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February 10, 2025

From Hype to Reality: How AI is Transforming Cybersecurity Practices

AI hype is everywhere, but not many vendors are getting specific. Darktrace’s multi-layered AI combines various machine learning techniques for behavioral analytics, real-time threat detection, investigation, and autonomous response.
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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10
Feb 2025

AI is everywhere, predominantly because it has changed the way humans interact with data. AI is a powerful tool for data analytics, predictions, and recommendations, but accuracy, safety, and security are paramount for operationalization.

In cybersecurity, AI-powered solutions are becoming increasingly necessary to keep up with modern business complexity and this new age of cyber-threat, marked by attacker innovation, use of AI, speed, and scale. The emergence of these new threats calls for a varied and layered approach in AI security technology to anticipate asymmetric threats.

While many cybersecurity vendors are adding AI to their products, they are not always communicating the capabilities or data used clearly. This is especially the case with Large Language Models (LLMs). Many products are adding interactive and generative capabilities which do not necessarily increase the efficacy of detection and response but rather are aligned with enhancing the analyst and security team experience and data retrieval.

Consequently, many  people erroneously conflate generative AI with other types of AI. Similarly, only 31% of security professionals report that they are “very familiar” with supervised machine learning, the type of AI most often applied in today’s cybersecurity solutions to identify threats using attack artifacts and facilitate automated responses. This confusion around AI and its capabilities can result in suboptimal cybersecurity measures, overfitting, inaccuracies due to ineffective methods/data, inefficient use of resources, and heightened exposure to advanced cyber threats.

Vendors must cut through the AI market and demystify the technology in their products for safe, secure, and accurate adoption. To that end, let’s discuss common AI techniques in cybersecurity as well as how Darktrace applies them.

Modernizing cybersecurity with AI

Machine learning has presented a significant opportunity to the cybersecurity industry, and many vendors have been using it for years. Despite the high potential benefit of applying machine learning to cybersecurity, not every AI tool or machine learning model is equally effective due to its technique, application, and data it was trained on.

Supervised machine learning and cybersecurity

Supervised machine models are trained on labeled, structured data to facilitate automation of a human-led trained tasks. Some cybersecurity vendors have been experimenting with supervised machine learning for years, with most automating threat detection based on reported attack data using big data science, shared cyber-threat intelligence, known or reported attack behavior, and classifiers.

In the last several years, however, more vendors have expanded into the behavior analytics and anomaly detection side. In many applications, this method separates the learning, when the behavioral profile is created (baselining), from the subsequent anomaly detection. As such, it does not learn continuously and requires periodic updating and re-training to try to stay up to date with dynamic business operations and new attack techniques. Unfortunately, this opens the door for a high rate of daily false positives and false negatives.

Unsupervised machine learning and cybersecurity

Unlike supervised approaches, unsupervised machine learning does not require labeled training data or human-led training. Instead, it independently analyzes data to detect compelling patterns without relying on knowledge of past threats. This removes the dependency of human input or involvement to guide learning.

However, it is constrained by input parameters, requiring a thoughtful consideration of technique and feature selection to ensure the accuracy of the outputs. Additionally, while it can discover patterns in data as they are anomaly-focused, some of those patterns may be irrelevant and distracting.

When using models for behavior analytics and anomaly detection, the outputs come in the form of anomalies rather than classified threats, requiring additional modeling for threat behavior context and prioritization. Anomaly detection performed in isolation can render resource-wasting false positives.

LLMs and cybersecurity

LLMs are a major aspect of mainstream generative AI, and they can be used in both supervised and unsupervised ways. They are pre-trained on massive volumes of data and can be applied to human language, machine language, and more.

With the recent explosion of LLMs in the market, many vendors are rushing to add generative AI to their products, using it for chatbots, Retrieval-Augmented Generation (RAG) systems, agents, and embeddings. Generative AI in cybersecurity can optimize data retrieval for defenders, summarize reporting, or emulate sophisticated phishing attacks for preventative security.

But, since this is semantic analysis, LLMs can struggle with the reasoning necessary for security analysis and detection consistently. If not applied responsibly, generative AI can cause confusion by “hallucinating,” meaning referencing invented data, without additional post-processing to decrease the impact or by providing conflicting responses due to confirmation bias in the prompts written by different security team members.

Combining techniques in a multi-layered AI approach

Each type of machine learning technique has its own set of strengths and weaknesses, so a multi-layered, multi-method approach is ideal to enhance functionality while overcoming the shortcomings of any one method.

