Network Detection and Response | Breaking Cybersecurity News | The Hacker News

Despite the abundance of telemetry at analysts’ disposal, many security operations teams struggle to answer a few basic questions during incident investigation: What happened? What evidence do we have? How do we know we’re seeing it all, in context? Answering these questions requires teams to go beyond alerts, the most common basis for initial triage. But investigations (and their outcomes) require defensible evidence, not assumptions, which is what alerts tend to offer.  Alerts are becoming less useful as vulnerability discovery accelerates (a.k.a., the Mythos Era). Most organizations can’t investigate the volume of new findings with existing workflows. Even with increased automation, SecOps teams need validated evidence of active exploit and exposure, not more raw telemetry. As AI expedites both attacks and defense, security teams need to lay the groundwork that allows them to validate findings, understand attacker behavior, and stop suspicious traffic before it results in...

Ask a cybersecurity pro about Network Detection and Response (NDR) and you might still hear "Noisy," "Too much data." But ask the teams running NDR that includes agentic AI capabilities and you'll hear they're actually using it to catch threats earlier, triage faster, and chase fewer false positives. The old complaint lingers in part because reputations are sticky, and because NDR has evolved faster than the narrative. The origins of noise NDR deployments have always given analysts deep visibility into network traffic, encrypted session behavior, and protocol anomalies. But visibility often came as raw material, not finished intelligence. Some systems required extensive manual tuning during deployment to prevent SIEM overload. Organizations that couldn't invest that time (or didn't know how important it was) helped cement NDR's "alert firehose" or "noisy" reputation. NDR with agentic AI turns noise into narrative A...

Security Operations Center (SOC) teams are facing a fundamentally new challenge — traditional cybersecurity tools are failing to detect advanced adversaries who have become experts at evading endpoint-based defenses and signature-based detection systems. The reality of these “invisible intruders” is driving a significant need for a multi-layered approach to detecting threats, including Network Detection and Response (NDR) solutions.  The invisible intruder problem Imagine your network has been compromised — not today or yesterday, but months ago. Despite your significant investments in security tools running 24/7, an advanced adversary has been quietly moving through your systems, carefully avoiding detection. They've stolen credentials, established backdoors, and exfiltrated sensitive data, all while your dashboards showed nothing but green. This scenario is not hypothetical. The average dwell time for attackers — the period between initial compro...

In recent years, the number and sophistication of zero-day vulnerabilities have surged, posing a critical threat to organizations of all sizes. A zero-day vulnerability is a security flaw in software that is unknown to the vendor and remains unpatched at the time of discovery. Attackers exploit these flaws before any defensive measures can be implemented, making zero-days a potent weapon for cybercriminals. A recent example is, for instance, CVE-2024-0519 in Google Chrome: this high-severity vulnerability was actively exploited in the wild and involved an out-of-bounds memory access issue in the V8 JavaScript engine. It allowed remote attackers to access sensitive information or trigger a crash by exploiting heap corruption.  Also, the zero-day vulnerability at Rackspace caused massive trouble. This incident was a zero-day remote code execution vulnerability in ScienceLogic's monitoring application that led to the compromise of Rackspace's internal systems. The breach expose...

Did you know that Network Detection and Response (NDR) has become the most effective technology to detect cyber threats? In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false alerts and efficient threat response. Are you aware of  Network Detection and Response (NDR)  and how it’s become the most effective technology to detect cyber threats?  NDR massively upgrades your security through risk-based alerting, prioritizing alerts based on the potential risk to your organization's systems and data. How? Well, NDR's real-time analysis, machine learning, and threat intelligence provide immediate detection, reducing alert fatigue and enabling better decision-making. In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false positives and efficient threat response. Why Use Risk-Based Alerting? Risk-based alerting is an approach where security alerts and responses are prioritized based on the level of risk they pose to an organization's syst...

While IT security managers in companies and public administrations rely on the concept of Zero Trust, APTS (Advanced Persistent Threats) are putting its practical effectiveness to the test. Analysts, on the other hand, understand that Zero Trust can only be achieved with comprehensive insight into one's own network.  Just recently, an attack believed to be perpetrated by the Chinese hacker group  Storm-0558  targeted several government agencies. They used fake digital authentication tokens to access webmail accounts running on Microsoft's Outlook service. In this incident, the attackers stole a signing key from Microsoft, enabling them to issue functional access tokens for Outlook Web Access (OWA) and Outlook.com and to download emails and attachments. Due to a plausibility check error, the digital signature, which was only intended for private customer accounts (MSA), also worked in the Azure Active Directory for business customers.  Embracing the Zero Trust Revo...

Why Data Exfiltration Detection is Paramount? The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This article highlights this challenge and expounds on the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring to the table. Data exfiltration often serves as the final act of a cyberattack, making it the last window of opportunity to detect the breach before the data is made public or is used for other sinister activities, such as espionage. However, data leakage isn't only an aftermath of cyberattacks, it can also be a consequence of human error. While prevention of data exfiltration through security controls is ideal, the escalating complexity a...

Endpoint devices like desktops, laptops, and mobile phones enable users to connect to enterprise networks and use their resources for their day-to-day work. However, they also expand the attack surface and make the organisation vulnerable to malicious cyberattacks and data breaches. Why Modern Organisations Need EDR According to the 2020 global  risk report  by Ponemon Institute, smartphones, laptops, mobile devices, and desktops are some of the most vulnerable entry points that allow threat actors to compromise enterprise networks. Security teams must assess and address the security risks created by these devices before they can damage the organisation. And for this, they require Endpoint Detection & Response (EDR). EDR solutions provide real-time visibility into endpoints and detect threats like malware and ransomware. By continuously monitoring endpoints, they enable security teams to uncover malicious activities, investigate threats, and initiate appropriate respon...