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September 28, 2023

How Darktrace Stopped an Account Hijack Fast

Learn how Darktrace detected an account hijack within days of deployment. Discover the strategies used to protect against cyber threats.
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
Min Kim
Cyber Security Analyst
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28
Sep 2023

Cloud Migration Expanding the Attack Surface

Cloud migration is here to stay – accelerated by pandemic lockdowns, there has been an ongoing increase in the use of public cloud services, and Gartner has forecasted worldwide public cloud spending to grow around 20%, or by almost USD 600 billion [1], in 2023. With more and more organizations utilizing cloud services and moving their operations to the cloud, there has also been a corresponding shift in malicious activity targeting cloud-based software and services, including Microsoft 365, a prominent and oft-used Software-as-a-Service (SaaS).

With the adoption and implementation of more SaaS products, the overall attack surface of an organization increases – this gives malicious actors additional opportunities to exploit and compromise a network, necessitating proper controls to be in place. This increased attack surface can leave organization’s open to cyber risks like cloud misconfigurations, supply chain attacks and zero-day vulnerabilities [2]. In order to achieve full visibility over cloud activity and prevent SaaS compromise, it is paramount for security teams to deploy sophisticated security measures that are able to learn an organization’s SaaS environment and detect suspicious activity at the earliest stage.

Darktrace Immediately Detects Hijacked Account

In May 2023, Darktrace observed a chain of suspicious SaaS activity on the network of a customer who was about to begin their trial of Darktrace/Apps™ and Darktrace/Email™. Despite being deployed on the network for less than a week, Darktrace DETECT™ recognized that the legitimate SaaS account, belonging to an executive at the organization, had been hijacked. Darktrace/Email was able to provide full visibility over inbound and outbound mail and identified that the compromised account was subsequently used to launch an internal spear-phishing campaign.

If Darktrace RESPOND™ were enabled in autonomous response mode at the time of this compromise, it would have been able to take swift preventative action to disrupt the account compromise and prevent the ensuing phishing attack.

Account Hijack Attack Overview

Unusual External Sources for SaaS Credentials

On May 9, 2023, Darktrace/Apps detected the first in a series of anomalous activities performed by a Microsoft 365 user account that was indicative of compromise, namely a failed login from an external IP address located in Virginia.

Figure 1: The failed login notice, as seen in Darktrace/Apps. The notice includes additional context about the failed login attempt to the SaaS account.

Just a few minutes later, Darktrace observed the same user credential being used to successfully login from the same unusual IP address, with multi-factor authentication (MFA) requirements satisfied.

Figure 2: The “Unusual External Source for SaaS Credential Use” model breach summary, showing the successful login to the SaaS user account (with MFA), from the rare external IP address.

A few hours after this, the user credential was once again used to login from a different city in the state of Virginia, with MFA requirements successfully met again. Around the time of this activity, the SaaS user account was also observed previewing various business-related files hosted on Microsoft SharePoint, behavior that, taken in isolation, did not appear to be out of the ordinary and could have represented legitimate activity.

The following day, May 10, however, there were additional login attempts observed from two different states within the US, namely Texas and Florida. Darktrace understood that this activity was extremely suspicious, as it was highly improbable that the legitimate user would be able to travel over 2,500 miles in such a short period of time. Both login attempts were successful and passed MFA requirements, suggesting that the malicious actor was employing techniques to bypass MFA. Such MFA bypass techniques could include inserting malicious infrastructure between the user and the application and intercepting user credentials and tokens, or by compromising browser cookies to bypass authentication controls [3]. There have also been high-profile cases in the recent years of legitimate users mistakenly (and perhaps even instinctively) accepting MFA prompts on their token or mobile device, believing it to be a legitimate process despite not having performed the login themselves.

New Email Rule

On the evening of May 10, following the successful logins from multiple US states, Darktrace observed the Microsoft 365 user creating a new inbox rule, named “.’, in Microsoft Outlook from an IP located in Florida. Threat actors are often observed naming new email rules with single characters, likely to evade detection, but also for the sake of expediency so as to not expend any additional time creating meaningful labels.

In this case the newly created email rules included several suspicious properties, including ‘AlwaysDeleteOutlookRulesBlob’, ‘StopProcessingRules’ and “MoveToFolder”.

Firstly, ‘AlwaysDeleteOutlookRulesBlob’ suppresses or hides warning messages that typically appear if modifications to email rules are made [4]. In this case, it is likely the malicious actor was attempting to implement this property to obfuscate the creation of new email rules.