Darktrace’s Self-Learning AI is a multi-layered engine is powered by multiple machine learning approaches, which operate in combination for cyber defense. This allows Darktrace to protect the entire digital estates of the organizations it secures, including corporate networks, cloud computing services, SaaS applications, IoT, Industrial Control Systems (ICS), and email systems.

Plugged into the organization’s infrastructure and services, our AI engine ingests and analyzes the raw data and its interactions within the environment and forms an understanding of the normal behavior, right down to the granular details of specific users and devices. The system continually revises its understanding about what is normal based on evolving evidence, continuously learning as opposed to baselining techniques.

This dynamic understanding of normal partnered with dozens of anomaly detection models means that the AI engine can identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. Understanding anomalies through the lens of many models as well as autonomously fine-tuning the models’ performances gives us a higher understanding and confidence in anomaly detection.

The next layer provides event correlation and threat behavior context to understand the risk level of an anomalous event(s). Every anomalous event is investigated by Cyber AI Analyst that uses a combination of unsupervised machine learning models to analyze logs with supervised machine learning trained on how to investigate. This provides anomaly and risk context along with investigation outcomes with explainability.

The ability to identify activity that represents the first footprints of an attacker, without any prior knowledge or intelligence, lies at the heart of the AI system’s efficacy in keeping pace with threat actor innovations and changes in tactics and techniques. It helps the human team detect subtle indicators that can be hard to spot amid the immense noise of legitimate, day-to-day digital interactions. This enables advanced threat detection with full domain visibility.

Digging deeper into AI: Mapping specific machine learning techniques to cybersecurity functions

Visibility and control are vital for the practical adoption of AI solutions, as it builds trust between human security teams and their AI tools. That is why we want to share some specific applications of AI across our solutions, moving beyond hype and buzzwords to provide grounded, technical explanations.

Darktrace’s technology helps security teams cover every stage of the incident lifecycle with a range of comprehensive analysis and autonomous investigation and response capabilities.

  1. Behavioral prediction: Our AI understands your unique organization by learning normal patterns of life. It accomplishes this with multiple clustering algorithms, anomaly detection models, Bayesian meta-classifier for autonomous fine-tuning, graph theory, and more.
  2. Real-time threat detection: With a true understanding of normal, our AI engine connects anomalous events to risky behavior using probabilistic models. 
  3. Investigation: Darktrace performs in-depth analysis and investigation of anomalies, in particular automating Level 1 of a SOC team and augmenting the rest of the SOC team through prioritization for human-led investigations. Some of these methods include supervised and unsupervised machine learning models, semantic analysis models, and graph theory.
  4. Response: Darktrace calculates the proportional action to take in order to neutralize in-progress attacks at machine speed. As a result, organizations are protected 24/7, even when the human team is out of the office. Through understanding the normal pattern of life of an asset or peer group, the autonomous response engine can isolate the anomalous/risky behavior and surgically block. The autonomous response engine also has the capability to enforce the peer group’s pattern of life when rare and risky behavior continues.
  5. Customizable model editor: This layer of customizable logic models tailors our AI’s processing to give security teams more visibility as well as the opportunity to adapt outputs, therefore increasing explainability, interpretability, control, and the ability to modify the operationalization of the AI output with auditing.

See the complete AI architecture in the paper “The AI Arsenal: Understanding the Tools Shaping Cybersecurity.”

Figure 1. Alerts can be customized in the model editor in many ways like editing the thresholds for rarity and unusualness scores above.

Machine learning is the fundamental ally in cyber defense

Traditional security methods, even those that use a small subset of machine learning, are no longer sufficient, as these tools can neither keep up with all possible attack vectors nor respond fast enough to the variety of machine-speed attacks, given their complexity compared to known and expected patterns.

Security teams require advanced detection capabilities, using multiple machine learning techniques to understand the environment, filter the noise, and take action where threats are identified.

Darktrace’s Self-Learning AI comes together to achieve behavioral prediction, real-time threat detection and response, and incident investigation, all while empowering your security team with visibility and control.

Learn how AI is Applied in Cybersecurity

Discover specifically how Darktrace applies different types of AI to improve cybersecurity efficacy and operations in this technical paper.

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Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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March 11, 2026

NetSupport RAT: How Legitimate Tools Can Be as Damaging as Malware

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What is NetSupport Manager?

NetSupport Manager is a legitimate IT tool used by system administrators for remote support, monitoring, and management. In use since 1989, NetSupport Manager enables users to remotely access and navigate systems across different platforms and operating systems [1].

