track record in data access governance and brings that history to Azure. Its platform covers Azure Blob Storage, SharePoint Online, OneDrive, Exchange Online, and connected SaaS platforms with deep visibility into actual access patterns, not just permission grants. The distinction matters: Varonis shows you who is reading which files, not just who could read which files.
For Azure environments where the concern is insider risk, data sprawl across SharePoint and OneDrive, or detecting unusual access to sensitive data, Varonis delivers signal that Purview’s DSPM does not generate at the same depth. The platform includes automated remediation workflows that can remove excessive permissions without requiring manual review of each finding. Varonis prices per terabyte of managed data and per user for SaaS coverage; the vendor does not publish current list pricing and quotes based on environment scope.
Best for: Organizations where M365 data sprawl, SharePoint oversharing, and user behaviour around sensitive files are the primary risks. Less differentiated for pure IaaS/PaaS data stores compared to cloud-native alternatives.
5. BigID
BigID is built around data discovery at scale across structured and unstructured sources, with a particular strength in regulatory classification: GDPR, CCPA, HIPAA, PCI-DSS, and similar frameworks. For Azure, it connects to Azure SQL, Azure Data Lake, Azure Blob, Azure Cosmos DB, and a wide catalogue of non-Azure sources including on-premises databases, which matters for hybrid Azure deployments.
BigID offers an enterprise-grade data security platform with unified capabilities across discovery, risk management, and privacy compliance. Pricing is module-based and consumption-based; the vendor does not publish list rates. For organizations building a data security posture management program from scratch across multiple regulatory frameworks, BigID’s breadth of source connectors and classification taxonomies is a genuine advantage.
Best for: Compliance-driven programs that need classification coverage across a wide mix of Azure services, on-premises databases, and non-Microsoft SaaS, and where regulatory taxonomy depth matters as much as attack path context.
6. Securiti
Securiti positions its platform at the intersection of data security, privacy, and AI governance. For Azure environments, it provides data discovery and classification across Azure-native services and connected SaaS, with a risk scoring model that factors in regulatory exposure, access risk, and data sensitivity in combination. Its AI-related data governance features are relevant for organizations deploying Azure OpenAI Service and building AI pipelines on Azure that interact with sensitive data.
Teams evaluating data security tooling for Azure AI workloads should read our analysis of DSPM platforms compared alongside the AI-specific coverage here, because Securiti’s AI data governance capabilities address a gap that standard DSPM tools, including Purview DSPM for AI, do not cover for non-Microsoft AI infrastructure. Securiti does not publish list pricing.
Best for: Organizations running AI workloads on Azure that need data governance across both the training and inference surface, or organizations with significant privacy compliance requirements across multiple jurisdictions.
7. Sentra
Sentra is positioned as a cloud-native DSPM platform. For Azure, the vendor describes data discovery and classification across Azure Blob Storage, Azure Data Lake, Azure SQL, and Azure-managed databases. Its stated differentiation is data movement tracking: identifying when sensitive data has been copied, moved, or replicated to a location with a different security posture than the origin, which is a common source of unintended exposure in Azure environments where data pipelines move data between storage tiers or across subscriptions. SecurityOpsWire has not independently tested Sentra’s Azure coverage; the capability description is drawn from vendor positioning and should be validated in a proof of concept against your specific environment.
Data movement detection is operationally significant regardless of which tool provides it. A common Azure misconfiguration pattern involves sensitive data flowing through Azure Data Factory pipelines into development or analytics environments with weaker access controls. Purview does not track that movement continuously. Sentra does not publish list pricing and quotes per environment.
Best for: Azure environments with active data pipelines, ETL processes, or data replication across storage tiers where data drift into lower-security environments is a real operational risk.
8. Netskope DSPM
Netskope approaches data security posture from its background in cloud access security broker and security service edge technology. For Azure, it combines inline visibility into data flows with posture-based discovery of data at rest. This means Netskope can surface not just where sensitive data sits in Azure storage, but how that data moves between Azure and external destinations, including SaaS applications accessed from Azure-hosted workloads. SecurityOpsWire has not independently verified Netskope’s current DSPM feature set against official product documentation; capability claims here are based on vendor positioning and should be confirmed with the vendor directly.
For organizations that have already deployed Netskope for SSE or CASB coverage, adding DSPM capabilities within the same platform reduces integration overhead and provides a unified policy model across data in motion and data at rest. Netskope does not publish DSPM pricing separately from its broader platform; pricing is negotiated based on deployment scope.
