A few years back, data was constrained to the on-premise infrastructure. Data management, governance, and protection were fairly uncomplicated in this enclosed environment. The emergence of cloud computing and multi-cloud infrastructures has not only introduced more complexity in data management and governance, but it has also increased security risks significantly.

The Fintech industry deals with financial and personal data of customers on a massive scale. From credit card transactions to identity verification details, companies collect and process huge amounts of sensitive data, making them an attractive target for cybercriminals. To ensure the highest standards of data protection and risk management, constant monitoring and analysis of security data is crucial.

Nightfall’s recent “State of Secrets” report uncovered that collaboration, communication, and IT service tools have the highest risk of data exposure, particularly in industry-leading SaaS apps like Slack and GitHub. This trend highlights an incredibly pervasive (yet often overlooked) risk in cloud cybersecurity: Data sprawl.

Data security is vital to your organization’s well-being. Even a single data exfiltration incident can bring unwanted attention to your organization, leading to reputational and financial losses. That’s why companies across industries pay maximum attention to their cybersecurity measures and constantly enhance them. In this article, we explain the meaning of data exfiltration and how it’s performed.

As business operations evolve, the challenge of securely moving data within the cloud is one of elevated concern. Transferring sensitive information to it is another. Many are caught between what worked in on-prem technologies and what is needed in cloud-based architectures. Others have sidestepped the security challenges by implementing a Managed File Transfer (MFT) solution.

If you are using Microsoft 365 (M365) or SharePoint and need to comply with or learn more about US export control requirements, read our 6 W’s of ITAR and EAR Compliance to help you comply with these strict U.S. regulations.

When we started to build Bearer, we wanted to understand how to validate the quality of our findings and be able to benchmark it. Code security scanning solutions are notorious for reporting a lot of false positives and other deficiencies, and even though we believed we could do much better, we needed a way to prove it. In Java, there is an OWASP project, BenchmarkJava, which makes it easy to compare the output of two software security solutions.

The most famous data breaches–the ones that keep security practitioners up at night–involved the leak of millions of user records. Companies have lost names, addresses, email addresses, Social Security numbers, passwords, and a wealth of other sensitive information. Protecting this data is the highest priority of most security teams, yet many teams still struggle to actually detect these leaks.

In today's data-driven world, businesses must navigate the complexities of data management while ensuring compliance with an ever-growing array of laws and regulations. Two concepts that often arise in this context are data sovereignty vs data residency. While related, these terms refer to distinct aspects of data management. Understanding their differences is crucial for businesses to make informed decisions on where to store their data and how to remain compliant with data protection regulations.

While Gen AI tools are useful conduits for creativity, security teams know that they’re not without risk. At worst, employees will leak sensitive company data in prompts to chatbots like ChatGPT. At best, attack surfaces will expand, requiring more security resources in a time when businesses are already looking to consolidate. How are security teams planning to tackle the daunting workload? According to a recent Morgan Stanley report, top CIOs and CISOs are also turning to AI.