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March 13, 2024

Simulated vs. Real Malware: What You Need To Know

Learn how Darktrace distinguishes between simulated and real malware. Discover the advanced detection techniques used to protect your network.
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
Priya Thapa
Cyber Analyst
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13
Mar 2024

Distinguishing attack simulations from the real thing

In an era marked by the omnipresence of digital technologies and the relentless advancement of cyber threats, organizations face an ongoing battle to safeguard their digital environment. Although red and blue team exercises have long served as cornerstones in evaluating organizational defenses, their reliance on manual processes poses significant constraints [1]. Led by seasoned security professionals, these tests offer invaluable insights into security readiness but can be marred by their resource-intensive and infrequent testing cycles. The gaps between assessments leave organizations open to undetected vulnerabilities, compromising the true state of their security environment. In response to the ever-changing threat landscape, organizations are adopting a proactive stance towards cyber security to fortify their defenses.

At the forefront, these efforts tend to revolve around simulated attacks, a process designed to test an organization's security posture against both known and emerging threats in a safe and controlled environment [2]. These meticulously orchestrated simulations imitate the tactics, techniques, and procedures (TTPs) employed by actual adversaries and provide organizations with invaluable insights into their security resilience and vulnerabilities. By immersing themselves in simulated attack scenarios, security teams can proactively probe for vulnerabilities, adopt a more aggressive defense posture, and stay ahead of evolving cyber threats.

Distinguishing between simulated malware observations and authentic malware activities stands as a critical imperative for organizations bolstering their cyber defenses. While simulated platforms offer controlled scenarios for testing known attack patterns, Darktrace’s Self-Learning AI can detect known and unknown threats, identify zero-day threats, and previously unseen malware variants, including attack simulations. Whereas simulated platforms focus on specific known attack vectors, Darktrace DETECT™ and Darktrace RESPOND™ can identify and contain both known and unknown threats across the entire attack surface, providing unparalleled protection of the cyber estate.

Darktrace’s Coverage of Simulated Attacks

In January 2024, the Darktrace Security Operations Center (SOC) received a high volume of alerts relating to an unspecified malware strain that was affecting multiple customers across the fleet, raising concerns, and prompting the Darktrace Analyst team to swiftly investigate the multitude of incident. Initially, these activities were identified as malicious, exhibiting striking resemblance to the characteristics of Remcos, a sophisticated remote access trojan (RAT) that can be used to fully control and monitor any Windows computer from XP and onwards [3]. However, further investigation revealed that these activities were intricately linked to a simulated malware provider.

This discovery underscores a pivotal insight into Darktrace’s capabilities. To this point, leveraging advanced AI, Darktrace operates with a sophisticated framework that extends beyond conventional threat detection. By analyzing network behavior and anomalies, Darktrace not only discerns between simulated threats, such as those orchestrated by breach and attack simulation platforms and genuine malicious activities but can also autonomously respond to these threats with RESPOND. This showcases Darktrace’s advanced capabilities in effectively mitigating cyber threats.

Attack Simulation Process: Initial Access and Intrusion

Darktrace initially observed devices breaching several DETECT models relating to the hostname “new-tech-savvy[.]com”, an endpoint that was flagged as malicious by multiple open-source intelligence (OSINT) vendors [4].

In addition, multiple HTML Application (HTA) file downloads were observed from the malicious endpoint, “new-tech-savvy[.]com/5[.]hta”. HTA files are often seen as part of the UAC-0050 campaign, known for its cyber-attacks against Ukrainian targets, which tends to leverage the Remcos RAT with advanced evasion techniques [5] [6]. Such files are often critical components of a malware operation, serving as conduits for the deployment of malicious payloads onto a compromised system. Often, within the HTA file resides a VBScript which, upon execution, triggers a PowerShell script. This PowerShell script is designed to facilitate the download of a malicious payload, namely “word_update.exe”, from a remote server. Upon successful execution, “word_update.exe” is launched, invoking cmd.exe and initiating the sharing of malicious data. This process results in the execution of explorer.exe, with the malicious RemcosRAT concealed within the memory of explorer.exe. [7].