Best for: Organizations that have invested in Netskope’s SSE platform and want data posture coverage that shares the same policy engine as their network security controls. Less compelling as a standalone DSPM purchase without the broader Netskope investment.
9. Zscaler DSPM
Zscaler integrates DSPM within its Zero Trust Exchange platform, combining inline visibility with posture-based discovery in a unified platform. For Azure, Zscaler discovers data in Azure-native services and correlates those findings with identity and access signals from its Zero Trust Exchange. Its strength is in organizations already running Zscaler for internet access and private access, where DSPM findings can directly inform Zero Trust policy decisions about which identities can reach which data. SecurityOpsWire has not independently verified Zscaler’s current DSPM feature set against official product documentation; capability claims here are based on vendor positioning and should be confirmed with the vendor directly.
The operational trade-off for Zscaler DSPM is similar to Netskope: the platform makes most sense as a capability inside an existing Zscaler deployment, not as a standalone data security posture tool. Zscaler does not publish DSPM pricing independently from its platform pricing.
Best for: Organizations deep in the Zscaler platform that want data risk signals to inform Zero Trust policy without adding a fourth or fifth vendor to their data security stack.
10. Orca Security DSPM
Orca Security is an agentless cloud security platform that includes DSPM capabilities. Its agentless architecture is a documented characteristic of the platform. For Azure, Orca is positioned to classify data inside Azure Blob Storage, managed databases, and data on Azure VMs without requiring per-resource instrumentation, and to correlate data findings with vulnerability, configuration, and identity risk in its unified risk model. SecurityOpsWire has not independently verified Orca’s current DSPM feature set against official product documentation; capability claims here are based on vendor positioning and should be confirmed with the vendor directly.
An agentless discovery approach carries a known architectural trade-off common to snapshot-based scanning: visibility reflects a point-in-time state rather than continuous real-time data movement. For environments where data changes rapidly or data pipeline movement is the primary risk, Sentra or Wiz may provide more current visibility. Orca does not publish pricing publicly.
Best for: Azure environments where the team wants combined vulnerability, configuration, and data posture risk without deploying agents, and where point-in-time classification accuracy is sufficient for the use case.
How Do the Licensing Models Compare at the Same Data Volume?
Direct price comparison across DSPM vendors is difficult because most do not publish list pricing. But the pricing models differ in ways that affect cost at scale, and understanding the model is more useful than a point-in-time number that will be out of date.
| Vendor | Pricing Model | Public Pricing? | Cost Driver at Scale |
|---|---|---|---|
| Microsoft Purview | Per-user licence (M365 E5 Compliance) or add-on | Partial (see Microsoft pricing pages) | User count, not data volume |
| Defender for Cloud CSPM | Per resource/hour for paid Defender CSPM tier | Yes, on Azure pricing page | Number of Azure resources |
| Wiz | Per workload/consumption, negotiated | No | Cloud workload count |
| Cyera | Consumption-based, negotiated | No | Data volume and source count |
| Varonis | Per TB managed data, per-user for SaaS | No | Data under management volume |
| BigID | Module-based, consumption-based | No | Data sources and module selection |
| Securiti | Platform-based, negotiated | No | User count and data source count |
| Sentra | Consumption-based, negotiated | No | Data assets under management |
| Netskope | Platform-based (SSE + DSPM bundled) | No | User count on SSE platform |
| Zscaler | Platform-based (Zero Trust Exchange) | No | User count on ZTE platform |
The practical implication: Purview’s cost scales with user count, not data volume. An organization with 10,000 users and 5 petabytes of Azure Blob data pays the same Purview licence cost as one with 10,000 users and 50 terabytes. Third-party tools that price on data volume or asset count behave differently at scale. An organization with a small user base but very large Azure storage footprint may find third-party volume-based pricing more expensive than it appears in early evaluations.
The SecurityOpsWire Azure DSPM Coverage Audit: Four Questions Before Any Purchase
This framework reduces the risk of buying coverage you already own or missing coverage you actually need.
Question 1: Which Purview tier does the organization currently hold? Pull the active Microsoft licence assignments. Confirm whether the tenancy includes M365 E5 Compliance or the Purview Information Protection add-on. If it does, the unified DSPM experience is available and likely underused. Configure it before buying anything else.
