Federal agencies should start by treating data as the control point for Zero Trust. That means finding sensitive data across all environments, classifying it, applying consistent access controls, encrypting it in transit and at rest, and continuously monitoring activity. DSPM helps provide the data intelligence needed to enforce policy across identity, devices, networks, applications, and analytics.
How data-centric Zero Trust changes the implementation sequence
For M-22-09, the practical shift is to treat data as the protected object and the policy anchor, not as a byproduct of network design. Agencies need to discover where sensitive information lives, determine who and what touches it, and then express policy in terms of classification, access conditions, encryption, and telemetry. That approach makes zero trust portable across cloud, on-premises, SaaS, and analytical environments.
Federal teams usually get the most leverage by starting with high-value data sets and the paths that routinely copy, transform, or expose them. The most important question is not just whether a user can reach a system, but whether the right data is visible, usable, exportable, and auditable under the current context. That is why data discovery and policy enforcement have to move together.
Because Zero Trust for data depends on evidence, not assumption, agencies should prefer controls that continuously reassess trust conditions rather than one-time approvals. A useful way to think about this is that identity, device posture, network location, application context, and analytics activity all become signals that influence data access decisions.
Where agencies struggle is the handoff between policy intent and actual data movement. Controls that work in one repository often fail when data is replicated into reports, exports, caches, or downstream analytics. The implementation sequence should therefore include inventory, classification, policy definition, enforcement, monitoring, and exception handling as a single chain, not as separate workstreams.
For practical grounding, NHI Mgmt Group’s Ultimate Guide to NHIs is useful because data-centric Zero Trust often fails when service accounts, API keys, and other non-human access paths are left outside normal governance.
Controls that make data-centric Zero Trust work across federal environments
Data-centric Zero Trust is strongest when it combines coarse-grained access policy with data-specific protections. Agencies should align classification to enforcement so that sensitive data is encrypted in transit and at rest, access is limited by need and context, and activity on the data is logged in a way that supports investigation and policy tuning. Without that alignment, agencies get visibility without control or control without enforcement.
DSPM is valuable because it helps close the gap between where data is stored and where policy is actually applied. In federal environments, that means finding sensitive records in repositories, collaboration tools, analytics platforms, and cloud services, then mapping those findings to protection requirements that can travel with the data itself. The point is not to create a one-off inventory, but to sustain a living control plane for data.
Encryption alone is not sufficient. If agencies cannot answer which data is sensitive, who accessed it, whether the access was expected, and whether the data was copied into less-controlled environments, then the Zero Trust posture remains incomplete. The control set has to cover discovery, classification, access governance, strong authentication, encryption, monitoring, and retention of audit evidence.
For agencies building workload and service-to-service enforcement, the Guide to SPIFFE and SPIRE is a useful companion because federated workload identity helps keep machine access bounded while data flows between systems.
FedRAMP, cloud service boundaries, and internal platform boundaries do not remove the need for data-centric controls. They make it more important to understand where policy decision points live and whether data protections survive replication and transformation. In practice, agencies should verify that their chosen controls still function when data is exported, queried through APIs, or used by analytics tooling.
For agencies wanting a standards-oriented view of the control set, the Ultimate Guide to NHIs, Standards section maps the Zero Trust discussion to relevant identity and security control families.
Risk and Threat Considerations
Data-centric Zero Trust fails when agencies assume a perimeter or endpoint boundary is enough to protect information after it has been copied, cached, shared, or analysed elsewhere. The main risks are overexposure, uncontrolled replication, and blind spots around non-human access paths that can move data faster than policy teams can review it.
Failure mechanism: Sensitive data is discovered too late, classified inconsistently, or left governed only by the source system, so downstream copies inherit weaker protections than the original.
Impact: Agencies can end up with unauthorized disclosure, excessive internal access, and weak auditability even when the originating system appears compliant.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, NIST SP 800-63, NIST Zero Trust (SP 800-207) and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OC — Organizational Context | M-22-09 implementation depends on knowing data criticality and business context. |
| ID.AM — Asset Management | Data-centric Zero Trust starts with discovering where sensitive data resides. | |
| PR.AA — Identity Management, Authentication and Access Control | Access to sensitive data must be consistently enforced across users and services. | |
| Recommendation — Map your highest-value data flows and ownership so Zero Trust policy reflects mission and data criticality. Inventory sensitive data assets and their flows before enforcing policy. Apply least-privilege access and strong authentication to data access paths. | ||
| NIST SP 800-63 | Digital Identity Guidelines | Strong authentication and federation shape who can access protected data under Zero Trust. |
| Recommendation — Use identity assurance appropriate to the sensitivity of the data access decision. | ||
| NIST Zero Trust (SP 800-207) | AC-4 — Information Flow Enforcement | Zero Trust for data requires policy enforcement over how information moves across boundaries. |
| Recommendation — Enforce data-flow restrictions so sensitive information is only released under approved conditions. | ||
| CIS Controls v8 | 3 — Data Protection | The question centers on protecting sensitive data with encryption and handling controls. |
| 6 — Access Control Management | Data-centric Zero Trust requires tight access control across identities and applications. | |
| 8 — Audit Log Management | Monitoring data activity is necessary to verify policy enforcement and investigate misuse. | |
| Recommendation — Classify, protect, and track sensitive data wherever it is stored or processed. Remove unnecessary access and review data permissions on a continuing basis. Log and review sensitive data access events to detect unauthorized movement or use. | ||
Practitioner Guidance
What to prioritise: Start with the datasets that are most likely to create mission, privacy, or regulatory impact if mishandled, then extend outward to the systems that replicate them. If you cannot trace the data path, you do not yet have data-centric Zero Trust.
What to verify: Confirm that classification, access policy, and monitoring are attached to the data flow, not only to the source application. A strong implementation can explain who accessed the data, under what conditions, and what happened next, including exports and downstream usage.
Common mistake: Treating DSPM as a discovery exercise instead of a control enabler. The useful outcome is not the inventory itself, but the ability to enforce consistent policy across identity, devices, networks, applications, and analytics without losing visibility.
Practitioner takeaway: Data-centric Zero Trust succeeds when agencies can prove that sensitive data remains governed after it leaves the system of record, because that is where most real exposure begins.
Related resources from NHI Mgmt Group
- How should federal agencies build a digital identity program that supports zero trust requirements?
- What are the signs that a federal SecOps team is not ready to meet Zero Trust requirements?
- What happens when federal agencies try to meet Zero Trust deadlines without security automation?
- How should federal agencies implement Zero Trust when budgets, staff, and skills are limited?
Deepen Your Knowledge
Free weekly newsletter
Subscribe to the NHI & AI Identity Journal
The latest on NHI and Agentic AI security – articles, research, breaches, news and events every week.
Bonus 33% off our NHI Course when you subscribe.
Reviewed and updated by the NHIMG editorial team on September 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org