As the customers were subscribed to Darktrace’s Proactive Threat Notification (PTN) service, an Enhanced Monitoring model was breached upon detection of the malicious HTA file. Enhanced Monitoring models are high-fidelity DETECT models designed to identify activity likely to be indicative of compromise. These PTN alerts were swiftly investigated by Darktrace’s round the clock SOC team.

Following this successful detection, Darktrace RESPOND took immediate action by autonomously blocking connections to the malicious endpoint, effectively preventing additional download attempts. Similar activity may be seen in the case of a legitimate malware attack; however, in this instance, the hostname associated with the download confirmed the detected malicious activity was the result of an attack simulation.

Figure 1: The Breach Log displays the model breach, “Anomalous File/Incoming HTA File”, where a device was detected downloading the HTA file, “5.hta” from the endpoint, “new-tech-savvy[.]com”.
'
Figure 2: The Model Breach Event Log shows a device making connections to the endpoint, “new-tech-savvy[.]com”. As a result, theRESPOND model, “Antigena/Network/External Threat/Antigena File then New Outbound Block", breached and connections to this malicious endpoint were blocked.
Figure 3: The Breach Log further showcases another RESPOND model, “Antigena/Network/External Threat/Antigena Suspicious File Block", which was triggered when the device downloaded a  HTA file from the malicious endpoint, “new-tech-savvy[.]com".

In other cases, Darktrace observed SSL and HTTP connections also attributed to the same simulated malware provider, highlighting Darktrace’s capability to distinguish between legitimate and simulated malware attack activity.

Figure 4: The Model Breach “Anomalous Connection/Low and Slow Exfiltration" displays the hostname of a simulated malware provider, confirming the detected malicious activity as the result of an attack simulation.
Figure 5: The Model Breach Event Log shows the SSL connections made to an endpoint associated with the simulated malware provider.
Figure 6: Darktrace’s Advanced Search displays SSL connection logs to the endpoint of the simulated malware provider around the time the simulation activity was observed.

Upon detection of the malicious activity occurring within affected customer networks, Darktrace’s Cyber AI Analyst™ investigated and correlated the events at machine speed. Figure 8 illustrates the synopsis and additional technical information that AI Analyst generated on one customer’s environment, detailing that over 220 HTTP queries to 18 different endpoints for a single device were seen. The investigation process can also be seen in the screenshot, showcasing Darktrace’s ability to provide ‘explainable AI’ detail. AI Analyst was able to autonomously search for all HTTP connections made by the breach device and identified a single suspicious software agent making one HTTP request to the endpoint, 45.95.147[.]236.

Furthermore, the malicious endpoints, 45.95.147[.]236, previously observed in SSH attacks using brute-force or stolen credentials, and “tangible-drink.surge[.]sh”, associated with the Androxgh0st malware [8] [9] [10], were detected to have been requested by another device.

This highlights Darktrace’s ability to link and correlate seemingly separate events occurring on different devices, which could indicate a malicious attack spreading across the network.  AI Analyst was also able to identify a username associated with the simulated malware prior to the activity through Kerberos Authentication Service (AS) requests. The device in question was also tagged as a ‘Security Device’ – such tags provide human analysts with valuable context about expected device activity, and in this case, the tag corroborates with the testing activity seen. This exemplifies how Darktrace’s Cyber AI Analyst takes on the labor-intensive task of analyzing thousands of connections to hundreds of endpoints at a rapid pace, then compiling results into a single pane that provides customer security teams with the information needed to evaluate activities observed on a device.

All in all, this demonstrates how Darktrace’s Self-Learning AI is capable of offering an unparalleled level of awareness and visibility over any anomalous and potentially malicious behavior on the network, saving security teams and administrators a great deal of time.

