Data Masking Solutions

Data masking is a data security technique in which a dataset is copied but with sensitive data obfuscated. This benign replica is then used instead of the authentic data for testing or training purposes. What do I need to know about data masking? Data masking does not just replace sensitive data with blanks.

Data masking solutions. One of the core solutions Diyotta provides is revamping the conventional data movement process and building a Modernized ETL.It helps in dealing with most of the data-masking and integration challenges and ensure a smooth workflow. The masking algorithms are applied to all the PII data fields and provision the masked data to downstream applications, non-prod environments, and marketing. By masking data before it is sent to downstream environments, sensitive information is removed and the surface area of risk decreases. There are a variety of data masking tools or data obfuscation tools on the market, and it's worth discussing the evolution of these solutions over time to compare each tool properly. Replace sensitive data with fictional but realistic values using a variety of masking techniques. Choose from pre-defined masking techniques or create custom data transformers. Masked data retains realism of production data and reduces sensitive data exposure, complying with data privacy and protection laws. Data masking is a method of creating a structurally similar but inauthentic version of an organization’s data that can be used for purposes such as software testing. The purpose is to protect the actual data while having a functional substitute for occasions when the real data is not required.

Data masking or data obfuscation is the process of hiding original data with modified content (characters or other data.). The main reason for applying masking to a data field is to protect data that is classified as personally identifiable information, sensitive personal data, or commercially sensitive data.However, the data must remain usable for the purposes of undertaking valid test cycles. Take back control of data, improve the auditability of data masking, and save time for IT personnel. Alongside encryption, the pseudonymisation or masking of data is a vital component in the enterprise IT toolbox.For requirements such as employee training or the development and testing of new software, organisations often need sets of “real” data. Data Masking ≄ Pseudonymization and Tokenization. Although often used interchangeably; data masking, pseudonymization and tokenization are different de-identification techniques.Although pseudonymization removes direct identifiers, it leaves indirect identifiers untouched, potentially including quasi-identifiers, and therefore is insufficient to de-identify data. Many organizations use production data to populate their test environments. The problem with this is that if there is sensitive data in your production environment, then you are exposing that data to software developers and testers. IBM offers the following two solutions to solve this problem: The InfoSphere Optim Data Masking option for Test Data Management, and the InfoSphere DataStage Pack.

IBM Security Guardium Data Encryption consists of an integrated suite of products built on a common infrastructure. These highly-scalable solutions provide encryption, tokenization, data masking and key management capabilities to help protect and control access to databases, files and containers across the hybrid multicloud—securing assets residing in cloud, virtual, big data and on-premise. Therefore, for having the best solutions out of your data masking, you must look into your future enterprise’s needs. Test results of Data Masking. This is the final step. QA and testing are required to ensure the concealing arrangements to yield the desired outcomes. Data masking is sometimes described as data obfuscation and is related to data encryption, and tokenization. An important distinction is that data masking is sometimes a non-reversible process where sensitive data is cloned and transformed into something credible but, once created, wholly different than the source. Informatica Persistent Data Masking is an accessible data masking tool that helps an IT organization to access and manage their most complex data. It delivers enterprise scalability, toughness, and integrity to a large volume of databases. It creates a reliable data masking rule across the industry with a single audit track.

Role-based and location-aware sensitive data masking in all of customer’s databases, both on-premises and in the cloud. Anonymize Sensitive Data‎. Data masking from DataSunrise can either be static or dynamic to ensure that all customer’s data security requirements are met and all data in the customer’s databases remains intact. Data Masking Solutions: Creating Secure and Useful Data Version 1.0 Released: August 10, 2012 Securosis, L.L.C. 515 E. Carefree Blvd. Suite #766 Phoenix, AZ 85085 T 602-412-3051 info@securosis.com www.securosis.com The AI-Powered Health Data Masking solution helps healthcare organizations identify and mask health data in images or text. This solution uses Amazon Comprehend Medical to detect health data in a body of text, Amazon Rekognition to identify text in an image, Amazon API Gateway and AWS Lambda to provide an API interface for this functionality, and AWS Identity and Access Management (IAM) to. Dynamic Data Masking is a column-level security feature that uses masking policies to selectively mask data at query time that was previously loaded in plain-text into Snowflake. Next Topics: Understanding Dynamic Data Masking

Data masking enables you to comply with data privacy laws like the GDPR and HIPAA. IRI data anoymization software products will classify, find, de-identify, and risk-score PII. Use them to consistently encryption, redaction, pseudonymization, and other obfuscation functions to mask your data wherever it lives. The support of stored procedure or function masking differentiates DDM from other masking solutions. For any organization, data is an invaluable asset and its protection is as important as growth of business. Sometimes the volume of data flow across systems governs the success of the business, but it is our responsibility to prevent sensitive. Data masking remains an emerging market. When Bloor Research conducted its most recent survey of the market for Data migration in 2011 it found that only a minority of projects that required data masking were actually using tools for this purpose. As the complexities of data masking become more familiar to both organisations and developers alike it is likely that this market will grow. For persistent data masking, Informatica’s proven platform can scale to meet the requirements of organizations that need to mask large data stores. Both solutions integrate without disruption, as no changes are required for applications or databases.

The SAP HANA data masking solution by DataSunrise is an effective and reliable measure to hide sensitive information from unwanted users. Outsourced specialists would still be able to use obfuscated data for purposes of application development, conducting tests. Real data is replaced with fictive info or characters set by the administrator.