Unifying Cloud & Data Risk with Wiz + Sentra: How a Digital Bank Detects Exposure and Prioritizes Real Risk
The Challenge
Cloud-Scale Growth Exposed a Critical Data Blind Spot
As a cloud-native financial services leader, the digital bank leverages cloud infrastructure to support lending, investing, and wealth management services. Their security team selected Wiz as its Cloud Security Posture Management (CSPM) platform to identify misconfigurations, exposed resources, and potential attack paths across its expanding cloud footprint.
While Wiz delivered strong visibility into cloud configuration risk, the team quickly encountered a familiar challenge: configuration risk alone does not provide a comprehensive view of data risk. Wiz could effectively identify exposed or misconfigured resources, but it lacked deep, accurate insight into what data actually lived inside those assets, especially unstructured data. This made it difficult to distinguish between theoretical risk and true exposure involving sensitive customer information.
In one investigation, the security team discovered files containing sensitive customer data that Wiz had flagged as misconfigured but could not contextualize based on data sensitivity. Without reliable, context-rich classification, the security team lacked confidence in prioritization and response.
The result: uncertainty, noise, and delayed escalation when real data exposure was at stake.
“Integrating Sentra with Wiz fundamentally changed how we evaluate cloud risk. For the first time, we can see not just where a misconfiguration exists, but what sensitive data is actually at stake. That context lets us prioritize real exposures, reduce noise, and respond with far greater confidence.”
— Director of Application Security
Why Wiz + Sentra
CSPM Without Data Intelligence is Incomplete
The user’s experience reflects a broader reality across cloud-first enterprises: CSPM tools - even those that list DSPM capabilities - lack the depth, accuracy, and scale needed to truly understand sensitive data risk. Configuration context without data context leaves security teams guessing.
To close this gap, the security team paired Wiz with Sentra’s Data Security Posture Management (DSPM) platform. Sentra was selected because it delivers deep, accurate, and scalable data intelligence that CSPM platforms alone cannot provide:
- AI-based data classification that accurately identifies PII, PCI, credentials, secrets, and regulated data
- High-speed, petabyte-scale scanning designed for efficiency at large data volumes
- Comprehensive coverage across cloud, on-prem, data lakes, and SaaS
- Context-rich unstructured data classification, addressing the ~80% of enterprise data other DSPMs struggle to analyze
- Agentless deployment that enables fast time-to-value without operational friction
By integrating Sentra with Wiz, they gained the missing layer: trusted data truth.
Turning Signals Into Real Risk
From Misconfigurations to Meaningful Exposure
With Sentra enriching Wiz findings, the security team now evaluates cloud risk based on actual data exposure, not assumptions.
Sentra continuously discovers and classifies sensitive data, feeding high-fidelity data context directly into Wiz. This enables “toxic combination” detection when sensitive data resides in exposed, misconfigured, or attack-path-accessible resources.
Instead of treating all misconfigurations as equal, the team can now answer the most important security question with certainty:
“Does this issue expose sensitive data and how severe is the impact?”
This clarity transforms Wiz alerts from broad signals into actionable, prioritized risks.
Business Impact
Precision, Prioritization, and Confidence at Scale
By combining Wiz CSPM with Sentra DSPM, digital bank established a unified view of cloud and data risk that materially improved security outcomes:
- Risk Prioritization
Clear differentiation between hypothetical risk and true data exposure based on accurate classification. - SOC Efficiency
High-risk findings are automatically escalated, reducing noise and alert fatigue. - Improved Compliance Readiness
Stronger evidence for audits and regulatory requirements across financial services environments. - Unified Risk Intelligence
A cohesive view across infrastructure, identity, and sensitive data that enables better decisions at speed.
Wiz + Sentra:
Setting a New Standard for Cloud and Data Security
In an industry where data exposure carries significant financial and reputational risk, this leading digital bank has adopted a comprehensive, intelligence-driven security model. Wiz provides critical visibility into cloud posture and attack paths and Sentra delivers the data context required to make those insights meaningful.
Together, Wiz and Sentra enable security teams to move beyond surface-level signals to true exposure awareness, helping organizations secure what matters most as cloud environments scale.
This partnership demonstrates a clear lesson for modern enterprises:CSPM is powerful—but only when paired with accurate, scalable, data-first intelligence.
More relevant Case Studies
How a Mortgage Lender Ensures Sensitive Data Gets Masked and Stays Masked
How a Mortgage Lender Ensures Sensitive Data Gets Masked and Stays Masked
One of the largest U.S. mortgage lenders manages over $350 billion in loans across a complex ecosystem of production and non-production cloud environments. They rely on data-intensive applications to support underwriting, processing, and customer management.