What is NetSupport RAT?

Although NetSupport Manager is a legitimate tool that can be used by IT and security professionals, there has been a rising number of cases in which it is abused to gain unauthorized access to victim systems. This misuse has become so prevalent that, in recent years, security researchers have begun referring to NetSupport as a Remote Access Trojan (RAT), a term typically used for malware that enables a threat actor to remotely access or control an infected device [2][3][4].

NetSupport RAT activity summary

The initial stages of NetSupport RAT infection may vary depending on the source of the initial compromise. Using tactics such as the social engineering tactic ClickFix, threat actors attempt to trick users into inadvertently executing malicious PowerShell commands under the guise of resolving a non-existent issue or completing a fake CAPTCHA verification [5]. Other attack vectors such as phishing emails, fake browser updates, malicious websites, search engine optimization (SEO) poisoning, malvertising and drive-by downloads are also employed to direct users to fraudulent pages and fake reCAPTCHA verification checks, ultimately inducing them to execute malicious PowerShell commands [5][6][7]. This leads to the successful installation of NetSupport Manager on the compromised device, which is often placed in non-standard directories such as AppData, ProgramData, or Downloads [3][8].

Once installed, the adversary is able to gain remote access to the affected machine, monitor user activity, exfiltrate data, communicate with the command-and-control (C2) server, and maintain persistence [5]. External research has also highlighted that post-exploitation of NetSupport RAT has involved the additional download of malicious payloads [2][5].

Attack flow diagram highlighting key events across each phase of the attack phase
Figure 1: Attack flow diagram highlighting key events across each phase of the attack phase [2][5].

Darktrace coverage

In November of 2025, suspicious behavior indicative of the malicious abuse of NetSupport Manager was observed on multiple customers across Europe, the Middle East, and Africa (EMEA) and the Americas (AMS).

While open-source intelligence (OSINT) has reported that, in a recent campaign, a threat actor impersonated government entities to trick users in organizations in the Information Technology, Government and Financial Services sectors in Central Asia into downloading NetSupport Manager [8], approximately a third of Darktrace’s affected customers in November were based in the US while the rest were based in EMEA. This contrast underscores how widely NetSupport Manager is leveraged by threat actors and highlights its accessibility as an initial access tool.  

The Darktrace customers affected were in sectors including Information and Communication, Manufacturing and Arts, entertainment and recreation.

The ClickFix social engineering tactic typically used to distribute the NetSupport RAT is known to target multiple industries, including Technology, Manufacturing and Energy sectors [9]. It also reflects activity observed in the campaign targeting Central Asia, where the Information Technology sector was among those affected [8].

The prevalence of affected Education customers highlights NetSupport’s marketing focus on the Education sector [10]. This suggests that threat actors are also aware of this marketing strategy and have exploited the trust it creates to deploy NetSupport Manager and gain access to their targets’ systems. While the execution of the PowerShell commands that led to the installation of NetSupport Manager falls outside of Darktrace's purview in cases identified, Darktrace was still able to identify a pattern of devices making connections to multiple rare external domains and IP addresses associated with the NetSupport RAT, using a wide range of ports over the HTTP protocol. A full list of associated domains and IP addresses is provided in the Appendices of this blog.

Although OSINT identifies multiple malicious domains and IP addresses as used as C2 servers, signature-based detections of NetSupport RAT indicators of compromise (IoCs) may miss broader activity, as new malicious websites linked to the RAT continue to appear.

Darktrace’s anomaly‑based approach allows it to establish a normal ‘pattern of life’ for each device on a network and identify when behavior deviates from this baseline, enabling the detection of unusual activity even when it does not match known IoCs or tactics, techniques and procedures (TTPs).

In one customer environment in late 2025, Darktrace / NETWORK detected a device initiating new connections to the rare external endpoint, thetavaluemetrics[.]com (74.91.125[.]57), along with the use of a previously unseen user agent, which it recognized as highly unusual for the network.

Darktrace’s detection of HTTP POST requests to a suspicious URI and new user agent usage.
Figure 2: Darktrace’s detection of HTTP POST requests to a suspicious URI and new user agent usage.

