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.
FILE ANALYSIS
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of sensitive data, identifying, and protecting intellectual property, finding, and eliminating unnecessary, outdated data that may cause unnecessary business risks.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.
DATA LOSS PREVENTION (DLP)
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.
ENCRYPTION
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.
