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.

Written by

Nicole Carignan

SVP, Security & AI Strategy, Field CISO

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.

Written by

Nicole Carignan

SVP, Security & AI Strategy, Field CISO

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 multi-layered AI 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.

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.‍
Real-time threat detection: With a true understanding of normal, our AI engine connects anomalous events to risky behavior using probabilistic models.
‍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.‍
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.‍
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 multi-layered 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.

Download the full report

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

‍

Written by

Nicole Carignan

SVP, Security & AI Strategy, Field CISO

Inside the SOC

Written by

Nicole Carignan

SVP, Security & AI Strategy, Field CISO

Product Marketing Manager, OT Security & Compliance

Watch the NIS2 Webinar

Blog

May 16, 2025

Catching a RAT: How Darktrace neutralized AsyncRAT

What is a RAT?

As the proliferation of new and more advanced cyber threats continues, the Remote Access Trojan (RAT) remains a classic tool in a threat actor's arsenal. RATs, whether standardized or custom-built, enable attackers to remotely control compromised devices, facilitating a range of malicious activities.

What is AsyncRAT?

Since its first appearance in 2019, AsyncRAT has become increasingly popular among a wide range of threat actors, including cybercriminals and advanced persistent threat (APT) groups.

Originally available on GitHub as a legitimate tool, its open-source nature has led to widespread exploitation. AsyncRAT has been used in numerous campaigns, including prolonged attacks on essential US infrastructure, and has even reportedly penetrated the Chinese cybercriminal underground market [1] [2].

How does AsyncRAT work?

Original source code analysis of AsyncRAT demonstrates that once installed, it establishes persistence via techniques such as creating scheduled tasks or registry keys and uses SeDebugPrivilege to gain elevated privileges [3].

Its key features include:

Keylogging
File search
Remote audio and camera access
Exfiltration techniques
Staging for final payload delivery

These are generally typical functions found in traditional RATs. However, it also boasts interesting anti-detection capabilities. Due to the popularity of Virtual Machines (VM) and sandboxes for dynamic analysis, this RAT checks for the manufacturer via the WMI query 'Select * from Win32_ComputerSystem' and looks for strings containing 'VMware' and 'VirtualBox' [4].

Darktrace’s coverage of AsyncRAT

In late 2024 and early 2025, Darktrace observed a spike in AsyncRAT activity across various customer environments. Multiple indicators of post-compromise were detected, including devices attempting or successfully connecting to endpoints associated with AsyncRAT.

On several occasions, Darktrace identified a clear association with AsyncRAT through the digital certificates of the highlighted SSL endpoints. Darktrace’s Real-time Detection effectively identified and alerted on suspicious activities related to AsyncRAT. In one notable incident, Darktrace’s Autonomous Response promptly took action to contain the emerging threat posed by AsyncRAT.

AsyncRAT attack overview

On December 20, 2024, Darktrace first identified the use of AsyncRAT, noting a device successfully establishing SSL connections to the uncommon external IP 185.49.126[.]50 (AS199654 Oxide Group Limited) via port 6606. The IP address appears to be associated with AsyncRAT as flagged by open-source intelligence (OSINT) sources [5]. This activity triggered the device to alert the ‘Anomalous Connection / Rare External SSL Self-Signed' model.

Figure 1: Model alert in Darktrace / NETWORK showing the repeated SSL connections to a rare external Self-Signed endpoint, 185.49.126[.]50.

Following these initial connections, the device was observed making a significantly higher number of connections to the same endpoint 185.49.126[.]50 via port 6606 over an extended period. This pattern suggested beaconing activity and triggered the 'Compromise/Beaconing Activity to External Rare' model alert.

Further analysis of the original source code, available publicly, outlines the default ports used by AsyncRAT clients for command-and-control (C2) communications [6]. It reveals that port 6606 is the default port for creating a new AsyncRAT client. Darktrace identified both the Certificate Issuer and the Certificate Subject as "CN=AsyncRAT Server". This SSL certificate encrypts the packets between the compromised system and the server. These indicators of compromise (IoCs) detected by Darktrace further suggest that the device was successfully connecting to a server associated with AsyncRAT.

Figure 2: Model alert in Darktrace / NETWORK displaying the Digital Certificate attributes, IP address and port number associated with AsyncRAT.