The ‘StopProcessingRules’ rule meant that any subsequent email rules created by the legitimate user would be overridden by the email rule created by the malicious actor [5]. Finally, the implementation of “MoveToFolder” would allow the malicious actor to automatically move all outgoing emails from the “Sent” folder to the “Deleted Items” folder, for example, further obfuscating their malicious activities [6]. The utilization of these email rule properties is frequently observed during account hijackings as it allows attackers to delete and/or forward key emails, delete evidence of exploitation and launch phishing campaigns [7].

In this incident, the new email rule would likely have enabled the malicious actor to evade the detection of traditional security measures and achieve greater persistence using the Microsoft 365 account.

Figure 3: Screenshot of the “New Email Rule” model breach. The Office365 properties associated with the newly modified Microsoft Outlook inbox rule, “.”, are highlighted in red.

Account Update

A few hours after the creation of the new email rule, Darktrace observed the threat actor successfully changing the Microsoft 365 user’s account password, this time from a new IP address in Texas. As a result of this action, the attacker would have locked out the legitimate user, effectively gaining full access over the SaaS account.

Figure 4: The model breach event log showing the user password and token change updates performed by the compromised SaaS account.

Phishing Emails

The compromised SaaS account was then observed sending a high volume of suspicious emails to both internal and external email addresses. Darktrace was able to identify that the emails attempting to impersonate the legitimate service DocuSign and contained a malicious link prompting users to click on the text “Review Document”. Upon clicking this link, users would be redirected to a site hosted on Adobe Express, namely hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/.

Adobe Express is a free service that allows users to create web pages which can be hosted and shared publicly; it is likely that the threat actor here leveraged the service to use in their phishing campaign. When clicked, such links could result in a device unwittingly downloading malware hosted on the site, or direct unsuspecting users to a spoofed login page attempting to harvest user credentials by imitating legitimate companies like Microsoft.

Figure 5: Screenshot of the phishing email, containing a malicious link hidden behind the “Review Document” text. The embedded link directs to a now-defunct page that was hosted on Adobe Express.

The malicious site hosted on Adobe Express was subsequently taken down by Adobe, possibly in response to user reports of maliciousness. Unfortunately though, platforms like this that offer free webhosting services can easily and repeatedly be abused by malicious actors. Simply by creating new pages hosted on different IP addresses, actors are able to continue to carry out such phishing attacks against unsuspecting users.

In addition to the suspicious SaaS and email activity that took place between May 9 and May 10, Darktrace/Email also detected the compromised account sending and receiving suspicious emails starting on May 4, just two days after Darktrace’s initial deployment on the customer’s environment. It is probable that the SaaS account was compromised around this time, or even prior to Darktrace’s deployment on May 2, likely via a phishing and credential harvesting campaign similar to the one detailed above.

Figure 6: Event logs of the compromised SaaS user, here seen breaching several Darktrace/Email model breaches on 4th May.

Darktrace Coverage

As the customer was soon to begin their trial period, Darktrace RESPOND was set in “human confirmation” mode, meaning that any preventative RESPOND actions required manual application by the customer’s security team.

If Darktrace RESPOND had been enabled in autonomous response mode during this incident, it would have taken swift mitigative action by logging the suspicious user out of the SaaS account and disabling the account for a defined period of time, in doing so disrupting the attack at the earliest possible stage and giving the customer the necessary time to perform remediation steps.  As it was, however, these RESPOND actions were suggested to the customer’s security team for them to manually apply.

Figure 7: Example of Darktrace RESPOND notices, in response to the anomalous user activity.

Nevertheless, with Darktrace/Apps in place, visibility over the anomalous cloud-based activities was significantly increased, enabling the swift identification of the chain of suspicious activities involved in this compromise.

In this case, the prospective customer reached out to Darktrace directly through the Ask the Expert (ATE) service. Darktrace’s expert analyst team then conducted a timely and comprehensive investigation into the suspicious activity surrounding this SaaS compromise, and shared these findings with the customer’s security team.

Conclusion

Ultimately, this example of SaaS account compromise highlights Darktrace’s unique ability to learn an organization’s digital environment and recognize activity that is deemed to be unexpected, within a matter of days.

