Start with API-based discovery across the platforms that hold regulated or business-critical data, then layer classification, access context, and monitoring on top. The key is consistency: the same policy logic should follow the data across cloud services, SaaS applications, and hybrid stores. Without that, visibility remains fragmented and exposure reports are incomplete.
Why This Matters for Security Teams
DSPM is no longer just a cloud storage inventory exercise. In multi-cloud and SaaS environments, the real challenge is proving where regulated data lives, who can reach it, and whether access patterns change as services, tenants, and integrations shift. That is why current guidance increasingly pairs data discovery with identity-aware controls, rather than treating classification as a one-time scan. The NIST Cybersecurity Framework 2.0 is useful here because it ties visibility to ongoing governance, not just asset lists.
NHIMG research shows the gap is operational, not theoretical: 35.6% of organisations cite consistent access across hybrid and multi-cloud environments as their top non-human identity challenge, which maps directly to data exposure in DSPM programs. Breaches such as the Snowflake breach and the Salesloft OAuth token breach show how token sprawl and weak access context can turn otherwise visible data stores into exposed ones. In practice, many security teams discover the data risk only after an integration has already widened access across multiple platforms.
How It Works in Practice
Effective DSPM across multi-cloud and SaaS starts with broad, API-based discovery across the systems that actually store or move sensitive data. That includes object stores, databases, collaboration platforms, CRM data, analytics tools, and backup or replication layers. The goal is not just to find files, but to connect data location with identity, entitlement, and activity context.
Practitioners usually build the program in four steps:
- Discover data sources through native APIs and approved connectors, then normalize metadata across providers.
- Classify data using policy-driven labels that can survive movement between cloud services and SaaS tenants.
- Map access to both human and non-human identities, including service accounts, OAuth apps, API keys, and workload identities.
- Continuously monitor access, sharing, and drift so policy violations are detected when they happen, not in quarterly reviews.
This is where identity governance matters. A data object may be correctly classified, but if an integration token is over-privileged or a SaaS admin role can silently expand access, the DSPM result is incomplete. The 2024 Non-Human Identity Security Report highlights why: 88.5% of organisations say their non-human IAM practices lag human IAM, and 59.8% see value in dynamic ephemeral credentials, which supports a DSPM model that is continuous rather than snapshot-based. For identity context, teams also use NIST CSF 2.0 as a governance anchor while mapping control ownership across cloud and SaaS.
The practical test is whether one policy logic can follow the same dataset from an S3 bucket to a SaaS export, then into a downstream analytics workspace. These controls tend to break down when SaaS vendors expose limited telemetry or when cross-tenant data sharing is driven by user-managed integrations rather than centrally governed identities.
Common Variations and Edge Cases
Tighter DSPM coverage often increases connector maintenance, metadata normalization work, and false-positive tuning, so organisations must balance broader visibility against operational overhead. That tradeoff becomes sharper in SaaS-heavy estates where every platform has different API limits, event formats, and permission models.
Best practice is evolving, and there is no universal standard for every SaaS application yet. Some platforms support rich classification and entitlement telemetry, while others only expose partial audit trails. In those cases, security teams should prioritize the systems that hold regulated, customer, or production data first, then extend coverage to lower-risk repositories.
Edge cases also appear when data is copied into shadow SaaS tools, external collaboration spaces, or managed services controlled by vendors. The Azure Key Vault privilege escalation exposure and Codefinger AWS S3 ransomware attack illustrate how access paths and storage controls can fail together when credentials or permissions are not scoped tightly. In those environments, DSPM should be paired with entitlement review, secret hygiene, and alerting for anomalous sharing rather than relying on classification alone.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OV-01 | DSPM needs ongoing oversight across clouds and SaaS. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Cross-platform access often depends on weakly managed machine credentials. |
| CSA MAESTRO | D3 | MAESTRO addresses data protection across distributed cloud and SaaS environments. |
Apply data-centric controls that follow sensitive data across services, tenants, and integrations.
Related resources from NHI Mgmt Group
- How should security teams implement DLP monitoring across cloud and SaaS environments?
- How should security teams implement cloud user access reviews across SaaS and multi-cloud environments?
- How should security teams classify data in cloud and SaaS environments?
- How should security teams prioritise NHI remediation in cloud environments?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