Figure 3: Darktrace’s detection of repeated connections to the suspicious IP address 185.49.126[.]50 over port 6606, indicative of beaconing behavior.

Figure 4: Darktrace's Autonomous Response actions blocking the suspicious IP address,185.49.126[.]50.

A few days later, the same device was detected making numerous connections to a different IP address, 195.26.255[.]81 (AS40021 NL-811-40021), via various ports including 2106, 6606, 7707, and 8808. Notably, ports 7707 and 8808 are also default ports specified in the original AsyncRAT source code [6].

Figure 5: Darktrace’s detection of connections to the suspicious endpoint 195.26.255[.]81, where the default ports (6606, 7707, and 8808) for AsyncRAT were observed.

Similar to the activity observed with the first endpoint, 185.49.126[.]50, the Certificate Issuer for the connections to 195.26.255[.]81 was identified as "CN=AsyncRAT Server". Further OSINT investigation confirmed associations between the IP address 195.26.255[.]81 and AsyncRAT [7].

Figure 6: Darktrace's detection of a connection to the suspicious IP address 195.26.255[.]81 and the domain name identified under the common name (CN) of a certificate as AsyncRAT Server.

Once again, Darktrace's Autonomous Response acted swiftly, blocking the connections to 195.26.255[.]81 throughout the observed AsyncRAT activity.

Figure 7: Darktrace's Autonomous Response actions were applied against the suspicious IP address 195.26.255[.]81.

A day later, Darktrace again alerted to further suspicious activity from the device. This time, connections to the suspicious endpoint 'kashuub[.]com' and IP address 191.96.207[.]246 via port 8041 were observed. Further analysis of port 8041 suggests it is commonly associated with ScreenConnect or Xcorpeon ASIC Carrier Ethernet Transport [8]. ScreenConnect has been observed in recent campaign’s where AsyncRAT has been utilized [9]. Additionally, one of the ASN’s observed, namely ‘ASN Oxide Group Limited’, was seen in both connections to kashuub[.]com and 185.49.126[.]50.

This could suggest a parallel between the two endpoints, indicating they might be hosting AsyncRAT C2 servers, as inferred from our previous analysis of the endpoint 185.49.126[.]50 and its association with AsyncRAT [5]. OSINT reporting suggests that the “kashuub[.]com” endpoint may be associated with ScreenConnect scam domains, further supporting the assumption that the endpoint could be a C2 server.

Darktrace’s Autonomous Response technology was once again able to support the customer here, blocking connections to “kashuub[.]com”. Ultimately, this intervention halted the compromise and prevented the attack from escalating or any sensitive data from being exfiltrated from the customer’s network into the hands of the threat actors.

Figure 8: Darktrace’s Autonomous Response applied a total of nine actions against the IP address 191.96.207[.]246 and the domain 'kashuub[.]com', successfully blocking the connections.

Due to the popularity of this RAT, it is difficult to determine the motive behind the attack; however, from existing knowledge of what the RAT does, we can assume accessing and exfiltrating sensitive customer data may have been a factor.

Conclusion

While some cybercriminals seek stability and simplicity, openly available RATs like AsyncRAT provide the infrastructure and open the door for even the most amateur threat actors to compromise sensitive networks. As the cyber landscape continually shifts, RATs are now being used in all types of attacks.

Darktrace’s suite of AI-driven tools provides organizations with the infrastructure to achieve complete visibility and control over emerging threats within their network environment. Although AsyncRAT’s lack of concealment allowed Darktrace to quickly detect the developing threat and alert on unusual behaviors, it was ultimately Darktrace Autonomous Response's consistent blocking of suspicious connections that prevented a more disruptive attack.

Credit to Isabel Evans (Cyber Analyst), Priya Thapa (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

‍

Appendices

Real-time Detection Models
- Compromise / Suspicious SSL Activity
- Compromise / Beaconing Activity To External Rare
- Compromise / High Volume of Connections with Beacon Score
- Anomalous Connection / Suspicious Self-Signed SSL
- Compromise / Sustained SSL or HTTP Increase
- Compromise / SSL Beaconing to Rare Destination
- Compromise / Suspicious Beaconing Behaviour
- Compromise / Large Number of Suspicious Failed Connections
Autonomous Response Models
- Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert
- Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

