Data classification allows information assets to be organized using an agreed classification, taxonomy, or ontology, thus enabling an effective and efficient prioritization for a data and analytical governance policy covering value, security, access, use, privacy, storage, ethics, and quality. It includes the application of contextualizing metadata to facilitate the use and governance of data and analytics.
Data classification has gained momentum, especially due to digital business transformations (with catalogs needed to adopt new, innovative business moments and decisions), the emergence of more automated and machine learning (ML) based approaches, as well as increased privacy regulations and opportunities.
In particular, GDPR and KVKK increase the need to classify data between individuals and organizations with their security-based classification efforts.


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File analysis (FA) software analyzes, indexes, searches, monitors, and reports file metadata and, in most cases (as in unstructured data environments) file content. FA software reports file attributes and provides detailed metadata and contextual information for better information governance and data management actions. FA software is an emerging technology that helps organizations understand unstructured data growing with the rapid adoption of file shares, e-mail databases, Microsoft SharePoint, content collaboration platforms, cloud platforms and especially Microsoft Office 365.
FA tools reduce risk by determining which files are where and who can access them. It supports improvement in areas such as the eliminating or quarantining of sensitive data, identifying, and protecting intellectual property, finding, and eliminating unnecessary, outdated data that may cause unnecessary business risks. Reports include data owner, location, duplicates, size, last accessed or modified date, security feature changes, file types, and custom metadata. Expectations related to GDPR / KVKK and the desire to comply with the myriad of sub-threads connected to these privacy regulations have greatly increased the interest and awareness of file analysis software.


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Data loss prevention (DLP) is dynamic application of a policy based on the content and the content during transaction. DLP tries to prevent the sensitive data to come out into open by using monitoring, filtering, block and correction features and data loss risks by mistake or on purpose.
DLP products include hardware devices and software products deployed at the endpoint (desktop and servers) and network border. Available forms of content-based controls can be summarized as data blocking, event alerts, automatic encryption, and discovery. DLP products also provide protection for detailed logs that can be used to support court investigations and provide legal hold requirements.
To protect sensitive data, policies also need to be applied to all email channels, including HTTP / S, particularly to webmail apps like Office 365, Google Apps, and messaging apps embedded in email-provider social networking tools like Facebook and Google+. As mobile devices are getting more widespread within the businesses landscape, the rules designed to address the mobility of information on these platforms are becoming increasingly important.
DLP technology is generally perceived as an effective way to prevent accidental disclosure of organized information and intellectual property. In practice, it has proven to be much more useful in identifying undocumented or incomplete business processes leading to data disclosure by mistake and providing policy and procedure training. At the same time, internal users and strangers motivated to extract data will always find ways to steal data, and no technology will be able to fully control it. DLP should also integrate with employee monitoring and products that detect threats and use advanced techniques (such as machine learning) to analyze content more accurately and provide richer content.


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Encryption is basically a type of access control to silos (storage), files or certain structured areas. Format Preserving Encryption (FPE), EKM technologies for structured areas, File Encryption and TDE (Transparent Data Encryption, storage encryption and SED (Self-Encrypting Drive) are examined as separate technologies, although it may be useful to analyze the entire encryption technology market collectively under this title. More than 100 national data privacy laws, including the GDPR, require data protection when the data moves across borders. In addition, internal and external auditors are making increasing pressure for usage of best data protection practices such as access controls and encryption.


Strategic tokenization refers to a framework that aims to plan for the regulation and management of multiple token types to support value creation and change to create or expand the digital ecosystem. Tokens are defined as proxies that represent value and related contractual agreements. The value can be money, data, assets, identities, and other information.
Commercial tokenization has been around for over a decade and has become the dominant data layer control to protect sensitive credit card data (CHD) since 2015. Compared to traditional encryption systems for customers, the benefit of tokenization is because is a service / vault solution, so it does not require key management at the end user level. Another benefit of the tokenization over traditional encryption systems is that although tokenized data can be reused by applications, encrypted data cannot be used.
X.509 Smart Hardware Tokens for User Authentication:
X.509 smart hardware tokens are public key tokens that primarily carry X.509 public key infrastructure (PKI) credentials for user authentication. Typical form factors include smart cards, USB tokens that insert the same chips, and contactless smart cards.


Format-preserving encryption (FPE) is used to protect the data at rest and in use, as well as data when accessed through apps, while preserving the original data length and structure. It is used to protect fields in an increasing number of relational database management systems (RDBMSs), data warehouses and NoSQL databases. FPE is becoming more important to minimize the risks of hacking or internal harassment and to meet compliance requirements by controlling access of administrators and users.
The NIST standard for FPE (FF1 mode only) has enabled organizations to accept them to address evolving compliance and threat environments without having to comprehensively modify their databases or applications. It provides a powerful and agile method to prevent unauthorized users from accessing data. This will help to comply with the data privacy statement (PDP) and residency requirements for PII and PHI, as well as data breach disclosure regulations. FPE should be used to enforce policy rules according to the data security governance framework.

Self-Encrypting Mass Storage Drives:

Interoperable storage encryption embedded in drive controllers can significantly improve the performance of secure mass storage drives. Showcase technologies for standard self-encrypting drives (SEDs) are Opal Security Subsystem Class (SSC) and Enterprise SSC, collectively referred to as “SED” in this study. Both SSC types have their roots in the TCG Storage Architecture Core Specification developed by the Trusted Computing Group (TCG) Storage Working Group and covers individual drives, arrays, and storage interfaces.

DB encryption

Database encryption solutions protect column, table, or database incidents of in-house relational database management systems (RDBMS).
Due to data privacy laws and data residency issues, the focus on encryption as a risk-based access control is increasing. Encryption is gaining importance to minimize breach risk and protect privacy by implementing separation of duties (SOD) and access control.
Database encryption, when applied consistently and aligned with the right risks, can offer a strong level of control against unauthorized data access. It can’t be used effectively unless combined with other tools such as DAP.
As a result, concerns about the privacy of PII and PHI, data breach disclosure regulations, and the PCI Data Security Standard (DSS) put pressure on organizations to use further encryption. Cross-border data residency also increases the need for FPE, tokenization or DDM to implement stronger SODs. RDBMS encryption is increasingly recommended by auditors. Organizations need to use the data security governance framework to examine how other compensatory security controls can be implemented as part of a broader, data-centric security strategy.


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You can automatically back up the files on your computer. Thus, you can prevent data loss due to disk failures and cyber-attacks (Ransomware, etc.). You can easily access your data backups from anywhere you want and restore them back to your machine. All data in the DivvyDrive system is encrypted and distributed with proprietary BLOCKCHAIN ​​technology. It is impossible to access, read and make sense of data from outside the system. In this way, it prevents malicious software such as zero-day attacks or viruses from damaging your files. You can edit and share your files with anyone you want via secure links or work on them securely. DivvyDrive has its own file management system, and it stores and classifies all files with time stamp in accordance with KVKK and other data regulations and ensures file integrity and invariance. You can view your stored data wherever you want with mobile access features and conduct detailed searches.


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