Given the nature of their business, mortgage lenders and financial institutions are subject to stringent and multi-layered data protection and privacy regulations, such as; FTC Safeguards Rule, Gramm-Leach-Bliley Act (GLBA), Consumer Financial Protection Bureau (CFPB), SOX, FFIEC guidelines, and increasingly state-level privacy laws like the California Consumer Privacy Act (CCPA). Compliance requires rigorous control over non-production data environments where customer data often gets replicated for development and testing. Most relevant regulations either require or recommend data masking for sensitive customer data.
The mortgage lender had a legacy DSPM solution that generated large volumes of false positives, and lacked the precision to support automated masking workflows needed to ensure compliance. This created significant manual overhead for the data security team.
The financial institution’s data security and compliance teams turned to Sentra and within weeks, they gained column-level visibility into regulated data, automated classification and masking of workflows, and uncovered hundreds of orphaned data stores that could be deleted to both significantly improve regulatory compliance, reduce storage costs and reduce manual workload for the security team.
The Challenge: Manual Masking and Limited Data Visibility
The mortgage lender uses a data masking tool to mask regulated data in non-production environments. Their previous DSPM solution lacked depth and breadth of classification and created too many false positives, leading to over-masking and a labor intensive manual verification process. This made it very difficult to spot what data needed to be masked. Like all financial institutions, the lender also has many sensitive data classifications unique to its business operations that had to be manually tagged. Together, all these classification limitations made it difficult to create data reports to feed to their data masking tool.
For known and correctly classified sensitive data, their data masking tool was able to transform it into realistic synthetic records. Once the original required data masking was performed, there was no reliable way to confirm whether data remained masked after refreshes, especially since the masked data resembled real data so closely. The mortgage lender needed visibility into where PII/PCI and toxic data combinations lived across non-production environments and accurately classified sensitive data before and after being masked.
“The challenge wasn't just masking data; it was the persistent uncertainty of whether that data stayed masked after system refreshes. We needed a reliable way to verify ongoing compliance at a granular level.”
— Chief Compliance Officer, Leading US Mortgage Lender
Why Sentra: Column-Level Precision, Workflow Automation, and Immediate ROI
After a thorough evaluation of leading DSPM vendors, the mortgage lender chose Sentra due to several key capabilities. Its flexible classifier system, which supports both regex and contextual logic using AI-powered classifiers, made it easier to identify masked and unmasked data accurately. The platform’s policy engine offered automated scanning for missing or reverted markers, helping teams detect issues early. Sentra also seamlessly integrated into existing workflows without requiring invasive changes to systems or processes.
Key Outcomes:
- Fast AI-Driven Column-Level Classification: Sentra’s precise tagging engine classified sensitive data across their entire environment in just six weeks, outperforming other vendor tools by automatically identifying PII/PCI, financial data, and compliance-relevant data types.
- Improved Accuracy: With Sentra the compliance and data security teams are able to create a clear view of all the data that needs to be masked and feed this information into their data masking tool for future masking. Sentra can detect whether a dataset contains markers like "@example.com" emails or specially formatted SSNs.
- Automated Data Masking via Jira: Sentra integrated with their existing data masking tool to mask data and pushed alerts to Jira, enabling end-to-end remediation workflows with executive visibility.
- Granular Visibility: By using data classifications and logical negation (e.g., “does not contain marker”), the compliance team can isolate and track both compliant and non-compliant datasets.
- Policy-based Automation: Sentra’s automatic policies engine is set to run on a regular schedule, identifying data assets without expected markers, allowing the compliance and data security teams to take action before audits or incidents occur.
- Compliance Confidence
Able to ensure compliance with multi-layered data protection and privacy regulations and internal security mandates for precise access and masking.
Implementation: From Manual Compliance Burden to Automated Remediation
The mortgage lender deployed Sentra in under six weeks, scanning thousands of data stores across AWS, Snowflake and other cloud and SaaS environments and applied accurate sensitivity labels. Sentra’s classification output determined user roles based on data sensitivity. The integration with Jira and their data masking tool enabled an automated masking workflow, flagging issues to executives and eliminating manual triage.
Following the initial deployment, the financial institution decided to build on this momentum and extend Sentra’s coverage to Google Workspace.
Real Business Impact: Data Visibility, Accurate Masking, and Compliance Confidence
With Sentra, the data security and compliance teams gained deep visibility into sensitive and regulated data across cloud environments and SaaS applications, transforming how they enforce compliance and scale a proactive, automated data protection strategy.
