The Linux/UNIX process model creates a new process by cloning the currently running one using the fork() system call. Subsequently, exec()—or one of its variants—loads a new program image into the newly cloned child process. There are a variety of issues that stem from using this approach in modern systems. There are also various widely used techniques and APIs aiming to alleviate the issues in different ways with different degrees of success and quirks.

While both data warehouses and lakes are big data storage solutions, they are useful in distinctly different situations. Data warehouses store structured data that can be accessed and interpreted by anyone with permission to do so, whereas a data lake is an unstructured storage space for large quantities of raw data. Data lakes store big data in its raw form, with minimal structure and few controls over what data is included or excluded from the storage space.

DevSecOps follows the same trend as Agile and DevOps: how can developers create software that’s better, faster, and less expensive? The DevSecOps motto — “software, safer, sooner” adds the missing piece to the latest approach to quicker product development. Security, previously an afterthought in the product development lifecycle, is now becoming an integral part of the process.

Cloud programs like Slack and Google Drive allow businesses to work collaboratively and efficiently, often at a low cost. However, these cloud platforms open a business up to new levels of risk: sharing information via cloud programs can put customer data at risk. Cloud security architecture provides a way to recognize and remedy vulnerabilities that result from using cloud service providers (CSPs).

Many organizations recognize the emergent need to discover, classify, and protect their sensitive information stored in cloud applications (SaaS) and infrastructure (IaaS) via a dedicated cloud content inspection process. However, cloud-native detection engines are a relatively new technology, and many corporate Information Security teams or Product Security developers are, understandably, not yet familiar with how to effectively evaluate cloud content detection.

EDRM sometimes referred to as Information Rights Management (IRM) is a core-data-centric technology that offers uninterrupted protection to unstructured data. It protects sensitive information/data everywhere by managing and enforcing access and usage rights to the information throughout its lifecycle, no matter where the information is distributed. EDRM controls how employees and partners use sensitive information.

In today’s world, data breaches are a fact of life for both consumers and companies. It’s become somewhat of a truism to point out that for many companies breaches are a matter of if not when as defenders are at a significant disadvantage.

While data security has been an important topic since the internet’s inception, the issue is experiencing renewed prominence as platforms collect and struggle to secure copious amounts of personal information and prevent cyberthreats at the same time. The early 2000s were characterized by unprecedented growth and participation in the digital data economy, but the 2010s saw the creeping consequences of this ecosystem.

Data loss refers to the unwanted removal of sensitive information either due to an information system error, or theft by cybercriminals. Data leaks are unauthorized exposures of sensitive information through vulnerabilities on the digital landscape. Data leaks are more complex to detect and remediate, they usually occur at the interface of critical systems, both internally and throughout the vendor network.