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Showing posts with label Network Detection and Response. Show all posts
Network Security
AI cyberattacks AI Risks AI technology Cyber Security Cyberattacks Network Detection and Response Network Security

Network Detection and Response Defends Against AI Powered Cyber Attacks

 

Cybersecurity teams are facing growing pressure as attackers increasingly adopt artificial intelligence to accelerate, scale, and conceal malicious activity. Modern threat actors are no longer limited to static malware or simple intrusion techniques. Instead, AI-powered campaigns are using adaptive methods that blend into legitimate system behavior, making detection significantly more difficult and forcing defenders to rethink traditional security strategies. 

Threat intelligence research from major technology firms indicates that offensive uses of AI are expanding rapidly. Security teams have observed AI tools capable of bypassing established safeguards, automatically generating malicious scripts, and evading detection mechanisms with minimal human involvement. In some cases, AI-driven orchestration has been used to coordinate multiple malware components, allowing attackers to conduct reconnaissance, identify vulnerabilities, move laterally through networks, and extract sensitive data at machine speed. These automated operations can unfold faster than manual security workflows can reasonably respond. 

What distinguishes these attacks from earlier generations is not the underlying techniques, but the scale and efficiency at which they can be executed. Credential abuse, for example, is not new, but AI enables attackers to harvest and exploit credentials across large environments with only minimal input. Research published in mid-2025 highlighted dozens of ways autonomous AI agents could be deployed against enterprise systems, effectively expanding the attack surface beyond conventional trust boundaries and security assumptions. 

This evolving threat landscape has reinforced the relevance of zero trust principles, which assume no user, device, or connection should be trusted by default. However, zero trust alone is not sufficient. Security operations teams must also be able to detect abnormal behavior regardless of where it originates, especially as AI-driven attacks increasingly rely on legitimate tools and system processes to hide in plain sight. 

As a result, organizations are placing renewed emphasis on network detection and response technologies. Unlike legacy defenses that depend heavily on known signatures or manual investigation, modern NDR platforms continuously analyze network traffic to identify suspicious patterns and anomalous behavior in real time. This visibility allows security teams to spot rapid reconnaissance activity, unusual data movement, or unexpected protocol usage that may signal AI-assisted attacks. 

NDR systems also help security teams understand broader trends across enterprise and cloud environments. By comparing current activity against historical baselines, these tools can highlight deviations that would otherwise go unnoticed, such as sudden changes in encrypted traffic levels or new outbound connections from systems that rarely communicate externally. Capturing and storing this data enables deeper forensic analysis and supports long-term threat hunting. 

Crucially, NDR platforms use automation and behavioral analysis to classify activity as benign, suspicious, or malicious, reducing alert fatigue for security analysts. Even when traffic is encrypted, network-level context can reveal patterns consistent with abuse. As attackers increasingly rely on AI to mask their movements, the ability to rapidly triage and respond becomes essential.  

By delivering comprehensive network visibility and faster response capabilities, NDR solutions help organizations reduce risk, limit the impact of breaches, and prepare for a future where AI-driven threats continue to evolve.
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Windows
Adobe Flash Fileless Malware Attack malware NDR Network Detection and Response PowerShell Trojan Windows

Fileless Malware Attacks and How To Fight Them!



It has been crystal clear over these years with the increase in a number of cyber-attacks of an equally unique kind making it almost impossible for the out-dated or conventional security mechanisms to intercept and fight.

As if a single one-of-a-kind cyber-attack tool wasn’t enough, the threat actors now are laden with polymorphic tactics up their sleeves. Per sources, an entirely new version of a threat could be created every time after infection.

After "polymorphism" became apparent, the vendors as per reports engineered “generic signatures” had numerous variants in them. But the cyber-cons always managed to slip in a new kind.

This is when the malware authors came up with a concept of fileless attacking. They fabricated malware that didn’t need files to infect their targets and yet caused equal damage.

Per sources, the most common fileless attacks use applications, software, or authorized protocol that already exists on the target device. The first step is a user-initiated action, followed by getting access to the target’s device memory which has been infected by now. Here the malicious code is injected via the exploitation of Windows tools like Windows Management Instrumentation and PowerShell.

Per reports, the Modus Operandi of a fileless attack is as follows:
It begins with a spam message which doesn’t look suspicious at all and when the unaware user clicks on the link in it they are redirected to a malicious website.
The website kicks-off the Adobe Flash.
That initiates the PowerShell and Flash employs the command line to send it instructions and this takes place inside the target device’s memory.
The instructions are such that one of them launches a connection with a command and control server and helps download the malicious PowerShell script which ferrets down sensitive data and information only to exfiltrate it later.
Researchers note that as these attacks have absolutely nothing to do with stocking malicious files onto the target’s device, it becomes more difficult for security products to anticipate or perceive any such attack because they are evidently left with nothing to compare the attacks with. The fact that files less malware can hide from view in the legitimate tools and applications makes it all the worse.

Recently lots of fileless attacks surfaced and researchers were elbow deep in analyzing them. According to sources, some well-known corporate names that faced the attacks include, Equifax that had a data breach via a command injection vulnerability, the Union Crypto Trader faced a remote code execution in the memory, the version used was a 'trojanized' form a legitimate installer file and the U.S. Democratic National Committee faced two threat actors used a PowerShell backdoor to automatically launch malicious codes.

These attacks are obviously disconcerting and require a different kind of approach for their prediction or prevention. A conventional security system would never be the solution corporates and organizations need to stand against such attacks.

Per sources, the Network Detection and Response (NDR) seem to be a lucrative mechanism for detecting uncommon malicious activities. It doesn’t simply count on signatures but uses a combination of machine learning tactics to fetch out irregular network behaviors. It perceives what is normal in a particular system, then tries to comprehend what isn’t normal and alerts the overseers.

Researchers think an efficient NDR solution takes note of the entire surrounding of a device including what is in the network, cloud deployments, in the IoT sections and not to mention the data storage and email servers.

Per sources, NDR gradually works up to its highest efficiency. Its and its sensors’ deployment takes a considerable amount of time and monitoring. But the final results encompass enhanced productivity, decreased false alerts, and heightened security.

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