Organizations can realize automated, comprehensive protection of sensitive data, aligned with business policies and complex compliance regulations
Hod HaSharon, Israel, June 24, 2020 — MinerEye, a leading data protector company, today announced the availability of its innovative DataTracker™ via the Microsoft Azure Marketplace. As organizations strive to strengthen their data protection, they are faced with tremendous obstacles in labeling sensitive and confidential data accurately and quickly. This information is often contained in voluminous “piles” of files, scattered among cloud and on-premise systems.
To overcome these obstacles, MinerEye’s DataTracker™ offers automated virtual labeling by providing a simulator for analyzing and resolving overlapping data protection and data privacy policies among file data. Simultaneously, it scores the risk on unlabeled or mislabeled files. By doing so, MinerEye enables organizations to execute detailed, granular data protection policies that resolve business/security policy conflicts often caused by end-user error or unsynchronized compliance requirements.
“Only an automated, AI-based technology can realistically overcome the huge task of inaccurate and underused data protection. MinerEye’s DataTracker tackles the challenge by extracting all the granular data, applying machine learning-based analytics and tagging virtual labels to the files in accordance with the organization’s policies. The files receive the correct physical labels following this automated process,” said Yaniv Avidan, CEO and Co-founder of MinerEye.
“MinerEye’s ability to get to the granularity in data, down to the entity and graphical object level, enables aligning a single file to multiple virtual labels, thereby matching the intentions of company policies to its data, even within its dark data.”
“During these difficult times, it is essential that an organization’s business is supported by good cyber practice. We are very pleased to offer this technology to the Microsoft Azure community so that they may achieve accurate policy enforcement on a granular level.” added Avidan.