Due to the lack of obvious or known indicators of compromise (IoCs) associated with the malicious activity in this incident, this account hijack would likely have gone unnoticed by traditional security tools that rely on a rules and signatures-based approach to threat detection. However, Darktrace’s Self-Learning AI enables it to detect the subtle deviations in a device’s behavior that could be indicative of an ongoing compromise.

Despite being newly deployed on a prospective customer’s network, Darktrace DETECT was able to identify unusual login attempts from geographically improbable locations, suspicious email rule updates, password changes, as well as the subsequent mounting of a phishing campaign, all before the customer’s trial of Darktrace had even begun.

When enabled in autonomous response mode, Darktrace RESPOND would be able to take swift preventative action against such activity as soon as it is detected, effectively shutting down the compromise and mitigating any subsequent phishing attacks.

With the full deployment of Darktrace’s suite of products, including Darktrace/Apps and Darktrace/Email, customers can rest assured their critical data and systems are protected, even in the case of hybrid and multi-cloud environments.

Credit: Samuel Wee, Senior Analyst Consultant & Model Developer

Appendices

References

[1] https://www.gartner.com/en/newsroom/press-releases/2022-10-31-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023

[2] https://www.upguard.com/blog/saas-security-risks

[3] https://www.microsoft.com/en-us/security/blog/2022/11/16/token-tactics-how-to-prevent-detect-and-respond-to-cloud-token-theft/

[4] https://learn.microsoft.com/en-us/powershell/module/exchange/disable-inboxrule?view=exchange-ps

[5] https://learn.microsoft.com/en-us/dotnet/api/microsoft.exchange.webservices.data.ruleactions.stopprocessingrules?view=exchange-ews-api

[6] https://learn.microsoft.com/en-us/dotnet/api/microsoft.exchange.webservices.data.ruleactions.movetofolder?view=exchange-ews-api

[7] https://blog.knowbe4.com/check-your-email-rules-for-maliciousness

Darktrace Model Detections

Darktrace DETECT and RESPOND Models Breached:

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Unusual Activity / Multiple Unusual External Sources for SaaS Credential

Antigena / SaaS / Antigena Unusual Activity Block (RESPOND Model)

SaaS / Compliance / New Email Rule

Antigena / SaaS / Antigena Significant Compliance Activity Block

SaaS / Compromise / Unusual Login and New Email Rule (Enhanced Monitoring Model)

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)

SaaS / Compromise / SaaS Anomaly Following Anomalous Login (Enhanced Monitoring Model)

SaaS / Compromise / Unusual Login and Account Update

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)

IoC – Type – Description & Confidence

hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/ - Domain – Probable Phishing Page (Now Defunct)

37.19.221[.]142 – IP Address – Unusual Login Source

35.174.4[.]92 – IP Address – Unusual Login Source

MITRE ATT&CK Mapping

Tactic - Techniques

INITIAL ACCESS, PRIVILEGE ESCALATION, DEFENSE EVASION, PERSISTENCE

T1078.004 – Cloud Accounts

DISCOVERY

T1538 – Cloud Service Dashboards

CREDENTIAL ACCESS

T1539 – Steal Web Session Cookie

RESOURCE DEVELOPMENT

T1586 – Compromise Accounts

PERSISTENCE

T1137.005 – Outlook Rules

Probability yardstick used to communicate the probability that statements or explanations given are correct.
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
Min Kim
Cyber Security Analyst

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November 27, 2025

CastleLoader & CastleRAT: Behind TAG150’s Modular Malware Delivery System

CastleLoader & CastleRAT: Behind TAG150’s Modular Malware Delivery SystemDefault blog imageDefault blog image

What is TAG-150?

TAG-150, a relatively new Malware-as-a-Service (MaaS) operator, has been active since March 2025, demonstrating rapid development and an expansive, evolving infrastructure designed to support its malicious operations. The group employs two custom malware families, CastleLoader and CastleRAT, to compromise target systems, with a primary focus on the United States [1]. TAG-150’s infrastructure included numerous victim-facing components, such as IP addresses and domains functioning as command-and-control (C2) servers associated with malware families like SecTopRAT and WarmCookie, in addition to CastleLoader and CastleRAT [2].

As of May 2025, CastleLoader alone had infected a reported 469 devices, underscoring the scale and sophistication of TAG-150’s campaign [1].

What are CastleLoader and CastleRAT?

