Zero trust data security extends the zero trust model to an organization’s most valuable asset—its data. First introduced by Forrester in 2010, the zero trust model shifts security from traditional perimeter defenses to a principle of continuous verification.
While the original model centered on network security, modern environments—driven by cloud adoption, remote work, and digital transformation—require companies to apply zero trust directly to data protection. This means treating data itself as the security perimeter, enforcing continuous verification and policy-based controls that follow the data across clouds, applications, and endpoints throughout its lifecycle.
Guide to Data Governance and Compliance in the Cloud
Our Guide to Data Governance and Compliance in the Cloud provides a straightforward, 7-step framework to help you strengthen your cloud governance approach with confidence.
Core principles of zero trust data security
1. Least-privilege access
Least privilege limits the access of users, applications, and systems to specific data sets based on job responsibilities and time-bound requirements. By removing unnecessary privileges, you reduce your attack surface and the blast radius if there’s a breach.
2. Continuous verification
Zero trust replaces static authentication with dynamic, continuous verification. Every data access request is treated as potentially risky and requires validation of user identity, device health, network conditions, access timing, and behavior patterns to determine if access should be granted.
3. Multi-layered microsegmentation
Microsegmentation spans multiple planes—not just the network. It establishes isolated security zones across complementary layers to enforce least privilege and contain threats:
Network layer: VPCs, subnets, and network security groups
Identity-permission layer: Scoped entitlements and role-based controls
Data layer: Data zones, classification tags, and policy-based access
Unlike traditional network segmentation, this is a multi-plane, fine-grained approach. Zero trust network segmentation limits lateral movement across users, systems, and data—embodying the defense-in-depth principle through coordinated controls.
4. Comprehensive visibility
As we’ve seen, zero trust requires visibility into data classification, location, movement, and access patterns. Continuous monitoring across on-premises systems, cloud platforms, endpoints, and third-party services gives you a unified view of your data landscape so you can detect anomalies and policy violations in real time.
5. Data-centric protection
Through encryption, tokenization, digital rights management, and persistent classification tags, security controls travel with your data from creation to deletion. These controls protect data at rest and in transit.
Protection for data in use requires specialized approaches like confidential computing with Trusted Execution Environments (TEEs), available in specific cloud services such as AWS Nitro Enclaves, Azure Confidential Computing, and Google Cloud Confidential VMs.
(For more information about how Wiz can help you implement these protections, check out our classification guide.)
Zero trust data security in cloud environments
| Cloud consideration | Implications | How to address it |
|---|---|---|
| Distributed cloud environments | Distributed cloud environments spread data and identities across services, zones, and providers, reducing visibility and consistency. | Zero trust in distributed environments demands adaptive, identity-centric enforcement using modern controls like Attribute-based authorization (ABAC), Just-in-time (JIT) elevation, Short-lived credentials, Conditional access (device posture, network context), Key-based or IAM-integrated datastore authentication |
| The shared responsibility model | Cloud security follows a shared responsibility model: Cloud service providers (CSPs) secure the physical and virtual infrastructure, while you safeguard data, identities, configurations, and workload integrity. | Implementing zero trust means understanding and continuously validating the boundaries between your responsibilities and your CSP’s in order to prevent protection gaps, misconfigurations, and implicit trust assumptions. |
| Multi-cloud environments | In multi-cloud environments, consistent security requires standardized data classification, unified policy frameworks, and centralized visibility across platforms. | Effective identity federation and central entitlement management enable coherent, least-privilege access controls across heterogeneous cloud providers and SaaS ecosystems. |
| Identities | With network boundaries dissolving, identity becomes the primary enforcement point for data access. | Enforce phishing-resistant MFA, ABAC/RBAC, and JIT elevation to reduce standing privileges and enforce dynamic least privilege. |
| Authorization and authentication | Cloud-native architectures enable continuous, context-aware access evaluation rather than static permissions. | Use real-time policy engines to assess behavior, device posture, network context, and session risk, then dynamically grant, limit, or revoke access. |
| Data flows | Modern applications create interconnected data flows across APIs, services, and storage layers. | A zero trust architecture enforces service-to-service validation, mutual TLS, and policy-based controls at every transition point, maintaining data classification and lineage metadata to preserve trust and compliance across distributed environments. |
| Differences in cloud service models | IaaS offers the most control—and responsibility—requiring strong cloud infrastructure security across compute, storage, and network layers. PaaS and SaaS reduce that control but add reliance on provider-managed safeguards. In SaaS, data security often lacks VPC-level controls and depends on tenant configuration, provider audit logs, and API-level policies. | Maintaining consistent policies across service models often requires multiple tools. CNAPPs reduce tool sprawl by unifying security across IaaS, PaaS, and SaaS, providing cross-layer context for consistent policy enforcement and faster threat response. |
Key components of zero trust data protection
1. Data discovery and classification
Automated tools scan environments to identify and classify sensitive data using content, context, and metadata, while machine learning applies tags to unstructured data to align with regulatory and business requirements. Agentless discovery: Modern CNAPPs can discover and classify sensitive data across clouds without deploying agents, eliminating blind spots and operational drag while maintaining complete visibility.