Figure 7: Cyber AI Analyst Incident Log containing a summary of the attack simulation activity,, including relevant technical details, and the AI investigation process.

Conclusion

Simulated cyber-attacks represent the ever-present challenge of testing and validating security defenses, while the threat of legitimate compromise exemplifies the constant risk of cyber threats in today’s digital landscape. Darktrace emerges as the solution to this conflict, offering real-time detection and response capabilities that identify and mitigate simulated and authentic threats alike.

While simulations are crafted to mimic legitimate threats within predefined parameters and controlled environments, the capabilities of Darktrace DETECT transcend these limitations. Even in scenarios where intent is not malicious, Darktrace’s ability to identify anomalies and raise alerts remains unparalleled. Moreover, Darktrace’s AI Analyst and autonomous response technology, RESPOND, underscore Darktrace’s indispensable role in safeguarding organizations against emerging threats.

Credit to Priya Thapa, Cyber Analyst, Tiana Kelly, Cyber Analyst & Analyst Team Lead

Appendices

Model Breaches

Darktrace DETECT Model Breach Coverage

Anomalous File / Incoming HTA File

Anomalous Connection / Low and Slow Exfiltration

Darktrace RESPOND Model Breach Coverage

§  Antigena / Network/ External Threat/ Antigena File then New Outbound Block

Cyber AI Analyst Incidents

• Possible HTTP Command and Control

• Suspicious File Download

List of IoCs

IP Address

38.52.220[.]2 - Malicious Endpoint

46.249.58[.]40 - Malicious Endpoint

45.95.147[.]236 - Malicious Endpoint

Hostname

tangible-drink.surge[.]sh - Malicious Endpoint

new-tech-savvy[.]com - Malicious Endpoint

References

1.     https://xmcyber.com/glossary/what-are-breach-and-attack-simulations/

2.     https://www.picussecurity.com/resource/glossary/what-is-an-attack-simulation

3.     https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US&sfdcIFrameOrigin=null

4.     https://www.virustotal.com/gui/url/c145cf7010545791602e9585f447347c75e5f19a0850a24e12a89325ded88735

5.     https://www.virustotal.com/gui/url/7afd19e5696570851e6413d08b6f0c8bd42f4b5a19d1e1094e0d1eb4d2e62ce5

6.     https://thehackernews.com/2024/01/uac-0050-group-using-new-phishing.html

7.     https://www.uptycs.com/blog/remcos-rat-uac-0500-pipe-method

8.     https://www.virustotal.com/gui/ip-address/45.95.147.236/community

9.     https://www.virustotal.com/gui/domain/tangible-drink.surge.sh/community

10.  https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-016a

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
Priya Thapa
Cyber Analyst

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July 27, 2026

Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection

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Why ransomware has changed

Ransomware attacks have continued to increase globally, with 698 incidents reported in May 2026, representing a 48% rise compared to 472 incidents in May 2025 [1]. At the same time, the ransomware landscape is evolving. Several major ransomware groups, including LockBit [2], have been disrupted by successful joint law enforcement operations, resulting in a shift away from a small number of dominant actors towards a more fragmented and distributed ecosystem. This is increasingly composed of smaller groups who play a specialized role in the attack, such as initial access brokers, affiliates or developers.

As a result, ransomware tactics, techniques, and procedures (TTPs) are becoming more diverse and less predictable. On top of this, adversaries are leveraging native tools and legitimate penetration testing frameworks to evade detection. Anomaly-based detection is therefore critical to identify pre-ransomware activity, rather than relying on signatures associated with a handful of well-known ransomware groups.

As these attacks often unfold over several days, there is a critical window for defenders to act. In this context, behavioral-based detection plays a vital role in identifying suspicious pre-ransomware activity, and enabling early intervention before encryption or exfiltration occurs.

