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June 3, 2024

The Price of Admission: Countering Stolen Credentials with Darktrace

This blog examines a network compromise that stemmed from the purchase of leaked credentials from the dark web. Credentials purchased from dark web marketplaces allow unauthorized access to internal systems. Such access can be used to exfiltrate data, disrupt operations, or deploy malware.
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
Charlotte Thompson
Cyber Analyst
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03
Jun 2024

Using leaked credentials to gain unauthorized access

Dark web marketplaces selling sensitive data have increased accessibility for malicious actors, similar to Ransomware-as-a-Service (RaaS), lowering the barrier to entry usually associated with malicious activity. By utilizing leaked credentials, malicious actors can easily gain unauthorized access to accounts and systems which they can leverage to carry out malicious activities like data exfiltration or malware deployment.

Usage of leaked credentials by malicious actors is a persistent concern for both organizations and security providers. Google Cloud’s ‘H1 2024 Threat Horizons Report’ details that initial access seen in 2.9% of cloud compromises observed on Google Cloud resulted from leaked credential usage [1], with the ‘IBM X-Force Threat Intelligence Index 2024’ reporting 71% year-on-year increase in cyber-attacks which utilize stolen or compromised credentials [2].

Darktrace coverage of leaked credentials

In early 2024, one Darktrace customer was compromised by a malicious actor after their internal credentials had been leaked on the dark web. Subsequent attack phases were detected by Darktrace/Network and the customer was alerted to the suspicious activity via the Proactive Threat Notification (PTN) service, following an investigation by Darktrace’s Security Operation Center (SOC).

Darktrace detected a device on the network of a customer in the US carrying out a string of anomalous activity indicative of network compromise. The device was observed using a new service account to authenticate to a Virtual Private Network (VPN) server, before proceeding to perform a range of suspicious activity including internal reconnaissance and lateral movement.

Malicious actors seemingly gained access to a previously unused service account for which they were able to set up multi-factor authentication (MFA) to access the VPN. As this MFA setup was made possible by the configuration of the customer’s managed service provider (MSP), the initial access phase of the attack fell outside of Darktrace’s purview.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled on the network at the time of the attack. Had RESPOND been active, it would have been able to autonomously act against the malicious activity by disabling users, strategically blocking suspicious connections and limiting devices to their expected patterns of activity.

Attack timeline of leaked credentials spotted by darktrace

Network Scanning Activity

On February 22, 2024, Darktrace detected the affected device performing activity indicative of network scanning, namely initiating connections on multiple ports, including ports 80, 161 389 and 445, to other internal devices. While many of these internal connection attempts were unsuccessful, some successful connections were observed.

Devices on a network can gather information about other internal devices by performing network scanning activity. Defensive scanning can be used to support network security, allowing internal security teams to discover vulnerabilities and potential entry points that require their attention, however attackers are also able to take advantage of such information, such as open ports and services available on internal devices, with offensive scanning.

Brute Force Login Attempts

Darktrace proceeded to identify the malicious actor attempting to access a previously unused service account for which they were able to successfully establish MFA to access the organization’s VPN. As the customer’s third-party MSP had been configured to allow all users to login to the organization’s VPN using MFA, this login was successful. Moreover, the service account had never previously been used and MFA and never been established, allowing the attacker to leverage it for their own nefarious means.

Darktrace/Network identified the attacker attempting to authenticate over the Kerberos protocol using a total of 30 different usernames, of which two were observed successfully authenticating. There was a total of 6 successful Kerberos logins identified from two different credentials.  Darktrace also observed over 100 successful NTLM attempts from the same device for multiple usernames including “Administrator” and “mail”. These credentials were later confirmed by the customer to have been stolen and leaked on the dark web.

Advanced Search query results showing the usernames that successfully authenticated via NTLM.
Figure 1: Advanced Search query results showing the usernames that successfully authenticated via NTLM.

Even though MFA requirements had been satisfied when the threat actor accessed the organization’s VPN, Darktrace recognized that this activity represented a deviation from its previously learned behavior.

Malicious actors frequently attempt to gain unauthorized access to accounts and internal systems by performing login attempts using multiple possible usernames and passwords. This type of brute-force activity is typically accomplished using computational power via the use of software or scripts to attempt different username/password combinations until one is successful.

By purchasing stolen credentials from dark web marketplaces, attackers are able to significantly increase the success rate of brute-force attacks and, if they do gain access, they can easily act on their objectives, be that exfiltrating sensitive data or moving through their target networks to further the compromise.

Share Enumeration

Around 30 minutes after the initial network scanning activity, the compromised device was observed performing SMB enumeration using one of the aforementioned accounts. Darktrace understood that this activity was suspicious as the device had never previously been used to perform SMB activity and had not been tagged as a security device.

Darktrace/Network identifying the suspicious SMB enumeration performed by the compromised device.
Figure 2: Darktrace/Network identifying the suspicious SMB enumeration performed by the compromised device.

Such enumeration can be used by malicious actors to gain insights into the structures and configurations of a target device, view permissions associated with shared resources, and also view general identifying information about the system.

Darktrace further identified that the device connected to the named pipe “srvsvc”. By enumerating over srvsvc, a threat actor is able to request a list of all available SMB shares on a destination device, enabling further data gathering as part of network reconnaissance. Srvsvc also provides access to remote procedure call (RPC) for various services on a destination device.

At this stage, a Darktrace/Network Enhanced Monitoring model was triggered for lateral movement activity taking place on the customer’s network. As this particular customer was subscribed to the PTN service, the Enhanced Monitoring model alert was promptly triaged and investigated by the Darktrace SOC. The customer was alerted to the emerging activity and given full details of the incident and the SOC team’s investigation.