Question 2: Is Defender CSPM enabled at the paid tier? Defender for Cloud has a free foundational CSPM tier and a paid Defender CSPM tier. Data security posture management for Azure-native services requires the paid tier. Many organizations pay for Defender for Cloud products without enabling Defender CSPM specifically. Check the Defender for Cloud plans in the Azure portal.
Question 3: What percentage of sensitive data lives outside Microsoft-native services? Map data sources: Azure-native PaaS, Azure VMs running self-managed databases, connected non-Microsoft SaaS, on-premises systems, other clouds. If that percentage exceeds 30 percent, native Purview coverage will leave meaningful gaps regardless of licence tier.
Question 4: Is the primary risk identity access to data, data movement between environments, or compliance classification? The answer shapes which third-party tool fits. Identity-to-data risk points toward Wiz or Cyera. Data movement and pipeline risk points toward Sentra. Compliance classification depth points toward BigID or Securiti. Access governance and user behaviour points toward Varonis.
Which Tools Cover Azure Plus Other Clouds Consistently?
For organizations running Azure alongside AWS and GCP, consistency of data model and classification quality across clouds matters as much as Azure-specific depth. A tool that classifies PII with high accuracy in Azure Blob but misses the same data in S3 creates a false sense of coverage.
| Vendor | Azure Coverage | AWS Coverage | GCP Coverage | Non-Microsoft SaaS |
|---|---|---|---|---|
| Microsoft Purview | Deep (native) | Limited | Limited | Selective connectors |
| Defender for Cloud DSPM | Deep (native) | Available (multicloud) | Available (multicloud) | No |
| Wiz | Yes | Yes | Yes | Selective |
| Cyera | Yes | Yes | Yes | Yes |
| Varonis | Yes (Azure + M365) | Yes | Limited | Yes (M365-centric) |
| BigID | Yes | Yes | Yes | Yes (broad connector catalogue) |
| Securiti | Yes | Yes | Yes | Yes |
| Sentra | Yes | Yes | Yes | Selective |
| Netskope | Yes | Yes | Yes | Yes (CASB-rooted) |
| Zscaler | Yes | Yes | Yes | Yes (SSE-rooted) |
| Orca | Yes | Yes | Yes | No |
Wiz, Cyera, BigID, and Securiti offer the most consistent cross-cloud coverage with a unified data model. For organizations concerned about AI data risks across their cloud environment, this topic connects to broader questions about AI security posture management platforms that extend beyond data classification into model and agent security.
What Is the Operational Cost of Running a Third-Party DSPM in Azure?
Tool cost is the smaller part of the total investment. The operational costs that vendors do not advertise clearly include classification tuning, false positive management, and remediation workflow integration.
Classification tuning is real work. Out-of-the-box classifiers for standard sensitive data types, such as Social Security numbers, credit card numbers, and UK National Insurance numbers, tend to be accurate. Custom data types specific to an organization’s business, internal identifiers, proprietary formats, and domain-specific sensitive fields require custom classifier development. Every DSPM tool on this list requires some degree of custom classifier work for non-standard data types, and the time investment ranges from days to months depending on the organization’s data complexity.
False positive burden shapes analyst experience more than any other factor. A DSPM tool that flags 40 percent of findings as non-sensitive after human review generates classifier debt that compounds over time. Evaluate classification accuracy on a sample of actual production data before committing to any tool. Request a proof of concept that runs against a representative subset of Azure storage, including data types that are specific to your environment.
Remediation workflow integration is where most DSPM programs stall. Finding that a storage account is overshared is straightforward. Routing that finding to the right team, getting it remediated, and closing the loop in a ticket system requires integration with ServiceNow, Jira, or equivalent. Most tools on this list support webhook or API-based integration, but the integration work is owned by the buyer’s team, not the vendor. Budget the integration time as part of the deployment cost.
For security operations teams thinking about how DSPM findings fit into their broader detection and response workflow, the challenge of connecting data posture findings to active investigation is related to the wider question of how AI SOC platforms handle alert triage and investigation at scale.
Frequently Asked Questions
Is DSPM included in Microsoft 365 E3?
No. Microsoft 365 E3 includes basic Purview capabilities such as sensitivity labelling and basic data classification, but the DSPM experience, whether the unified DSPM surface or DSPM classic, requires Microsoft 365 E5 Compliance or the Microsoft Purview add-on for E3 customers. DSPM for AI has its own licensing requirements tied to M365 E5 Compliance or specific Copilot licences. Run a licence inventory in the Microsoft 365 admin centre before assuming DSPM is available to your tenant.