CastleLoader is a loader malware, primarily designed to download and install additional malware, enabling chain infections across compromised systems [3]. TAG-150 employs a technique known as ClickFix, which uses deceptive domains that mimic document verification systems or browser update notifications to trick victims into executing malicious scripts. Furthermore, CastleLoader leverages fake GitHub repositories that impersonate legitimate tools as a distribution method, luring unsuspecting users into downloading and installing malware on their devices [4].

CastleRAT, meanwhile, is a remote access trojan (RAT) that serves as one of the primary payloads delivered by CastleLoader. Once deployed, CastleRAT grants attackers extensive control over the compromised system, enabling capabilities such as keylogging, screen capturing, and remote shell access.

TAG-150 leverages CastleLoader as its initial delivery mechanism, with CastleRAT acting as the main payload. This two-stage attack strategy enhances the resilience and effectiveness of their operations by separating the initial infection vector from the final payload deployment.

How are they deployed?

Castleloader uses code-obfuscation methods such as dead-code insertion and packing to hinder both static and dynamic analysis. After the payload is unpacked, it connects to its command-and-control server to retrieve and running additional, targeted components.

Its modular architecture enables it to function both as a delivery mechanism and a staging utility, allowing threat actors to decouple the initial infection from payload deployment. CastleLoader typically delivers its payloads as Portable Executables (PEs) containing embedded shellcode. This shellcode activates the loader’s core module, which then connects to the C2 server to retrieve and execute the next-stage malware.[6]

Following this, attackers deploy the ClickFix technique, impersonating legitimate software distribution platforms like Google Meet or browser update notifications. These deceptive sites trick victims into copying and executing PowerShell commands, thereby initiating the infection kill chain. [1]

When a user clicks on a spoofed Cloudflare “Verification Stepprompt, a background request is sent to a PHP script on the distribution domain (e.g., /s.php?an=0). The server’s response is then automatically copied to the user’s clipboard using the ‘unsecuredCopyToClipboard()’ function. [7].

The Python-based variant of CastleRAT, known as “PyNightShade,” has been engineered with stealth in mind, showing minimal detection across antivirus platforms [2]. As illustrated in Figure 1, PyNightShade communicates with the geolocation API service ip-api[.]com, demonstrating both request and response behavior

Packet Capture (PCAP) of PyNightShade, the Python-based variant of CastleRAT, communicating with the geolocation API service ip-api[.]com.
Figure 1: Packet Capture (PCAP) of PyNightShade, the Python-based variant of CastleRAT, communicating with the geolocation API service ip-api[.]com.

Darktrace Coverage

In mid-2025, Darktrace observed a range of anomalous activities across its customer base that appeared linked to CastleLoader, including the example below from a US based organization.

The activity began on June 26, when a device on the customer’s network was observed connecting to the IP address 173.44.141[.]89, a previously unseen IP for this network along with the use of multiple user agents, which was also rare for the user.  It was later determined that the IP address was a known indicator of compromise (IoC) associated with TAG-150’s CastleRAT and CastleLoader operations [2][5].

Figure 2: Darktrace’s detection of a device making unusual connections to the malicious endpoint 173.44.141[.]89.

The device was observed downloading two scripts from this endpoint, namely ‘/service/download/data_5x.bin’ and ‘/service/download/data_6x.bin’, which have both been linked to CastleLoader infections by open-source intelligence (OSINT) [8]. The archives contains embedded shellcode, which enables attackers to execute arbitrary code directly in memory, bypassing disk writes and making detection by endpoint detection and response (EDR) tools significantly more difficult [2].

 Darktrace’s detection of two scripts from the malicious endpoint.
Figure 3: Darktrace’s detection of two scripts from the malicious endpoint.

In addition to this, the affected device exhibited a high volume of internal connections to a broad range of endpoints, indicating potential scanning activity. Such behavior is often associated with reconnaissance efforts aimed at mapping internal infrastructure.

Darktrace / NETWORK correlated these behaviors and generated an Enhanced Monitoring model, a high-fidelity security model designed to detect activity consistent with the early stages of an attack. These high-priority models are continuously monitored and triaged by Darktrace’s Security Operations Center (SOC) as part of the Managed Threat Detection and Managed Detection & Response services, ensuring that subscribed customers are promptly alerted to emerging threats.

Darktrace detected an unusual ZIP file download alongside the anomalous script, followed by internal connectivity. This activity was correlated under an Enhanced Monitoring model.
Figure 4: Darktrace detected an unusual ZIP file download alongside the anomalous script, followed by internal connectivity. This activity was correlated under an Enhanced Monitoring model.

