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February 9, 2022

The Impact of Conti Ransomware on OT Systems

Learn how ransomware can spread throughout converged IT/OT environments, and how Self-Learning AI empowers organizations to contain these 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
Oakley Cox
Director of Product
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09
Feb 2022

Ransomware has taken the world by storm, and IT is not the only technology affected. Operational Technology (OT), which is increasingly blending with IT, is also susceptible to ransomware tactics, techniques, and procedures (TTPs). And when ransomware strikes OT, the effects have the potential to be devastating.

Here, we will look at a ransomware attack that spread from IT to OT systems. The attack was detected by Darktrace AI.

This threat find demonstrates a use case of Darktrace’s technology that delivers immense value to organizations with OT: spotting and stopping ransomware at its earliest stages, before the damage is done. This is particularly helpful for organizations with interconnected enterprise and industrial environments, as it means:

  1. Emerging attacks can be contained in IT before they spread laterally into OT, and even before they spread from device to device in IT;
  2. Organizations gain granular visibility into their industrial environments, detecting deviations from normal activity, and quick identification of remediating actions.

Threat find: Ransomware and crypto-mining hijack affecting IT and OT systems

Darktrace recently identified an aggressive attack targeting an OT R&D investment firm in EMEA. The attack originally started as a crypto-mining campaign and later evolved into ransomware. This organization deployed Darktrace in a digital estate containing both IT and OT assets that spanned over 3,000 devices.

If the organization had deployed Darktrace’s Autonomous Response technology in active mode, this threat would have been stopped in its earliest stages. Even in the absence of Autonomous Response, however, mere human attention would have stopped this attack’s progression. Darktrace’s Self-Learning AI gave clear indications of an ongoing compromise in the month prior to the detonation of ransomware. In this case, however, the security team was not monitoring Darktrace’s interface, and so the attack was allowed to proceed.

Compromised OT devices

This threat find will focus on the attack techniques used to take over two OT devices, specifically, a HMI (human machine interface), and an ICS Historian used to collect and log industrial data. These OT devices were both VMware virtual machines running Windows OS, and were compromised as part of a wider Conti ransomware infection. Both devices were being used primarily within an industrial control system (ICS), running a popular ICS software package and making regular connections to an industrial cloud platform.

These devices were thus part of an ICSaaS (ICS-as-a-Service) environment, using virtualised and Cloud platforms to run analytics, update threat intelligence, and control the industrial process. As previously highlighted by Darktrace, the convergence of cloud and ICS increases a network’s attack surface and amplifies cyber risk.

Attack lifecycle

Opening stages

The initial infection of the OT devices occurred when a compromised Domain Controller (DC) made unusual Active Directory requests. The devices made subsequent DCE-RPC binds for epmapper, often used by attackers for command execution, and lsarpc, used by attackers to abuse authentication policies and escalate privileges.

The payload was delivered when the OT devices used SMB to connect to the sysvol folder on the DC and read a malicious executable file, called SetupPrep.exe.

Figure 1: Darktrace model breaches across the whole network from initial infection on October 21 to the detonation on November 15.

Figure 2: ICS reads on the HMI in the lead up, during, and following detonation of the ransomware.

Device encryption and lateral spread

The malicious payload remained dormant on the OT devices for three weeks. It seems the attacker used the time to install crypto-mining malware elsewhere on the network and consolidate their foothold.

On the day the ransomware detonated, the attacker used remote management tools to initiate encryption. The PSEXEC tool was used on an infected server (separate from the original DC) to remotely execute malicious .dll files on the compromised OT devices.

The devices then attempted to make command and control (C2) connections to rare external endpoints using suspicious ports. Like in many ICS networks, sufficient network segregation had been implemented to prevent the HMI device from making successful connections to the Internet and the C2 communications failed. But worryingly, the failed C2 did not prevent the attack from proceeding or the ransomware from detonating.

The Historian device made successful C2 connections to around 40 unique external endpoints. Darktrace detected beaconing-type behavior over suspicious TCP/SSL ports including 465, 995, 2078, and 2222. The connections were made to rare destination IP addresses that did not specify the Server Name Indication (SNI) extension hostname and used self-signed and/or expired SSL certificates.

Both devices enumerated network SMB shares and wrote suspicious shell scripts to network servers. Finally, the devices used SMB to encrypt files stored in network shares, adding a file extension which is likely to be unique to this victim and which will be called ABCXX for the purpose of this blog. Most encrypted files were uploaded to the folder in which the file was originally located, but in some instances were moved to the images folder.

During the encryption, the device was using the machine account to authenticate SMB sessions. This is in contrast to other ransomware incidents that Darktrace has observed, in which admin or service accounts are compromised and abused by the attacker. It is possible that in this instance the attacker was able to use ‘Living off the Land’ techniques (for example the use of lsarpc pipe) to give the machine account admin privileges.

Examples of files being encrypted and moved:

  • SMB move success
  • File: new\spbr0007\0000006A.bak
  • Renamed: new\spbr0007\0000006A.bak.ABCXX
  • SMB move success
  • File: ActiveMQ\readme.txt
  • Renamed: Images\10j0076kS1UA8U975GC2e6IY.488431411265952821382.png.ABCXX

Detonation of ransomware

Upon detonation, the ransomware note readme.txt was written by the ICS to targeted devices as part of the encryption activity.

