Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Sensitive data is zooming across dozens of platforms every day, from Slack to email to gen AI platforms and many more sources. We all need this connectivity to stay productive, but the connectivity also creates countless opportunities for data to slip through the cracks. A single misplaced email attachment can end up exposing confidential information in a matter of seconds.

Organizations today face an unprecedented challenge: their most valuable assets can disappear in a matter of milliseconds through accidental sharing, malicious theft, or simple human error. Data Loss Prevention is a strategic approach to safeguarding information before it crosses organizational boundaries, acting as both a guardian and a gatekeeper for critical business assets.

The rise of generative AI has fundamentally changed how we work, create, and collaborate. But as organizations rush to integrate AI tools into their workflows, they're inadvertently creating entirely new categories of data risk that traditional security measures weren't designed to handle.

When it comes to data security, you want it to be accurate, reliable, thorough, and fast. Ideally, faster today than it was yesterday, and faster tomorrow than it was today, to address increasing data security needs in the same amount of time or less. We’re seeing a growing prevalence of employees using unmanaged devices, handling sensitive organizational structured and unstructured data, and accessing managed and unmanaged apps with those devices and that data.

Partners in the defence supply chain, including manufacturers, distributors, and service providers, play a critical role in supporting national security initiatives. These entities frequently handle sensitive and classified information, which necessitates a robust framework of cybersecurity measures. To safeguard this sensitive data from potential breaches and cyber threats, they are required to adhere to stringent government-mandated cybersecurity protocols.

Personally identifiable information (PII) is any data that can be used on its own or in combination with other information to identify, contact, or locate an individual. Examples of PII include full name, Social Security number, passport number, driver’s license number,r and biometric data.

Implementing the right separation and information protection needed to meet defense and national security requirements is often challenging. As government and defense organisations continue to face increasingly sophisticated threats, cybersecurity must evolve to incorporate new technologies and methodologies where applicable. Dynamic Multi-Level Security (MLS) offers a solution.

The complex nature of the U.S. CLOUD Act (CLOUD Act) presents far-reaching implications for global data governance. In this article, we explore how this pivotal legislation is reshaping compliance requirements, transforming privacy frameworks and challenging traditional concepts of data sovereignty, as well as strategies and technologies to ensure compliance.

Managing Jira Cloud empowers and challenges administrators at the same time. Especially when dealing with critical data security and recovery issues. The complexity of tasks like project migrations, account transitions, or backup restores can often lead to unforeseen data loss or operational disruptions. In this article, you’ll explore how Jira admins can boost data security and prevent pitfalls. Especially while maintaining control over data during backups and migrations.