Darktrace Autonomous Response

Fortunately, Darktrace’s Autonomous Response capability was fully configured, enabling it to take immediate action against the offending device by blocking any further connections external to the malicious endpoint, 173.44.141[.]89. Additionally, Darktrace enforced a ‘group pattern of life’ on the device, restricting its behavior to match other devices in its peer group, ensuring it could not deviate from expected activity, while also blocking connections over 443, shutting down any unwanted internal scanning.

Figure 5: Actions performed by Darktrace’s Autonomous Response to contain the ongoing attack.

Conclusion

The rise of the MaaS ecosystem, coupled with attackers’ growing ability to customize tools and techniques for specific targets, is making intrusion prevention increasingly challenging for security teams. Many threat actors now leverage modular toolkits, dynamic infrastructure, and tailored payloads to evade static defenses and exploit even minor visibility gaps. In this instance, Darktrace demonstrated its capability to counter these evolving tactics by identifying early-stage attack chain behaviors such as network scanning and the initial infection attempt. Autonomous Response then blocked the CastleLoader IP delivering the malicious ZIP payload, halting the attack before escalation and protecting the organization from a potentially damaging multi-stage compromise

Credit to Ahmed Gardezi (Cyber Analyst) Tyler Rhea (Senior Cyber Analyst)
Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

  • Anomalous Connection / Unusual Internal Connections
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous File / Script from Rare External Location
  • Initial Attack Chain Activity (Enhanced Monitoring Model)

MITRE ATT&CK Mapping

  • T15588.001 - Resource Development – Malware
  • TG1599 – Defence Evasion – Network Boundary Bridging
  • T1046 – Discovery – Network Service Scanning
  • T1189 – Initial Access

List of IoCs
IoC - Type - Description + Confidence

  • 173.44.141[.]89 – IP – CastleLoader C2 Infrastructure
  • 173.44.141[.]89/service/download/data_5x.bin – URI – CastleLoader Script
  • 173.44.141[.]89/service/download/data_6x.bin – URI  - CastleLoader Script
  • wsc.zip – ZIP file – Possible Payload

References

[1] - https://blog.polyswarm.io/castleloader

[2] - https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations

[3] - https://www.pcrisk.com/removal-guides/34160-castleloader-malware

[4] - https://www.scworld.com/brief/malware-loader-castleloader-targets-devices-via-fake-github-clickfix-phishing

[5] https://www.virustotal.com/gui/ip-address/173.44.141.89/community

[6] https://thehackernews.com/2025/07/castleloader-malware-infects-469.html

[7] https://www.cryptika.com/new-castleloader-attack-using-cloudflare-themed-clickfix-technique-to-infect-windows-computers/

[8] https://www.cryptika.com/castlebot-malware-as-a-service-deploys-range-of-payloads-linked-to-ransomware-attacks/

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November 20, 2025

Managing OT Remote Access with Zero Trust Control & AI Driven Detection

managing OT remote access with zero trust control and ai driven detectionDefault blog imageDefault blog image

The shift toward IT-OT convergence

Recently, industrial environments have become more connected and dependent on external collaboration. As a result, truly air-gapped OT systems have become less of a reality, especially when working with OEM-managed assets, legacy equipment requiring remote diagnostics, or third-party integrators who routinely connect in.

This convergence, whether it’s driven by digital transformation mandates or operational efficiency goals, are making OT environments more connected, more automated, and more intertwined with IT systems. While this convergence opens new possibilities, it also exposes the environment to risks that traditional OT architectures were never designed to withstand.

The modernization gap and why visibility alone isn’t enough

The push toward modernization has introduced new technology into industrial environments, creating convergence between IT and OT environments, and resulting in a lack of visibility. However, regaining that visibility is just a starting point. Visibility only tells you what is connected, not how access should be governed. And this is where the divide between IT and OT becomes unavoidable.

Security strategies that work well in IT often fall short in OT, where even small missteps can lead to environmental risk, safety incidents, or costly disruptions. Add in mounting regulatory pressure to enforce secure access, enforce segmentation, and demonstrate accountability, and it becomes clear: visibility alone is no longer sufficient. What industrial environments need now is precision. They need control. And they need to implement both without interrupting operations. All this requires identity-based access controls, real-time session oversight, and continuous behavioral detection.