The final model breached by the device was “Unresponsive ICS Device” as the device either stopped working due to the effects of the ransomware, or was removed from the network.

Figure 3: abc-histdev — external connections filtered on destination port 995 shows C2 connections starting around one hour before encryption began.

How the attack bypassed the rest of the security stack

In this threat find, there were a number of factors which resulted in the OT devices becoming compromised.

The first is IT/OT convergence. The ICS network was insufficiently segregated from the corporate network. This means that devices could be accessed by the compromised DC during the lateral movement stage of the attack. As OT becomes more reliant on IT, ensuring sufficient segregation is in place, or that an attacker can not circumvent such segregation, is becoming an ever increasing challenge for security teams.

Another reason is that the attacker used attack methods which leverage Living off the Land techniques to compromise devices with no discrimination as to whether they were part of an IT or OT network. Many of the machines used to operate ICS networks, including the devices highlighted here, rely on operating systems vulnerable to the kinds of TTPs observed here and that are regularly employed by ransomware groups.

Darktrace insights

Darktrace’s Cyber AI Analyst was able to stitch together many disparate forms of unusual activity across the compromised devices to give a clear security narrative containing details of the attack. The incident report for the Historian server is shown below. This provides a clear illustration of how Cyber AI Analyst can close any skills or communication gap between IT and OT specialists.

Figure 4: Cyber AI Analyst of the Historian server (abc-histdev). It investigated and reported the C2 communication (step 2) that started just before network reconnaissance using TCP scanning (step 3) and the subsequent file encryption over SMB (step 4).

In total, the attacker’s dwell time within the digital estate was 25 days. Unfortunately, it lead to disruption to operational technology, file encryption and financial loss. Altogether, 36 devices were crypto-mining for over 20 days – followed by nearly 100 devices (IT and OT) becoming encrypted following the detonation of the ransomware.

If it were active, Autonomous Response would have neutralized this activity, containing the damage before it could escalate into crisis. Darktrace’s Self-Learning AI gave clear indications of an ongoing compromise in the month prior to the detonation of ransomware, and so any degree of human attention toward Darktrace’s revelations would have stopped the attack.

Autonomous Response is highly configurable, and so, in industrial environments — whether air-gapped OT or converged IT/OT ecosystems — Antigena can be deployed in a variety of manners. In human confirmation mode, human operators need to give the green light before the AI takes action. Antigena can also be deployed only in the higher levels of the Purdue model, or the “IT in OT,” protecting the core assets from fast-moving attacks like ransomware.

Ransomware and interconnected IT/OT systems

ICS networks are often operated by machines that rely on operating systems which can be affected by TTPs regularly employed by ransomware groups — that is, TTPs such as Living off the Land, which do not discriminate between IT and OT.

The threat that ransomware poses to organizations with OT, including critical infrastructure, is so severe that the Cyber Infrastructure and Security Agency (CISA) released a fact sheet concerning these threats in the summer of 2021, noting the risk that IT attacks pose to OT networks:

“OT components are often connected to information technology (IT) networks, providing a path for cyber actors to pivot from IT to OT networks… As demonstrated by recent cyber incidents, intrusions affecting IT networks can also affect critical operational processes even if the intrusion does not directly impact an OT network.”

Major ransomware attacks against the Colonial Pipeline and JBS Foods demonstrate the potential for ransomware affecting OT to cause severe economic disruption on a national and international scale. And ransomware can wreak havoc on OT systems regardless of whether they directly target OT systems.

As industrial environments continue to converge and evolve — be they IT/OT, ICSaaS, or simply poorly segregated legacy systems — Darktrace stands ready to contain attacks before the damage is done. It is time for organizations with industrial environments to take the quantum leap forward that Darktrace’s Self-Learning AI is uniquely positioned to provide.

Thanks to Darktrace analysts Ash Brice and Andras Balogh for their insights on the above threat find.

Discover more on how Darktrace protects OT environments from ransomware

Darktrace model detections

HMI in chronological order at time of detonation:

  • Anomalous Connection / SMB Enumeration
  • Anomalous File / Internal / Unusual SMB Script Write
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Compromise / Ransomware / Suspicious SMB Activity [Enhanced Monitoring]
  • ICS / Unusual Data Transfer By OT Device
  • ICS / Unusual Unresponsive ICS Device

Historian

  • ICS / Rare External from OT Device
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • ICS / Unusual Activity From OT Device
  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Unusual Activity / SMB Access Failures
  • Device / Large Number of Model Breaches
  • ICS / Unusual Data Transfer By OT Device
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Device / SMB Lateral Movement
  • Compromise / Ransomware / Suspicious SMB Activity [Enhanced Monitoring]
  • Device / Multiple Lateral Movement Model Breaches [Enhanced Monitoring]

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
Oakley Cox
Director of Product

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December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

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What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAILTM and Darktrace / IDENTITYTM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services.  This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Darktrace’s detection of the anomalous email rule creation.
Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.
Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress,  as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.
Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique  

Phishing - T1566

Adversary-in-the-Middle - T1557

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