When we think of threats to data, malicious hackers who are out to profit off our information tend to come to mind. But what about the risks that are hiding in plain sight? Outdated systems, shared passwords, and lax controls may all seem like low grade issues – until they...
With cloud data platforms becoming the most common way for companies to store and access data from anywhere, questions about the cloud’s security have been top of mind for leaders in every industry. Skepticism about the security of cloud-based solutions can even delay or prevent organizations from moving workloads to...
More organizations than ever are leveraging the power of multiple cloud data platforms for business-driving analytics. In fact, 93% of organizations have a multi-cloud strategy for analytics and data science, and 87% have a hybrid cloud strategy. In the next two years, the trend toward diverse cloud data ecosystems will continue, as more...
Data protection measures are only as strong as their ability to outsmart increasingly advanced technology and bad actors, while still preserving the utility and value of the underlying data. With it taking an average of nearly 300 days to catch a data breach, being proactive about how data is protected is more...
As data moves among the storage, compute, and analysis layers of a data stack, there is constant need for measures to ensure its security and protect personally identifiable information (PII). This security is often required by law, as is evident through financial regulations like PCI-DSS, the Gramm-Leach-Bliley Act, and more. Immuta’s integration with Databricks helps...
Data masking is a data access control and security measure that involves creating a fake but highly convincing version of secure data that can’t be reverse-engineered to reveal the original data points. It allows organizations to use functional data sets for demonstration, training, or testing, while protecting actual user data from breaches or...
What Is Data Tokenization? Why Is Data Tokenization Important for Data Security? When Should I Use Data Tokenization? Top Tokenization Use Cases What’s the Difference Between Tokenization and Encryption? Applying Data Tokenization for Secure Analytics
Data masking replaces sensitive information with fake but convincing versions of the original data. Given the necessity for sensitive data protection, data masking must be adaptable to any data environment. Regardless of the size, purpose, or tools in your data stack, there is a type of data masking that fits your...
What Is Data Masking? Why Do I Need Data Masking? What Are Some Data Masking Techniques? When Do I Need Data Masking? Top Use Cases How Do I Apply Dynamic Data Masking?
The world of data is full of interesting language, complex terms, and more acronyms than a bowl of alphabet soup. Being such a dynamic field, these terms are always being developed, adopted, and adapted to describe new and exciting advances. That said, it’s easy to feel out of the loop...
As data use has become ubiquitous, data breaches have followed suit. Though down from a peak of 125 million compromised data sets in late 2020 – which was at least partly attributable to the sudden shift to remote work during the pandemic – data breaches still expose millions of data assets every...
Is it possible to manage a successful business or organization if you’re not successfully managing your data? Data management in business can be perceived as both an essential task and an annoying nuisance. The fact is that if your organization deals with data of any kind, you need a system...
There’s an old adage that all press is good press, but one kind of attention that’s been showered on companies both large and small in recent months is the type that no organization wants — scorn after an inadvertent data leak. While keeping customer data safe from leaks may seem...
For more than 160 years, Credit Suisse has been providing customers with best-in-class wealth management, investment banking, and financial services. As one of the largest multinational financial firms, technology is critical to the company’s global operations — and with nearly 50,000 employees worldwide, there is a high standard for efficient, adaptable IT...
Data masking is one of the most important tasks in data governance. It may seem simple in practice, but getting it right can be the difference between a straightforward path to properly secured data, and a complex, confusing experience that leaves your data exposed to breaches. To add to the...
Data generalization is the process of creating a more broad categorization of data in a database, essentially ‘zooming out’ from the data to create a more general picture of trends or insights it provides. If you have a data set that includes the ages of a group of people, the...
Data de-identification is a form of dynamic data masking that refers to breaking the link between data and the individual with whom the data is initially associated. Essentially, this requires removing or transforming personal identifiers. Once personal identifiers are removed or transformed using the data de-identification process, it is much easier to...
Businesses and organizations hold more personal data now than ever before. In general, this data is used to better serve customers and more effectively run business operations. But with plenty of malicious parties eager to access personal data and track sensitive information back to its source, finding a way to...
Immuta and Databricks have deepened our strategic partnership and augmented our native product integration by announcing Immuta’s support for Databricks SQL Analytics and deployment on Google Cloud. These are the latest capabilities offered to joint Immuta and Databricks customers, which also include metadata-driven policy authoring, fine-grained and attribute-based access controls, and most recently, expanded access control for R...
In today’s day and age, we’re accustomed to technological advances and capabilities being uncovered all the time. However, mere availability does not necessarily correspond to immediate adoption. This is at least somewhat true for differential privacy. The first seminal contribution on the topic was published in 2006 by Microsoft Distinguished Scientist Cynthia Dwork,...
A few years removed from the peak of the pandemic, we’re still understanding COVID-19’s impact on daily life. But one thing is certain – the pandemic made clear the importance of collecting and sharing health data. The virus’ unpredictable and widespread nature made tracing and testing critical to understanding transmission...
Data engineers and product managers are often responsible for implementing various controls and audit capabilities when managing healthcare data. To enable faster, data-driven innovation, these data professionals – particularly those who come to healthcare from other industries like tech or financial services – apply best practices such as deploying a proven data analytics stack...