APIs are the backbone of modern digital ecosystems, but their misuse can expose systems to cyber threats. Effective API throttling not only optimizes performance but also acts as a critical defense mechanism against abuse, such as denial-of-service attacks. Discover how this powerful strategy enhances API security and safeguards your organization’s data in an interconnected world.

It is becoming increasingly important for organizations to manage Kubernetes security costs as they deploy, scale, and manage containerized applications using Kubernetes. Organizations must ensure robust protection without overpaying, especially as 89% of enterprises experience at least one Kubernetes or container-related security incident annually (VentureBeat).

2024 marked a transformative shift in cybersecurity with AI and data driven cyber security leading the change. As the threat landscape evolves, the stage is set for further advancements in continuous risk management, threat monitoring, resilience, and governance in 2025, shaping a more secure and dynamic digital landscape where cybersecurity is becoming a catalyst for business success and compliance.

Traditionally, approaches to Attack Surface Management (ASM) went something like this: A business scanned its own IT estate to discover assets and understand what its attack surface actually included. We can think of this as Phase I. Following the completion of an asset inventory, they assessed each of their assets to identify risks and vulnerabilities, such as open ports, certificate issues, DNS misconfigurations, and more.

As we enter 2025, the frequency and sophistication of cyberattacks on critical national infrastructure (CNI) in the US are rising at an alarming rate. These attacks target the foundational systems that support everything from energy and water to transportation and communications, and the consequences are far-reaching and potentially catastrophic. They impact not just the operations of these services but also the very way of life for affected populations.

In my experience, computers are only as smart as the person in front of them. Same with AI. The results are dependent on the prompts given. Today, users typing prompts from their brains into Microsoft Security Copilot may find it hard to get value. Prompts with adequate specificity are difficult to create, let alone repeat.

No matter what industry you’re in, solving for performance issues is always at the top of every networker’s mind. Especially with applications like Microsoft Teams, or similar unified communications as a service (UCaaS) solutions from Zoom, Webex, RingCentral, Mitel, Vonage, or 8×8, that have become critical to day-to-day business operations.

In today’s cloud-native environments, network security is more complex than ever, with Kubernetes and containerized workloads introducing unique challenges. Traditional tools struggle to monitor and secure these dynamic, interconnected systems, leaving organizations vulnerable to advanced threats, such as lateral movement, zero-day exploits, ransomware, data exfiltration, and more.

In this guest post, Eric Newcomer, Principal Analyst at Intellyx explores the practical applications and limitations of generative AI. Generative AI is a game-changing technology. Chat bots seem like magic compared to a traditional static web search. You submit questions in natural human language and receive back complete sentences and paragraphs. But it isn’t always clear what it is really good for, given limitations such as hallucinations and inaccurate answers, and possible bias.

On January 7, 2025, CrowdStrike identified a phishing campaign exploiting its recruitment branding to deliver malware disguised as an "employee CRM application." The attack begins with a phishing email impersonating CrowdStrike recruitment, directing recipients to a malicious website. Victims are prompted to download and run a fake application, which serves as a downloader for the cryptominer XMRig.