Unified data security across AWS, Azure, GCP, and SaaS environments with cloud-native classification, access governance, and threat detection.
Only three platforms are featured. Each is independently assessed across encryption, access architecture, threat detection, and compliance depth.
Varonis provides unified data security across cloud and hybrid environments, automatically discovering and classifying sensitive data in AWS S3, Azure Blob Storage, Google Cloud Storage, Microsoft 365, Google Workspace, Salesforce, Box, and more. For organisations with sensitive data spread across multiple cloud services, Varonis provides the single pane of visibility that cloud-native tools from individual providers cannot deliver. Its access analytics map complex cloud permission models into understandable views, identifying overshared files, excessive permissions, and public exposure that create data breach risk.
Securiti AI provides a unified data intelligence platform that combines data security, privacy management, and governance for cloud environments. Its AI-powered engine automatically discovers sensitive data across 200+ cloud data systems, classifies it by regulation and sensitivity, maps data flows between systems, and enforces security policies. Securiti's unique advantage is combining data security with privacy operations — DSARs, consent management, and data mapping — in a single platform, addressing both security and privacy requirements for organisations subject to GDPR, CCPA, and other privacy regulations.
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| Capability | Varonis Data Security Platform | Securiti AI | Your Solution? |
|---|---|---|---|
| Multi-Cloud Data Discovery | ✅ Major Clouds + SaaS | ✅ 200+ Data Systems | — |
| Data Classification | ✅ ML-Powered | ✅ AI-Powered | — |
| Access Governance | ✅ Deep Permission Analytics | ✅ Entitlement Management | — |
| Threat Detection | ✅ UEBA Behavioural | 🔶 Policy-Based | — |
| Privacy Management (DSAR) | 🔶 Limited | ✅ Full Privacy Suite | — |
| Data Flow Mapping | ✅ Access Path Analysis | ✅ Cross-System Lineage | — |
| SaaS Coverage Depth | ✅ M365, Google, Box, SF | ✅ Broad SaaS Coverage | — |
| On-Premises Support | ✅ Full Hybrid | 🔶 Cloud-Primary | — |
| Remediation Automation | ✅ Permission Reduction | ✅ Policy Enforcement | — |
The majority of data breaches now involve data stored in cloud environments. Cloud data sprawl across multiple providers and SaaS applications creates visibility gaps that traditional on-premises security cannot address.
Nearly half of sensitive data in cloud environments lacks appropriate security controls. Organisations moving data to cloud often outpace their ability to extend data security policies to new environments.
89% of enterprises operate across multiple cloud providers. Each provider uses different permission models, security configurations, and monitoring capabilities — creating inconsistencies that attackers exploit.
Employees adopt SaaS applications without IT approval, creating shadow data repositories containing sensitive information outside security team visibility. Cloud data security platforms discover these exposures automatically.
Cloud adoption has fundamentally changed the data security problem. In on-premises environments, data resided in known locations — file servers, databases, email servers — that security teams controlled and monitored. In cloud environments, data is distributed across dozens of services, replicated across regions, shared through collaboration links, and accessed from any device anywhere. The attack surface has expanded exponentially while visibility has fragmented across provider-specific tools.
Cloud data security platforms address this by providing unified discovery and classification across all cloud environments from a single platform. Rather than managing separate visibility tools for AWS, Azure, Google Cloud, and each SaaS application, organisations gain a comprehensive view of where sensitive data exists, who can access it, and whether access patterns indicate risk — regardless of which cloud service hosts the data.
Cloud environments use complex permission models that create unintended data exposure. AWS IAM policies, Azure RBAC roles, Google Cloud IAM bindings, and SaaS application-specific permissions each use different structures, inheritance patterns, and evaluation logic. A user's effective permissions result from the combination of identity policies, resource policies, organisation-level boundaries, and service-level configurations — creating access paths that are nearly impossible to audit manually.
Cloud data security platforms analyse these permission models to calculate effective access — the actual data each user can reach when all policies are evaluated together. This analysis frequently reveals surprising results: users with access to sensitive data they should not reach, public sharing links exposing confidential documents, service accounts with excessive permissions, and cross-account trust relationships that create lateral access paths. Identifying and remediating these exposures is the highest-impact data security activity for cloud-first organisations.
When evaluating platforms for your environment, request a proof-of-concept deployment against your actual data estate. Vendor demonstrations using sanitised demo data do not reveal how the platform performs with your specific data volumes, access complexity, and compliance requirements.
Discovering sensitive data across cloud environments requires automated classification at scale. Manual classification — relying on users to label their own data — consistently fails because users prioritise productivity over classification accuracy. Automated classification uses machine learning to scan data content, identify sensitive information (PII, financial data, health records, intellectual property), and apply sensitivity labels without user intervention.
Cloud data security platforms scan cloud storage, databases, and SaaS applications continuously, classifying data as it is created and modified. This continuous classification ensures that newly created sensitive data is identified immediately rather than discovered during periodic scans. For organisations subject to GDPR, the automated discovery of personal data across all cloud environments satisfies the regulation's requirement to maintain records of processing activities and know where personal data resides.
SaaS applications — Microsoft 365, Google Workspace, Salesforce, Slack, Box — contain vast quantities of sensitive data that organisations often overlook in their security programmes. Users share sensitive documents through collaboration links, store confidential information in cloud drives, and communicate sensitive details through messaging platforms. Each SaaS application has its own sharing model, permission structure, and security capabilities.
Cloud data security platforms extend data protection to SaaS environments by monitoring sharing settings (identifying files shared publicly or with external parties), classifying content within SaaS applications (finding PII in Google Sheets or financial data in SharePoint), and detecting anomalous user behaviour (bulk downloads from departing employees, unusual sharing patterns, access from suspicious locations). For organisations where employees are the primary data creators and sharers, SaaS data security is not optional — it is where the majority of sensitive data activity occurs.
Generative AI adoption is creating new data security requirements. Ensure your platform can discover and classify sensitive data within AI training datasets, monitor data flows to AI services, and enforce policies that prevent confidential data from entering AI prompts and pipelines.
Cloud data security and data privacy are converging into a single discipline. GDPR, CCPA, and other privacy regulations require organisations to know where personal data resides (data discovery), control who can access it (access governance), protect it from unauthorised disclosure (threat detection), and respond to individual rights requests (DSARs) — all capabilities that data security platforms provide. Privacy-specific requirements like consent management, data subject access requests, and cross-border transfer controls increasingly integrate with security platforms.
Securiti AI exemplifies this convergence, combining data security capabilities (discovery, classification, monitoring) with privacy operations (DSAR automation, consent management, data mapping for privacy impact assessments) in a single platform. For organisations managing both security and privacy compliance across cloud environments, a converged platform reduces operational complexity and ensures that security controls and privacy policies are applied consistently to the same data assets.
Effective cloud data security architecture follows four design principles. Data-centric: protect the data itself rather than the perimeter, because cloud data moves between services, regions, and devices continuously. Policy-driven: define security policies centrally and enforce them across all cloud environments automatically, rather than configuring each service individually. Continuous: monitor data security posture continuously rather than relying on periodic assessments that miss interim exposures.
API-first: integrate with cloud services through APIs rather than deploying network-level interception that cloud architectures render ineffective. These principles guide platform selection: evaluate whether each platform provides data-level protection (not just infrastructure security), centralised policy management across clouds, continuous monitoring and detection, and native API integration with your cloud services. Platforms that align with these principles provide durable security as cloud architectures evolve.
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