Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

When a hacker breaches a network or system, data exfiltration often follows. But what is data exfiltration and how can you prevent it?

Generative Artificial Intelligence (AI) has revolutionized various fields, from creative arts to content generation. However, as this technology becomes more prevalent, it raises important considerations regarding data privacy and confidentiality. In this blog post, we will delve into the implications of Generative AI on data privacy and explore the role of Data Leak Prevention (DLP) solutions in mitigating potential risks.

Cyber threats are a growing concern for organizations of all sizes. Data breaches, malware infections, and ransomware attacks can severely disrupt operations, including financial loss, reputational damage, and legal liabilities. As a result, it is essential to proactively monitor your environment and identify malicious activity to detect threats before they can cause significant damage.

There’s no denying it, public cloud is here to stay and there’s a pretty good chance that your company is running some workloads on Amazon Web Services.

Efforts to facilitate critical information sharing between multinational coalition partners, including traditional and non-traditional allies, have become more critical in light of recent conflicts. In addition, partner nations need to share and work together on highly classified information at different security classifications and clearances, which poses a challenge to multinational collaboration efforts.

The content of this post is solely the responsibility of the author. AT&T does not adopt or endorse any of the views, positions, or information provided by the author in this article. In today's digital age, sensitive information is constantly being shared and transmitted over various electronic devices and networks.

Last year, we launched Cato DLP to great success. It was the first DLP engine that could protect data across all enterprise applications without the need for complex, cumbersome DLP rules. Since then, we have been improving the DLP engine and adding key capabilities, including user-defined data types for increased control and integration with Microsoft Information Protection (MIP) to immediately apply sensitivity labels to your DLP policy. Let’s take a closer look.

As organizations increasingly adopt cloud services and applications, securing access to these services becomes crucial to protect sensitive data and maintain compliance. Cloud Access Security Brokers (CASBs) have emerged as a key component in providing comprehensive visibility and control over cloud services.

Attack surfaces are a fundamental concept within information security. However, attack surfaces can be constituted of different things. For example, some formulations of an attack surface include not just software and hardware, but the people using them. In this post, we’re going to cover four common types of attack surface, discuss how you should think about the risks associated with each type, and best practices for addressing these risks.