2. Identity and access management integration
Zero trust access control requires tight IAM integration so user, service, and application identities are evaluated with device posture, behavior, and authentication strength before access is granted."
3. Encryption and tokenization
Encryption and tokenization protect data end-to-end in a zero trust model, with strong key management securing data at rest, in transit, and in use. Advanced approaches include format-preserving encryption (FPE) and attribute-based encryption (ABE), which build access policies into the encryption itself.
4. Data loss prevention
DLP technologies track and manage data flow across all channels. In zero trust frameworks, DLP provides continuous content inspection and enforces consistent policies across channels—spanning SaaS applications, cloud storage, and collaboration platforms.
5. Behavioral analytics and anomaly detection
Analytics establish baseline patterns of normal data access and then identify deviations that may be threats. These systems analyze access timing, volume, operations, and patterns, using machine learning to reduce false positives and detect sophisticated attacks.
6. Microsegmentation for data environments
Microsegmentation requires technologies that create fine-grained policies, including software-defined perimeters, cloud security groups, and identity-aware proxies. Modern approaches use metadata to dynamically adjust boundaries based on sensitivity and risk.
7. Policy framework architecture
Zero trust separates policy decision points (PDPs)—where access decisions are made—from policy enforcement points (PEPs)—where decisions are implemented. This enables API-centric policy engines (e.g., Open Policy Agent, AWS Cedar, HashiCorp Sentinel) to centralize policy definitions with distributed enforcement across multi-cloud environments.
8. Continuous logging and monitoring
Logging and monitoring is a two-step process. First, zero trust requires all data access to be logged with detailed context about each access event—who accessed what data, when, from where, and how. The next step is correlating events to find patterns invisible in individual logs.
9. Automation and orchestration
Guardrails over gates: Use policy-as-code to enforce preventative guardrails in pipelines (e.g., IaC checks) and route fixes to owners via integrations, enabling security without blocking developer velocity.
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Best practices for overcoming implementation challenges
Integrating with legacy systems
Instead of aiming for the full zero trust treatment for your legacy data environments, use gateways, data access proxies, and tokenization layers to apply a select few zero trust policies. Start with the stuff that's really valuable or regulated and work your way down as you upgrade your systems.
Balancing security with usability
Control policies that are too strict can cause friction if they affect employees' output. Take a risk-based approach with your data access controls—tighten them up more for sensitive data and ease off a bit in less sensitive situations. Provide clear guidance on encryption, and let users know when they've got permission to access something. Other smart moves? Incorporating user feedback and automating access request paths.
Creating an implementation roadmap
Break your implementation journey down into stages aligned with NIST SP 800-207 Zero Trust Architecture principles:
Data visibility: Map and classify sensitive data.
Access control: Implement context-aware and least-privilege data access.
Protection: Encrypt data at rest, in transit, and in use.
Automation: Continuously monitor and remediate policy violations.
Prioritizing critical assets
Prioritize which assets to address first based on data sensitivity, regulatory requirements, and business impact. Build a strong zero trust foundation first, then implement automated controls, track risk reduction, and update policies regularly to maintain long-term security.
Measuring success
Keep track of key numbers like…
Percentage of sensitive data with defined owners and classification tags
Percentage of data stores meeting encryption standards and key rotation SLAs
Mean time to revoke or adjust excessive data entitlements
Coverage and completeness of data access audit logging
Reduction in exposed or unmonitored data paths
Time to detect and remediate data-related anomalies
How Wiz enables comprehensive zero trust data security
The major takeaway from this article? Implementing zero trust principles means having complete visibility and control over where sensitive data lives and how it’s accessed.
Enter Wiz.
Wiz combines agentless data discovery, graph-based contextual risk analysis, and automated policy enforcement across multi-cloud environments to accelerate zero trust outcomes. The agentless architecture provides complete visibility without performance impact, while the Wiz Security Graph correlates data exposure with identity permissions, network paths, and vulnerabilities to prioritize truly exploitable risks.
Here’s a closer look at some of Wiz’s offerings:
Automated sensitive data discovery: Wiz’s agentless scanning automatically discovers and classifies sensitive data across multi-cloud environments without disrupting operations. As part of a DSPM approach, Wiz maps exposures and complements inline DLP, providing the visibility foundation for zero trust data security.
Real-time access monitoring: To identify over-privileged accounts and risky permission combinations that violate least-privilege principles, Wiz's CIEM correlates cloud identities, effective permissions, and access activity to highlight excess and toxic entitlements.
Policy-driven remediation: With Wiz, you can leverage automation rules, integrations, and guardrails (e.g., IaC checks, ticketing workflows, and targeted cloud-native changes) to accelerate remediation, reduce manual effort, and maintain compliance.
Unified platform approach: Say goodbye to tool sprawl: Wiz’s CNAPP architecture unifies data security posture management, identity management, and vulnerability assessment, giving you consistent visibility and control.
Code-to-cloud traceability: Trace cloud exposures back to code and pipelines to fix root causes and prevent drift, maintaining zero trust controls from development through production.
See how Wiz discovers sensitive data across multi-cloud, correlates identity, network, and workload context to prioritize real exposure, and automates policy enforcement—without agents: Schedule a demo today.
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