List of IoCs

· 185.49.126[.]50 - IP – AsyncRAT C2 Endpoint

· 195.26.255[.]81 – IP - AsyncRAT C2 Endpoint

· 191.96.207[.]246 – IP – Likely AsyncRAT C2 Endpoint

· CN=AsyncRAT Server - SSL certificate - AsyncRATC2 Infrastructure

· Kashuub[.]com– Hostname – Likely AsyncRAT C2 Endpoint

‍

MITRE ATT&CK Mapping:

Tactic –Technique – Sub-Technique

Execution– T1053 - Scheduled Task/Job: Scheduled Task

DefenceEvasion – T1497 - Virtualization/Sandbox Evasion: System Checks

Discovery– T1057 – Process Discovery

Discovery– T1082 – System Information Discovery

LateralMovement - T1021.001 - Remote Services: Remote Desktop Protocol

Collection/ Credential Access – T1056 – Input Capture: Keylogging

Collection– T1125 – Video Capture

Commandand Control – T1105 - Ingress Tool Transfer

Commandand Control – T1219 - Remote Access Software

Exfiltration– T1041 - Exfiltration Over C2 Channel

References

[1] https://blog.talosintelligence.com/operation-layover-how-we-tracked-attack/

[2] https://intel471.com/blog/china-cybercrime-undergrond-deepmix-tea-horse-road-great-firewall

[3] https://www.attackiq.com/2024/08/01/emulate-asyncrat/

[4] https://www.fortinet.com/blog/threat-research/spear-phishing-campaign-with-new-techniques-aimed-at-aviation-companies

[5] https://www.virustotal.com/gui/ip-address/185.49.126[.]50/community

[6] https://dfir.ch/posts/asyncrat_quasarrat/

[7] https://www.virustotal.com/gui/ip-address/195.26.255[.]81

[8] https://www.speedguide.net/port.php?port=8041

[9] https://www.esentire.com/blog/exploring-the-infection-chain-screenconnects-link-to-asyncrat-deployment

[10] https://scammer.info/t/taking-out-connectwise-sites/153479/518?page=26

About the author

Isabel Evans

Cyber Analyst

Blog

May 13, 2025

Revolutionizing OT Risk Prioritization with Darktrace 6.3

Powering smarter protection for industrial systems

In industrial environments, security challenges are deeply operational. Whether you’re running a manufacturing line, a power grid, or a semiconductor fabrication facility (fab), you need to know: What risks can truly disrupt my operations, and what should I focus on first?

Teams need the right tools to shift from reactive defense, constantly putting out fires, to proactively thinking about their security posture. However, most OT teams are stuck using IT-centric tools that don’t speak the language of industrial systems, are consistently overwhelmed with static CVE lists, and offer no understanding of OT-specific protocols. The result? Compliance gaps, siloed insights, and risk models that don’t reflect real-world exposure, making risk prioritization seem like a luxury.

Darktrace / OT 6.3 was built in direct response to these challenges. Developed in close collaboration with OT operators and engineers, this release introduces powerful upgrades that deliver the context, visibility, and automation security teams need, without adding complexity. It’s everything OT defenders need to protect critical operations in one platform that understands the language of industrial systems.

Contextual risk modeling with smarter Risk Scoring

Darktrace / OT 6.3 introduces major upgrades to OT Risk Management, helping teams move beyond generic CVE lists with AI-driven risk scoring and attack path modeling.

By factoring in real-world exploitability, asset criticality, and operational context, this release delivers a more accurate view of what truly puts critical systems at risk.

The platform now integrates:

CISA’s Known Exploited Vulnerabilities (KEV) database
End-of-life status for legacy OT devices
Firewall misconfiguration analysis
Incident response plan alignment

Most OT environments are flooded with vulnerability data that lacks context. CVE scores often misrepresent risk by ignoring how threats move through the environment or whether assets are even reachable. Firewalls are frequently misconfigured or undocumented, and EOL (End of Life) devices, some of the most vulnerable, often go untracked.

Legacy tools treat these inputs in isolation. Darktrace unifies them, showing teams exactly which attack paths adversaries could exploit, mapped to the MITRE ATT&CK framework, with visibility into where legacy tech increases exposure.

The result: teams can finally focus on the risks that matter most to uptime, safety, and resilience without wasting resources on noise.

Automating compliance with dynamic IEC-62443 reporting

Darktrace / OT now includes a purpose-built IEC-62443-3-3 compliance module, giving industrial teams real-time visibility into their alignment with regulatory standards. No spreadsheets required!

