Latest coverage for Threat Detection

As you know, enterprise network security has undergone significant evolution over the past decade. Firewalls have become more intelligent, threat detection methods have advanced, and access controls are now more detailed. However (and it’s a big “however”), the increasing use of mobile devices in business operations necessitates network security measures that are specifically

An ongoing campaign has been observed targeting Amazon Web Services (AWS) customers using compromised Identity and Access Management (IAM) credentials to enable cryptocurrency mining

Identity security fabric (ISF) is a unified architectural framework that brings together disparate identity capabilities. Through ISF, identity governance and administration (IGA), access management (AM), privileged access management (PAM), and identity threat detection and response (ITDR) are all integrated into a single, cohesive control plane

In cybersecurity, speed isn’t just a win — it’s a multiplier. The faster you learn about emerging threats, the faster you adapt your defenses, the less damage you suffer, and the more confidently your business keeps scaling. Early threat detection isn’t about preventing a breach someday: it’s about protecting the revenue you’re supposed to earn every day

Running a SOC often feels like drowning in alerts. Every morning, dashboards light up with thousands of signals; some urgent, many irrelevant. The job is to find the real threats fast enough to keep cases from piling up, prevent analyst burnout, and maintain client or leadership confidence

Cybersecurity researchers have demonstrated a proof-of-concept (PoC) rootkit dubbed Curing that leverages a Linux asynchronous I/O mechanism called io_uring to bypass traditional system call monitoring

Identity-based attacks are on the rise. Attackers are targeting identities with compromised credentials, hijacked authentication methods, and misused privileges. While many threat detection solutions focus on cloud, endpoint, and network threats, they overlook the unique risks posed by SaaS identity ecosystems. This blind spot is wreaking havoc on heavily SaaS-reliant organizations big and small

Behavioral analytics, long associated with threat detection (i.e. UEBA or UBA), is experiencing a renaissance. Once primarily used to identify suspicious activity, it’s now being reimagined as a powerful post-detection technology that enhances incident response processes. By leveraging behavioral insights during alert triage and investigation, SOCs can transform their workflows to become more

The proliferation of cybersecurity tools has created an illusion of security. Organizations often believe that by deploying a firewall, antivirus software, intrusion detection systems, identity threat detection and response, and other tools, they are adequately protected. However, this approach not only fails to address the fundamental issue of the attack surface but also introduces dangerous

The Emergence of Identity Threat Detection and Response Identity Threat Detection and Response (ITDR) has emerged as a critical component to effectively detect and respond to identity-based attacks. Threat actors have shown their ability to compromise the identity infrastructure and move laterally into IaaS, Saas, PaaS and CI/CD environments. Identity Threat Detection and Response solutions help

Digital content is a double-edged sword, providing vast benefits while simultaneously posing significant threats to organizations across the globe. The sharing of digital content has increased significantly in recent years, mainly via email, digital documents, and chat. In turn, this has created an expansive attack surface and has made ‘digital content’ the preferred carrier for cybercriminals

Identity and Access Management (IAM) systems are a staple to ensure only authorized individuals or entities have access to specific resources in order to protect sensitive information and secure business assets

In today's digital landscape, your business data is more than just numbers—it's a powerhouse. Imagine leveraging this data not only for profit but also for enhanced AI and Machine Learning (ML) threat detection

Why SaaS Security Is a Challenge In today's digital landscape, organizations are increasingly relying on Software-as-a-Service (SaaS) applications to drive their operations. However, this widespread adoption has also opened the doors to new security risks and vulnerabilities

Google's cloud division is following in the footsteps of Microsoft with the launch of Security AI Workbench that leverages generative AI models to gain better visibility into the threat landscape.  Powering the cybersecurity suite is Sec-PaLM, a specialized large language model (LLM) that's "fine-tuned for security use cases." The idea is to take advantage of the latest advances in AI to augment

The use of AI in cybersecurity is growing rapidly and is having a significant impact on threat detection, incident response, fraud detection, and vulnerability management. According to a report by Juniper Research, the use of AI for fraud detection and prevention is expected to save businesses $11 billion annually by 2023. But how to integrate AI into business cybersecurity infrastructure

As the threat landscape evolves and multiplies with more advanced attacks than ever, defending against these modern cyber threats is a monumental challenge for almost any organization.  Threat detection is about an organization’s ability to accurately identify threats, be it to the network, an endpoint, another asset or an application – including cloud infrastructure and assets. At scale, threat

TL;DR:  Adopt a modern, test-driven methodology for securing your organization with Detection-as-Code

A new class of security tools is emerging that promises to significantly improve the effectiveness and efficiency of threat detection and response

Back in 2018, Palo Alto Networks CTO and co-founder Nir Zuk coined a new term to describe the way that businesses needed to approach cybersecurity in the years to come. That term, of course, was extended detection and response (XDR). It described a unified cybersecurity infrastructure that brought endpoint threat detection, network analysis and visibility (NAV), access management, and more under