The risk of unmonitored remote access

This risk becomes most evident during critical moments, such as when an OEM needs urgent access to troubleshoot a malfunctioning asset.

Under that time pressure, access is often provisioned quickly with minimal verification, bypassing established processes. Once inside, there’s little to no real-time oversight of user actions whether they’re executing commands, changing configurations, or moving laterally across the network. These actions typically go unlogged or unnoticed until something breaks. At that point, teams are stuck piecing together fragmented logs or post-incident forensics, with no clear line of accountability.  

In environments where uptime is critical and safety is non-negotiable, this level of uncertainty simply isn’t sustainable.

The visibility gap: Who’s doing what, and when?

The fundamental issue we encounter is the disconnect between who has access and what they are doing with it.  

Traditional access management tools may validate credentials and restrict entry points, but they rarely provide real-time visibility into in-session activity. Even fewer can distinguish between expected vendor behavior and subtle signs of compromise, misuse or misconfiguration.  

As a result, OT and security teams are often left blind to the most critical part of the puzzle, intent and behavior.

Closing the gaps with zero trust controls and AI‑driven detection

Managing remote access in OT is no longer just about granting a connection, it’s about enforcing strict access parameters while continuously monitoring for abnormal behavior. This requires a two-pronged approach: precision access control, and intelligent, real-time detection.

Zero Trust access controls provide the foundation. By enforcing identity-based, just-in-time permissions, OT environments can ensure that vendors and remote users only access the systems they’re explicitly authorized to interact with, and only for the time they need. These controls should be granular enough to limit access down to specific devices, commands, or functions. By applying these principles consistently across the Purdue Model, organizations can eliminate reliance on catch-all VPN tunnels, jump servers, and brittle firewall exceptions that expose the environment to excess risk.

Access control is only one part of the equation

Darktrace / OT complements zero trust controls with continuous, AI-driven behavioral detection. Rather than relying on static rules or pre-defined signatures, Darktrace uses Self-Learning AI to build a live, evolving understanding of what’s “normal” in the environment, across every device, protocol, and user. This enables real-time detection of subtle misconfigurations, credential misuse, or lateral movement as they happen, not after the fact.

By correlating user identity and session activity with behavioral analytics, Darktrace gives organizations the full picture: who accessed which system, what actions they performed, how those actions compared to historical norms, and whether any deviations occurred. It eliminates guesswork around remote access sessions and replaces it with clear, contextual insight.

Importantly, Darktrace distinguishes between operational noise and true cyber-relevant anomalies. Unlike other tools that lump everything, from CVE alerts to routine activity, into a single stream, Darktrace separates legitimate remote access behavior from potential misuse or abuse. This means organizations can both audit access from a compliance standpoint and be confident that if a session is ever exploited, the misuse will be surfaced as a high-fidelity, cyber-relevant alert. This approach serves as a compensating control, ensuring that even if access is overextended or misused, the behavior is still visible and actionable.

If a session deviates from learned baselines, such as an unusual command sequence, new lateral movement path, or activity outside of scheduled hours, Darktrace can flag it immediately. These insights can be used to trigger manual investigation or automated enforcement actions, such as access revocation or session isolation, depending on policy.

This layered approach enables real-time decision-making, supports uninterrupted operations, and delivers complete accountability for all remote activity, without slowing down critical work or disrupting industrial workflows.

Where Zero Trust Access Meets AI‑Driven Oversight:

  • Granular Access Enforcement: Role-based, just-in-time access that aligns with Zero Trust principles and meets compliance expectations.
  • Context-Enriched Threat Detection: Self-Learning AI detects anomalous OT behavior in real time and ties threats to access events and user activity.
  • Automated Session Oversight: Behavioral anomalies can trigger alerting or automated controls, reducing time-to-contain while preserving uptime.
  • Full Visibility Across Purdue Layers: Correlated data connects remote access events with device-level behavior, spanning IT and OT layers.
  • Scalable, Passive Monitoring: Passive behavioral learning enables coverage across legacy systems and air-gapped environments, no signatures, agents, or intrusive scans required.

Complete security without compromise

We no longer have to choose between operational agility and security control, or between visibility and simplicity. A Zero Trust approach, reinforced by real-time AI detection, enables secure remote access that is both permission-aware and behavior-aware, tailored to the realities of industrial operations and scalable across diverse environments.

Because when it comes to protecting critical infrastructure, access without detection is a risk and detection without access control is incomplete.

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