Mortgage Lender and Sentra: Turning Compliance into a Competitive Advantage
What started as a goal to streamline masking and compliance has become a long-term foundation for cloud data governance. The data security team replaced an underperforming legacy DSPM and gained deep visibility into sensitive and regulated data across cloud environments and SaaS applications, transforming how they enforce compliance and scale a proactive, automated data protection strategy. They also implemented a strategic, automated framework for protecting customer data across every environment and ensuring compliance.
Together, the mortgage lender and Sentra have transformed how the financial institution security team supports excellence in development speed, data protection, and regulatory compliance.
How a Consumer App Company Secured Over 130 Petabytes in Weeks
How a Consumer App Company Secured Over 130 Petabytes in Weeks
A global Consumer App company manages vast, complex cloud environments spanning multiple continents and hundreds of petabytes of sensitive customer and operational data. But their legacy data classification tools were not designed for the massive scale and speed of their cloud data, especially when it came to identifying sensitive information buried deep in complex file formats like JSON and Parquet.
Faced with multiple, complex compliance requirements and ballooning data security costs, the company turned to Sentra.
By adopting Sentra’s AI-powered Data Security Posture Management (DSPM) platform, they accelerated and scaled their data security strategy, achieving 98% classification accuracy and full visibility across cloud-scale infrastructure, and enabling faster compliance - all while reducing operational overhead and cutting cloud costs.
The Challenge: Massive Data, Complex Formats, and Untenable Costs
The data security team’s existing classification tools were never built for the scale and complexity of a data estate over 130 petabytes. As regulatory requirements increased, and data structures became more nested and dynamic, manual tagging and legacy solutions became expensive, inaccurate, and unsustainable.
The team also faced an immense data security challenge: how to accurately classify sensitive information across an enormous cloud environment, while keeping operational costs in check. Their existing legacy tools lacked the precision and scalability to handle complex, nested file formats like JSON and Parquet, which are common in modern data engineering pipelines. Manual tagging was not only time-consuming but also inaccurate, resulting in low coverage and high compliance risk. With regulatory deadlines rapidly approaching, the security team needed a way to gain complete visibility into sensitive data, improve classification accuracy, and implement a scalable architecture that wouldn’t break the budget.
"Our previous solutions simply couldn't keep pace with the sheer volume and complexity of our cloud data. We needed a robust, cloud-native approach that was both effective and economically sound across our entire digital footprint."
— Deputy CISO
After evaluating multiple vendors, the company selected Sentra for its unique combination of deep technical sophistication and practical efficiency.
What stood out:
AI-Driven Classification at Scale: Sentra’s multi-model architecture, including GLiNER for Named Entity Recognition and embedding-based contextual detection, enabled granular, column-level classification, even inside deeply nested Parquet structures.
Cost-Efficient Ephemeral Scanning: Unlike always-on tools, Sentra’s ephemeral EC2 architecture scales to zero when not scanning. Combined with S3 inventory-based change detection and AI- driven smart sampling, it enables fast classification across hundreds of petabytes, at a fraction of the time and cost, and without impacting performance.
Seamless Terraform Deployment: Rapid deployment via infrastructure-as-code made it easy to scale Sentra across multiple environments while enforcing least-privilege access through dual-role AWS authentication.
Why Sentra: Accuracy and Efficiency at Cloud-Native Scale
"Sentra accurately uncovered mislabeled sensitive customer data, enabling rapid validation and remediation. It is now an indispensable element of our data protection strategy allowing us to stay compliant and keep our data protection promise to millions of customers around the world."
— Deputy CISO
Sentra was deployed and delivering results in the customer’s environment in just 12 days. During the initial proof of concept, the data security team was able to select where they wanted scanning to begin and easily configure the platform, allowing the solution to scan 1 terabyte of high-risk data across complex file formats to achieve over 98% classification accuracy. Sentra’s smart sampling approach prioritized the most sensitive and high-impact datasets, optimizing performance without sacrificing precision. The platform was deployed seamlessly using Terraform, integrating directly into the customer’s existing AWS architecture. A secure two-role access model, one for metadata access and another for scanning, ensured strict least-privilege control throughout the process.
Following the successful POC, the security team decided to continue scaling Sentra’s coverage across their vast data estate to cover hundreds of petabytes. The data security team was able to easily roll out Sentra according to their data priorities and leverage automation to minimize manual effort and dramatically accelerate risk remediation.