Darktrace identified that user agent present in connections to this endpoint was the ‘NetSupport Manager/1.3’, initially suggesting legitimate NetSupport Manager activity. Subsequent investigation, however, revealed that the endpoint was in fact a malicious NetSupportRAT C2 endpoint [12]. Shortly after, Darktrace detected the same device performing HTTP POST requests to the URI fakeurl[.]htm. This pattern of activity is consistent with OSINT reporting that details communication between compromised devices and NetSupport Connectivity Gateways functioning as C2 servers [11].

Conclusion

As seen not only with NetSupport Manager but with any legitimate or open‑source software used by IT and security professionals, the legitimacy of a tool does not prevent it from being abused by threat actors. Open‑source software, especially tools with free or trial versions such as NetSupport Manager, remains readily accessible for malicious use, including network compromise. In an age where remote work is still prevalent, validating any anomalous use of software and remote management tools is essential to reducing opportunities for unauthorized access.

Darktrace’s anomaly‑based detection enables security teams to identify malicious use of legitimate tools, even when clear signatures or indicators of compromise are absent, helping to prevent further impact on a network.


Credit to George Kim (Analyst Consulting Lead – AMS), Anna Gilbertson (Senior Cyber Analyst)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Alerts

·       Compromise / Suspicious HTTP and Anomalous Activity

·       Compromise / New User Agent and POST

·       Device / New User Agent

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / Posting HTTP to IP Without Hostname

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·       Anomalous Connection / Application Protocol on Uncommon Port

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

·       Compromise / Beaconing Activity To External Rare

·       Compromise / HTTP Beaconing to Rare Destination

·       Compromise / Agent Beacon (Medium Period)

·       Compromise / Agent Beacon (Long Period)

·       Compromise / Quick and Regular Windows HTTP Beaconing

·       Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

·       Compromise / POST and Beacon to Rare External

Indicators of Compromise (IoCs)

Indicator           Type     Description

/fakeurl.htm URI            NetSupportRAT C2 URI

thetavaluemetrics[.]com        Connection hostname              NetSupportRAT C2 Endpoint

westford-systems[.]icu            Connection hostname              NetSupportRAT C2 Endpoint

holonisz[.]com                Connection hostname              NetSupportRAT C2 Endpoint

heaveydutyl[.]com      Connection hostname              NetSupportRAT C2 Endpoint

nsgatetest1[.]digital   Connection hostname              NetSupportRAT C2 Endpoint

finalnovel[.]com            Connection hostname              NetSupportRAT C2 Endpoint

217.91.235[.]17              IP             NetSupportRAT C2 Endpoint

45.94.47[.]224                 IP             NetSupportRAT C2 Endpoint

74.91.125[.]57                 IP             NetSupportRAT C2 Endpoint

88.214.27[.]48                 IP             NetSupportRAT C2 Endpoint

104.21.40[.]75                 IP             NetSupportRAT C2 Endpoint

38.146.28[.]242              IP             NetSupportRAT C2 Endpoint

185.39.19[.]233              IP             NetSupportRAT C2 Endpoint

45.88.79[.]237                 IP             NetSupportRAT C2 Endpoint

141.98.11[.]224              IP             NetSupportRAT C2 Endpoint

88.214.27[.]166              IP             NetSupportRAT C2 Endpoint

107.158.128[.]84          IP             NetSupportRAT C2 Endpoint

87.120.93[.]98                 IP             Rhadamanthys C2 Endpoint

References

  1. https://mspalliance.com/netsupport-debuts-netsupport-24-7/
  2. https://blogs.vmware.com/security/2023/11/netsupport-rat-the-rat-king-returns.html
  3. https://redcanary.com/threat-detection-report/threats/netsupport-manager/
  4. https://www.elastic.co/guide/en/security/8.19/netsupport-manager-execution-from-an-unusual-path.html
  5. https://rewterz.com/threat-advisory/netsupport-rat-delivered-through-spoofed-verification-pages-active-iocs
  6. https://thehackernews.com/2025/11/new-evalusion-clickfix-campaign.html
  7. https://corelight.com/blog/detecting-netsupport-manager-abuse
  8. https://thehackernews.com/2025/11/bloody-wolf-expands-java-based.html
  9. https://unit42.paloaltonetworks.com/preventing-clickfix-attack-vector
  10. https://www.netsupportsoftware.com/education-solutions
  11. https://www.esentire.com/blog/unpacking-netsupport-rat-loaders-delivered-via-clickfix
  12. https://threatfox.abuse.ch/browse/malware/win.netsupportmanager_rat/
  13. https://www.virustotal.com/gui/url/5fe6936a69c786c9ded9f31ed1242c601cd64e1d90cecd8a7bb03182c47906c2

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About the author
George Kim
Analyst Consulting Lead – AMS

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March 5, 2026

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

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Investigating Cloud Attacks with Forensic Acquisition & Investigation

Darktrace / Forensic Acquisition & Investigation™ is the industry’s first truly automated forensic solution purpose-built for the cloud. This blog will demonstrate how an investigation can be carried out against a compromised cloud server in minutes, rather than hours or days.