What is the difference between Purview DSPM and Defender for Cloud DSPM?
Purview DSPM focuses on data classification, sensitivity labelling, and risk signals across Microsoft 365 and connected services. It understands the content of the data. Defender for Cloud DSPM focuses on the security posture of Azure data resources, including exposure paths, misconfigurations, and attack paths to data stores. It understands the infrastructure around the data. The two generate overlapping but distinct findings. Organizations with both products benefit from using them together rather than treating them as substitutes.
Does DSPM replace DLP in Azure?
No. DSPM and data loss prevention address different problems. DSPM discovers where sensitive data exists and assesses its exposure posture. DLP enforces policies that prevent sensitive data from leaving controlled environments. DSPM informs DLP policy design by identifying which data types and locations carry the most risk, but it does not enforce data movement controls. Purview includes both capabilities. For organizations evaluating the relationship between the two, the correct framing is that DSPM feeds DLP policy, not that one replaces the other.
How does Purview DSPM handle data in Azure SQL versus Azure Blob Storage?
Purview’s data discovery and classification handles Azure SQL through its data map, which scans SQL databases and applies sensitive information types and custom classifiers. Azure Blob Storage is also supported through the data map, but the classification quality for unstructured data in blob containers varies depending on file type and classifier configuration. Defender for Cloud DSPM handles both Azure SQL and Azure Blob Storage with direct integration into Azure resource management, making it more comprehensive for Azure-native data resources at the infrastructure level.
Which DSPM tools work with Azure Government Cloud?
Microsoft Purview and Defender for Cloud operate in Azure Government regions. For third-party tools, sovereign cloud support varies by vendor and is not uniformly documented on public product pages. Organizations in regulated US government environments should ask vendors specifically about Azure Government Cloud support, FedRAMP authorization status, and data residency commitments before proceeding to evaluation. None of the third-party vendors in this article have been independently verified here as FedRAMP authorized; this note reflects the limits of what SecurityOpsWire can confirm independently, not a ranking or comparison of vendors on this dimension. Confirm authorization status directly with each vendor and through the FedRAMP Marketplace.
What is DSPM for AI and does it cover Azure OpenAI?
According to Microsoft’s documentation, DSPM for AI is purpose-built for Microsoft Copilot and other AI applications running on Microsoft infrastructure. It provides visibility into AI interactions and the data those interactions touch. Microsoft’s documentation does not specifically address coverage of custom Azure OpenAI deployments that organizations build and manage themselves; the documented scope focuses on Microsoft’s own AI applications. Organizations deploying custom AI applications on Azure OpenAI should evaluate DSPM for AI against their specific AI architecture and confirm coverage with Microsoft before assuming it applies. The broader topic of securing AI data pipelines connects to questions that AI agent discovery and monitoring tools address from a different angle.
The Audit Before the Buy
The most common mistake in Azure DSPM purchasing is treating it as a net-new spend without first mapping what Microsoft licensing already covers. An organization with M365 E5 Compliance and Defender CSPM has a material data security posture capability available today that most security teams have not fully configured. The correct sequence is: audit current Purview and Defender configuration, identify what is genuinely uncovered, then evaluate third-party tools against that specific gap.
For multi-cloud organizations or those with significant data outside Microsoft-native services, Wiz and Cyera represent the strongest general-purpose additions because they extend the same risk model across clouds without requiring a separate tool per environment. For compliance-heavy programs with broad source requirements, BigID’s connector depth and module-based licensing make it worth evaluating. For organizations already running Varonis for M365 access governance, extending that investment to Azure IaaS data is more efficient than adding a second platform.
The decision that tends to get skipped is the identity-to-data risk correlation question. Most Azure data exposures that surface in incident investigations trace back not to public storage misconfigurations, which are caught early, but to service principals, managed identities, and non-human accounts that accumulated access over time. A DSPM tool that surfaces sensitive data without correlating it to the identity graph is answering half the question. That correlation is where the operational value actually lives, and it is the capability worth weighting most heavily in any Azure DSPM evaluation. Teams thinking carefully about non-human identity risk in their environment should also review how non-human identity security platforms address the access side of the same problem.