Industrial environments are among the most heavily regulated. However, for many OT teams, staying compliant is still a manual, time-consuming process.

Darktrace / OT introduces a dedicated IEC-62443-3-3 module designed specifically for industrial environments. Security and operations teams can now map their security posture to IEC standards in real time, directly within the platform. The module automatically gathers evidence across all four security levels, flags non-compliance, and generates structured reports to support audit preparation, all in just a few clicks.Most organizations rely on spreadsheets or static tools to track compliance, without clear visibility into which controls meet standards like IEC-62443. The result is hidden gaps, resource-heavy audits, and slow remediation cycles.

Even dedicated compliance tools are often built for IT, require complex setup, and overlook the unique devices found in OT environments. This leaves teams stuck with fragmented reporting and limited assurance that their controls are actually aligned with regulatory expectations.

By automating compliance tracking, surfacing what matters most, and being purpose built for industrial environments, Darktrace / OT empowers organizations to reduce audit fatigue, eliminate blind spots, and focus resources where they’re needed most.

Expanding protocol visibility with deep insights for specialized OT operations

Darktrace has expanded its Deep Packet Inspection (DPI) capabilities to support five industry-specific protocols, across healthcare, semiconductor manufacturing, and ABB control systems.

The new protocols build on existing capabilities across all OT industry verticals and protocol types to ensure the Darktrace Self-Learning AI ^TM can learn intelligently about even more assets in complex industrial environments. By enabling native, AI-driven inspection of these protocols, Darktrace can identify both security threats and operational issues without relying on additional appliances or complex integrations.

Most security platforms lack native support for industry-specific protocols, creating critical visibility gaps in customer environments like healthcare, semiconductor manufacturing, and ABB-heavy industrial automation. Without deep protocol awareness, organizations struggle to accurately identify specialized OT and IoT assets, detect malicious activity concealed within proprietary protocol traffic, and generate reliable device risk profiles due to insufficient telemetry.

These blind spots result in incomplete asset inventories, and ultimately, flawed risk posture assessments which over-index for CVE patching and legacy equipment.

By combining protocol-aware detection with full-stack visibility across IT, OT, and IoT, Darktrace’s AI can correlate anomalies across domains. For example, connecting an anomaly from a Medical IoT (MIoT) device with suspicious behavior in IT systems, providing actionable, contextual insights other solutions often miss.

Conclusion

Together, these capabilities take OT security beyond alert noise and basic CVE matching, delivering continuous compliance, protocol-aware visibility, and actionable, prioritized risk insights, all inside a single, unified platform built for the realities of industrial environments.

[related-resource]

About the author

Pallavi Singh

Product Marketing Manager, OT Security & Compliance

Your data. Our AI.

Elevate your network security with Darktrace AI

Get a demo

Check out this article by Darktrace: From Hype to Reality: How AI is Transforming Cybersecurity Practices

From Hype to Reality: How AI is Transforming Cybersecurity Practices

Modernizing cybersecurity with AI

Supervised machine learning and cybersecurity

Unsupervised machine learning and cybersecurity

LLMs and cybersecurity

Combining techniques in a multi-layered AI approach

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

Machine learning is the fundamental ally in cyber defense

Download the full report

Catching a RAT: How Darktrace neutralized AsyncRAT

Revolutionizing OT Risk Prioritization with Darktrace 6.3

Enjoying the blog?

More in this series

Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)

AI Uncovered: Introducing Darktrace Incident Graph Evaluation for Security Threats (DIGEST)

Force Multiply Your Security Team with Agentic AI: How the Industry’s Only True Cyber AI Analyst™ Saves Time and Stop Threats

Blog

May 16, 2025

Catching a RAT: How Darktrace neutralized AsyncRAT

What is a RAT?

What is AsyncRAT?

How does AsyncRAT work?

Darktrace’s coverage of AsyncRAT

AsyncRAT attack overview

Conclusion

Appendices

Blog

May 13, 2025

Revolutionizing OT Risk Prioritization with Darktrace 6.3

Powering smarter protection for industrial systems

Contextual risk modeling with smarter Risk Scoring

Automating compliance with dynamic IEC-62443 reporting

Expanding protocol visibility with deep insights for specialized OT operations

Conclusion