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Credit Union Eliminates DLP Blindspots

Credit union reduces time to protect sensitive data from 6 months to 1 day.
credit union

Summary of outcome

Challenge

Mid-market firms face big bank security demands without their resources. This firm sought an efficient DLP solution, reducing overhead and internal threat risks.
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Solution

The firm tested Reveal against competitors; Reveal deployed quickly, offering immediate insights and real-time data classification, simplifying processes.
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Result

Reveal provided real-time data protection across diverse tech stacks, reducing exposure gaps, and enhancing security against internal and external threats without affecting user experience.
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team in meeting looking at laptops

About this company

The customer is a not-for-profit member-owned financial cooperative serving over 100,000 members, 300+ businesses, and managing over $2 billion in assets. They employ 200+ people distributed over 25 branch offices; its security needs include in-branch and remote protection of sensitive data.

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Challenge

Mid-market financial firms face regulatory and security challenges similar to major banks but with limited resources. This particular firm, with a compact security team under the CIO, found their existing DLP solution inadequate for internal threat detection. Seeking a comprehensive solution, they prioritized offerings integrating DLP, insider risk management, and UEBA. Essential criteria for their choice were easy deployment, user-friendliness, minimal endpoint impact, low upkeep, and swift value realization.
rubiks cube

Solution

The firm trialed Reveal alongside other vendors, testing them under near-production conditions. Reveal stood out due to its swift deployment and immediate insights, contrasting with competitors' weeks-long setups. With Reveal's phased rollout, the security team instantly understood data movements, identifying risks and potential compliance breaches. What particularly captivated the team was Reveal's real-time data classification. Unlike other solutions that demand pre-classification of sensitive enterprise data, Reveal classifies files upon access, streamlining the data protection process and making it more efficient for organizations.

Result

lightweight agent

Lightweight agent

The customer, like most, relies on a diverse tech stack, Reveal’s modern approach to data protection means full visibility and protection for sensitive data. This includes coverage across the multiple web applications, browsers, and messaging apps as well as traditional desktop applications. The lightweight agent operated invisibly to the end users during normal activity, including time sensitive customer service interactions, leaving existing workflows intact and no impact to customers.

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real time protection

Real time protection

Reveal’s ability to inspect and protect data in real time meant that there was no gap in coverage during the migration and newly created data was automatically protected. Because they were constantly adding new clients and updating existing client records, data discovery often lagged operations, leaving critical records exposed; Reveal took this gap from 6+ months down to zero.
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internal and external protection

Internal and external protection in one

Most importantly, the customer found that Reveal provided protection against both malicious insiders and external threats on a single platform. By moving machine learning and behavioral analytics to the endpoint, Reveal could quickly build individual user baselines, then flag anomalous activity immediately vs. the months required of their legacy DLP solution – both on and off the corporate network. These real time updates to risky activity further closed coverage gaps that their previous solution had and eliminated manual updating by the infosec team.

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upgraded software

Upgraded software

Reveal delivers a tremendous upgrade to the organization from deployment to ongoing operation. The infosec team is better able to protect their sensitive client information and support the challenging and evolving compliance regulations without adding headcount, impacting users, or customers.

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