THE PROBLEM
Organizations face this problem that delves into the intricate task of managing data access and assessing risks within an organization's ecosystem, particularly targeting open file shares. The challenge lies in the potential vulnerabilities posed by these open shares, which may grant unrestricted access to sensitive data, including personally identifiable information (PII) and protected health information (PHI). Identifying and securing these open shares is paramount to ensuring data integrity, compliance with regulations such as GDPR or HIPAA, and safeguarding against potential breaches or unauthorized access.
OUR SOLUTION
The integration of Zantaz Data Optimization with Data Ownership capabilities offers a powerful solution for addressing the challenges of data access governance and risk assessment. By systematically identifying open file shares, assessing risks associated with sensitive information exposure, and enabling targeted remediation efforts, our solution empowers organizations to enhance data security, comply with regulatory requirements, and mitigate potential data breaches. With a focus on precision, scalability, and seamless integration, our solution equips decision-makers with the tools they need to safeguard sensitive data assets and ensure ongoing compliance in an ever-evolving threat landscape.
Zantaz Data Optimization serves as the backbone of our solution, leveraging advanced scanning capabilities to meticulously comb through disparate data sources. It systematically identifies open file shares, especially those accessible to the entire security group, which are often overlooked vulnerabilities in organizational data security frameworks. Through its robust algorithms and extensive data analysis capabilities, Zantaz Data Optimization pinpoints potential risk areas within these open shares, laying the foundation for effective risk assessment. Furthermore, our solution incorporates a specialized sensitive data model, which is seamlessly integrated into Zantaz Data Optimization. This model is trained to recognize and classify personally identifiable information and protected health information entities within documents residing in open file shares.
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