The compromised server investigated in this case originates from Darktrace’s Cloudypots system, a global honeypot network designed to observe adversary activity in real time across a wide range of cloud services. Whenever an attacker successfully compromises one of these honeypots, a forensic copy of the virtual server's disk is preserved for later analysis. Using Forensic Acquisition & Investigation, analysts can then investigate further and obtain detailed insights into the compromise including complete attacker timelines and root cause analysis.

Forensic Acquisition & Investigation supports importing artifacts from a variety of sources, including EC2 instances, ECS, S3 buckets, and more. The Cloudypots system produces a raw disk image whenever an attack is detected and stores it in an S3 bucket. This allows the image to be directly imported into Forensic Acquisition & Investigation using the S3 bucket import option.

As Forensic Acquisition & Investigation runs cloud-natively, no additional configuration is required to add a specific S3 bucket. Analysts can browse and acquire forensic assets from any bucket that the configured IAM role is permitted to access. Operators can also add additional IAM credentials, including those from other cloud providers, to extend access across multiple cloud accounts and environments.

Figure 1: Forensic Acquisition & Investigation import screen.

Forensic Acquisition & Investigation then retrieves a copy of the file and automatically begins running the analysis pipeline on the artifact. This pipeline performs a full forensic analysis of the disk and builds a timeline of the activity that took place on the compromised asset. By leveraging Forensic Acquisition & Investigation’s cloud-native analysis system, this process condenses hour of manual work into just minutes.

Successful import of a forensic artifact and initiation of the analysis pipeline.
Figure 2: Successful import of a forensic artifact and initiation of the analysis pipeline.

Once processing is complete, the preserved artifact is visible in the Evidence tab, along with a summary of key information obtained during analysis, such as the compromised asset’s hostname, operating system, cloud provider, and key event count.

The Evidence overview showing the acquired disk image.
Figure 3: The Evidence overview showing the acquired disk image.

Clicking on the “Key events” field in the listing opens the timeline view, automatically filtered to show system- generated alarms.

The timeline provides a chronological record of every event that occurred on the system, derived from multiple sources, including:

  • Parsed log files such as the systemd journal, audit logs, application specific logs, and others.
  • Parsed history files such as .bash_history, allowing executed commands to be shown on the timeline.
  • File-specific events, such as files being created, accessed, modified, or executables being run, etc.

This approach allows timestamped information and events from multiple sources to be aggregated and parsed into a single, concise view, greatly simplifying the data review process.

Alarms are created for specific timeline events that match either a built-in system rule, curated by Darktrace’s Threat Research team or an operator-defined created at the project level. These alarms help quickly filter out noise and highlight on events of interest, such as the creation of a file containing known malware, access to sensitive files like Amazon Web Service (AWS) credentials, suspicious arguments or commands, and more.

 The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.
Figure 4: The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.

In this case, several alarms were generated for suspicious Base64 arguments being passed to Selenium. Examining the event data, it appears the attacker spawned a Selenium Grid session with the following payload:

"request.payload": "[Capabilities {browserName: chrome, goog:chromeOptions: {args: [-cimport base64;exec(base64...], binary: /usr/bin/python3, extensions: []}, pageLoadStrategy: normal}]"

This is a common attack vector for Selenium Grid. The chromeOptions object is intended to specify arguments for how Google Chrome should be launched; however, in this case the attacker has abused the binary field to execute the Python3 binary instead of Chrome. Combined with the option to specify command-line arguments, the attacker can use Python3’s -c option to execute arbitrary Python code, in this instance, decoding and executing a Base64 payload.

Selenium’s logs truncate the Arguments field automatically, so an alternate method is required to retrieve the full payload. To do this, the search bar can be used to find all events that occurred around the same time as this flagged event.

Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].
Figure 5: Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].

Scrolling through the search results, an entry from Java’s systemd journal can be identified. This log contains the full, unaltered payload. GCHQ’s CyberChef can then be used to decode the Base64 data into the attacker’s script, which will ultimately be executed.[NJ9]

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About the author
Nathaniel Bill
Malware Research Engineer
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