Data Loss Prevention and Classification

GTB Data Loss Prevention is a data loss prevention platform that detects and blocks sensitive data being shared without authorization or taken outside the organization over endpoint, network, email, web and cloud channels. Using content analysis, contextual controls, data fingerprinting and classification, it tracks where data resides, who uses it and which channel it moves through, and enforces corporate policy accordingly.

Deploying the solution provides central visibility over critical data and consistent policy enforcement. It reduces risks such as sending to the wrong recipient, unauthorized cloud uploads, removable media use and deliberate exfiltration, while supporting compliance work for KVKK, GDPR and sector-specific regulations. Security teams can therefore stop incidents at an earlier stage, shorten investigation time, and limit the financial and reputational losses a data breach can cause.

Zscaler Data Loss Prevention is a unified, cloud-based DLP solution that inspects the movement of sensitive data across the internet, SaaS, email, private applications, cloud infrastructures and endpoints. Running on the Zscaler Zero Trust Exchange, it can inspect content within SSL/TLS traffic, classify sensitive data, and apply policy according to user, device, application and data context.

A cloud-delivered architecture provides central control while reducing the dependence on additional hardware for branch and remote user traffic. Applying the same classification and policy structure across web, cloud, AI and endpoint channels closes protection gaps. The organization gains a clearer view of where sensitive data resides and how it is shared, and can manage modern risks such as shadow IT, personal cloud storage and unauthorized AI use far more quickly.

DECE Software is a data governance solution that classifies the documents and content in an organization's data sources automatically or with user assistance, according to their meaning, sensitivity level and the business rules defined. Working across structured and unstructured data, it identifies data classes such as personal data, financial information, intellectual property or organization-specific content and manages them with appropriate labels.

Accurate classification means security policies such as DLP, access control, retention and disposal rest on reliable data context. The organization can determine more quickly which data is critical, reduce the burden of manual review, and build an auditable inventory for regulations such as the KVKK. As a result, data ownership becomes clear, the risk of mistaken sharing falls, and it becomes easier to direct security investment at the right data sets.

On the masking side, DECE is a data privacy solution that uses masking, anonymization and, where needed, synthetic data generation techniques to hide the real values of sensitive data from unauthorized people and from non-production environments. By transforming personal and critical fields in databases or files in a way that preserves functional integrity, it enables safe use in test, analysis, training and data sharing scenarios.

The solution shrinks the breach surface by reducing development and test teams' need to access real customer or employee data. In external sharing, reporting and analytics processes it preserves the business value of the data while lowering the risk of identification. It supports compliance with the KVKK and similar privacy obligations, reduces manual data cleansing work, and helps prevent projects from being delayed on security grounds.

As for DECE Data Discovery, it is a data discovery solution that scans file systems, document repositories, databases and other data sources within the organization to find and inventory sensitive, personal or regulated information. Through content and context analysis it determines the type and location of the data, the users who may own it, and its risk status, building a central view.

Revealing data sets an organization does not know about or has forgotten means data security programmes can be run against a real inventory. Unnecessary, old or risky data can be cleaned up, and access and retention policies applied to the right sources. Response times to audit and compliance requests are shortened, while the scope of records potentially affected by a breach is determined faster and operational uncertainty is reduced.