Attack and Reconnaissance Tool Usage

A few minutes later, Darktrace observed the device making a connection with a user agent associated with the Nmap network scanning tool, “Mozilla/5.0 (compatible; Nmap Scripting Engine; https://nmap.org/book/nse[.]html)”. While these tools are often used legitimately by an organization’s security team, they can also be used maliciously by attackers to exploit vulnerabilities that attackers may have unearthed during earlier reconnaissance activity.

As such services are often seen as normal network traffic, attackers can often use them to bypass traditional security measures. Darktrace’s Self-Learning AI, however, was able to recognize that the affected device was not a security device and therefore not expected to carry out such activity, even if it was using a legitimate Nmap service.

Darktrace/Network identifying the compromised device using the Nmap scanning tool.
Figure 3: Darktrace/Network identifying the compromised device using the Nmap scanning tool.

Further Lateral Movement

Following this suspicious Nmap usage, Darktrace observed a range of additional anomalous SMB activity from the aforementioned compromised account. The affected device attempted to establish almost 900 SMB sessions, as well as performing 65 unusual file reads from 29 different internal devices and over 300 file deletes for the file “delete.me” from over 100 devices using multiple paths, including ADMIN$, C$, print$.

Darktrace also observed the device making several DCE-RPC connections associated with Active Directory Domain enumeration, including DRSCrackNames and DRSGetNCChanges; a total of more than 1000 successful DCE-RPC connection were observed to a domain controller.

As this customer did not have Darktrace/Network's autonomous response deployed on their network, the above detailed lateral movement and network reconnaissance activity was allowed to progress unfettered, until Darktrace’s SOC alerted the customer’s security team to take urgent action. The customer also received follow-up support through Darktrace’s Ask the Expert (ATE) service, allowing them to contact the analyst team directly for further details and support on the incident.

Thanks to this early detection, the customer was able to quickly identify and disable affected user accounts, effectively halting the attack and preventing further escalation.

Conclusions

Given the increasing trend of ransomware attackers exfiltrating sensitive data for double extortion and the rise of information stealers, stolen credentials are commonplace across dark web marketplaces. Malicious actors can exploit these leaked credentials to drastically lower the barrier to entry associated with brute-forcing access to their target networks.

While implementing well-configured MFA and enforcing regular password changes can help protect organizations, these measures alone may not be enough to fully negate the advantage attackers gain with stolen credentials.

In this instance, an attacker used leaked credentials to compromise an unused service account, allowing them to establish MFA and access the customer’s VPN. While this tactic may have allowed the attacker to evade human security teams and traditional security tools, Darktrace’s AI detected the unusual use of the account, indicating a potential compromise despite the organization’s MFA requirements being met. This underscores the importance of adopting an intelligent decision maker, like Darktrace, that is able to identify and respond to anomalies beyond standard protective measures.

Credit to Charlotte Thompson, Cyber Security Analyst, Ryan Traill, Threat Content Lead

Appendices

Darktrace DETECT Model Coverage

-       Device / Suspicious SMB Scanning Activity (Model Alert)

-       Device / ICMP Address Scan (Model Alert)

-       Device / Network Scan (Model Alert)

-       Device / Suspicious LDAP Search Operation (Model Alert)

-       User / Kerberos Username Brute Force (Model Alert)

-       Device / Large Number of Model Breaches (Model Alert)

-       Anomalous Connection / SMB Enumeration (Model Alert)

-       Device / Multiple Lateral Movement Model Breaches (Enhanced Monitoring Model Alert)

-       Device / Possible SMB/NTLM Reconnaissance (Model Alert)

-       Anomalous Connection / Possible Share Enumeration Activity (Model Alert)

-       Device / Attack and Recon Tools (Model Alert)

MITRE ATT&CK Mapping

Tactic – Technique - Code

INITIAL ACCESS - Hardware Additions     -T1200

DISCOVERY - Network Service Scanning -T1046

DISCOVERY - Remote System Discovery - T1018

DISCOVERY - Domain Trust Discovery      - T1482

DISCOVERY - File and Directory Discovery - T1083

DISCOVERY - Network Share Discovery - T1135

RECONNAISSANCE - Scanning IP Blocks - T1595.001

RECONNAISSANCE - Vulnerability Scanning - T1595.002

RECONNAISSANCE - Client Configurations - T1592.004

RECONNAISSANCE - IP Addresses - T1590.005

CREDENTIAL ACCESS - Brute Force - T1110

LATERAL MOVEMENT - Exploitation of Remote Services -T1210

References

  1. 2024 Google Cloud Threat Horizons Report
    https://services.google.com/fh/files/misc/threat_horizons_report_h12024.pdf
  2. IBM X-Force Threat Intelligence Index 2024
    https://www.ibm.com/reports/threat-intelligence
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
Charlotte Thompson
Cyber Analyst

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May 16, 2025

Catching a RAT: How Darktrace neutralized AsyncRAT

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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.

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

Model alert in Darktrace / NETWORK displaying the Digital Certificate attributes, IP address and port number associated with AsyncRAT.
Figure 2: Model alert in Darktrace / NETWORK displaying the Digital Certificate attributes, IP address and port number associated with AsyncRAT.
Darktrace’s detection of repeated connections to the suspicious IP address 185.49.126[.]50 over port 6606, indicative of beaconing behavior.
Figure 3: Darktrace’s detection of repeated connections to the suspicious IP address 185.49.126[.]50 over port 6606, indicative of beaconing behavior.
Darktrace's Autonomous Response actions blocking the suspicious IP address,185.49.126[.]50.
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].

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.
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].

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
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.

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.
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

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About the author
Isabel Evans
Cyber Analyst

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May 13, 2025

Revolutionizing OT Risk Prioritization with Darktrace 6.3

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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.

additions to darktrace / ot 6/3

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]

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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