End-to-End Data Security

Data Security Transactions

Data Leakage/Loss Prevention, Data Classification, Sensitive Data Discovery, Data Destruction-File Deletion, and Anonymization processes…

Data Loss Prevention (DLP)

Data loss prevention (DLP) is a set of tools and processes used for “detection and prevention” to ensure that sensitive data is not lost, misused, or accessed by unauthorized users. DLP software classifies regulated, confidential and business-critical data and identifies policy violations identified by organizations or within a predefined policy suite typically driven by regulatory compliance such as GDPR, ISO 27001, HIPAA, GDPR, or PCI-DSS. Once these violations are identified, DLP enforces remediation with warnings, encryption, and other protective actions to prevent end users from accidentally or maliciously sharing data that could put the organization at risk. Data loss prevention software and tools monitor and control endpoint activities, filter data streams in corporate networks, and monitor data in the cloud to protect data. DLP also provides reporting to meet compliance and auditing requirements and identify weak and abnormal areas for forensics and incident response.

Data Classification

Identifying and tagging sensitive information can ensure that it is stored in secure locations and given the appropriate level of access to each user account. It also helps enforce the principle of least privilege.

We suggest that you define, implement and communicate a Data Classification Policy to provide employees with clear guidance on how to classify data processed within the organization. This policy should describe the classification levels used and outline the rules for how each classification level should be handled. A data loss prevention tool alone cannot understand the nuances of certain information or the full context of a document without classification.

Sensitive Data Discovery

Sensitive and business-critical data within the enterprise should be determined and analyzed. It is imperative to identify who has access to all discovered data and how it is used. Data discovery is a business intelligence process that involves scanning a network, identifying various locations containing data, integrating data for analysis, and deriving insights that can be passed on to managers and IT personnel so they can make informed decisions.

Importance of Data Discovery: A sensitive data discovery tool is a vital asset to organizations because when data is left unprotected, it can be exposed to undesired parties and harm the company's credibility.

Difficulties of Data Discovery in Organizations: Sensitive information can be dispersed across various locations due to the dynamic nature of data. Manually inspecting all endpoints and cataloging stored data is nearly impossible in SMBs or especially large organizations. An effective solution is to implement software for automated data discovery.

Data Destruction and File Deletion

Cleaning the hard drive with special software is the best choice. Files are deleted and replaced with random characters using a utility program. These characters have no value or meaning. That is why it is useless to someone who gets an old file.

The hard drive may require you to purchase disk management software or it can be provided for free.

Physical destruction of documents is the process of breaking them into small pieces with the help of paper shredder machines. These parts are then classified as metal, plastic, and glass. After it, they are remelted and used to make new products.

Data Anonymization

Data anonymization is the process of protecting private or sensitive information by deleting or encrypting identifiers that connect an individual to stored data. For example, you can run Personally Identifiable Information such as names, social security numbers, and addresses through a data anonymization process that retains the data but keeps the source anonymous. However, even if you clear the data of the identifiers, attackers can use de-anonymization methods to re-track the data anonymization process. Because data often comes from multiple sources (some of which are public), anonymization techniques can cross-reference sources and expose personal information.

Anonymized data may be personally identifiable by data anonymization tools to protect a data subject's privacy. It is a type of information cleansing where it encrypts or removes information from datasets. This reduces the risk of unwanted disclosure during the transfer of information across borders and facilitates post-anonymization evaluation and analytics.

Data anonymization algorithms are designed to automate the process of protecting the identity of a data subject in a dataset. Some data anonymization methods include:

Anonymity is the process of preventing an individual's identity from being removed from all personally identifiable information. A common identifying technique is a nickname, which hides person identifiers from data records by replacing real names with a temporary ID.

Data masking adds another layer of security to the anonymization of data by masking specific pieces of data and only showing the most relevant pieces of data to data processors who are expressly authorized to see those specific pieces of data. This facilitates secure application testing, where authorized testers only see what they need to see.

Bilgi Birikim Sistemleri believes that offering the “right solution” to the customer is the greatest value. It has been serving in this field for years aiming to adhere to the determined project budget and complete it on time. In this context, it has performed many successful projects with its competent and experienced personnel. Please let us to contact you so that we can introduce our work and offer you the right solution.

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