Inside a modern ransomware intrusion

In early 2026, Darktrace detected activity within a customer’s environment related to a multi-stage ransomware intrusion from the initial compromise. This activity does not appear to be attributable to a specific ransomware group, and no known ransomware payload was observed until the final stage.

The attack aligns with a broader industry trend in which compromised virtual private network (VPN) credentials are used as an entry point, followed by rapid internal reconnaissance and lateral movement using legitimate administrative tools. This growing preference for native tools and legitimate frameworks in cyber-attacks illustrates that it is increasingly unreliable to depend solely on traditional indicators of compromise such as known malware signatures or exploit detection.

The intrusion also involved the use of Sliver, an open-source adversary emulation framework, which is increasingly observed in real-world attacks. Originally designed for penetration testing and red teaming, Sliver has gained traction among threat actors as a stealthier alternative to more heavily signatured frameworks such as Cobalt Strike. As a legitimate framework, its use further complicates detection for security tools that rely on known malicious signatures.

Darktrace’s detection of a ransomware event in a customer’s environment

The initial compromise appears to have occurred via compromised credentials used over the VPN shortly before, or at the onset of the first indicators of suspicious activity. While it remains unclear as to how or when the threat actors gained access to these credentials, the use of initial access brokers (IABs) is a common feature of modern ransomware operations. This suggests that access to the environment may have been established several days or weeks beforehand.

The intrusion unfolded over three days, presenting multiple opportunities for early detection and intervention before ransomware deployment. The attack progressed through a compressed but structured sequence: initial access and reconnaissance were completed within hours, followed by privilege escalation and lateral movement the next day, and culminating in data exfiltration and encryption shortly thereafter. Throughout each stage, distinct behavioral anomalies emerged across the network providing clear indicators of malicious activity well before the ransomware was deployed.

While Darktrace’s Autonomous Response capability was enabled within the customer’s environment, it was not fully configured across the impacted devices, allowing the attack to progress to ransomware deployment. Had Autonomous Response been fully deployed across the affected systems, it could have taken targeted action against the earliest stages of malicious activity, potentially disrupting the intrusion before it escalated.

Figure 1: Timeline of the attack progression.

Day 1: Reconnaissance and privilege escalation

The threat actor gained access via compromised VPN credentials and initiated internal reconnaissance. Darktrace detected anomalous scanning behavior, including unusual port scanning activity and widespread network enumeration.

Specifically, Darktrace detected a high volume of east-west scanning activity across a broad range of ports, with TCP connections targeting ports 21, 80, 445, 4899 and 8080. Associated URIs suggested the use of Nmap, a widely used penetration testing tool. This highlights how attackers often leverage legitimate penetration testing tools for malicious reconnaissance, enabling them to blend into normal network activity and evade traditional signature-based detection methods.

Figure 2: Darktrace's detection of a sharp increase in anomalous internal connections, triggering multiple high-severity model alerts associated with reconnaissance activity.

Several devices were observed using administrative credentials to carry out privileged actions in a manner that was highly anomalous for the environment. This activity was accompanied by behavior consistent with SMB authentication scanning, suggesting efforts to identify and access additional systems. As the activity intensified, an increasing number of devices became involved, signalling lateral movement and further spread across the network.

Darktrace also identified privilege escalation through active directory (AD) replication abuse, specifically via the drsuapi::DRSGetNCChanges function. This technique allows an attacker with sufficient privileges to request directory replication data from a domain controller (DC), enabling them to extract credentials, including password hashes, without directly interacting with user accounts. Commonly associated with ‘DCSync’ attacks, this technique is frequently used to obtain highly privileged credentials and enable further escalation within an environment.

Figure 3: Darktrace’s detection of anomalous AD replication activity indicative of privilege escalation.

This activity was seen alongside the use of the now obsolete SMBv1, repeated NTLM authentication attempts using multiple variations of ‘Administrator’ credentials, reverse DNS scanning, and large-scale network scanning. Darktrace observed widespread use of SMBv1 across the customer’s environment, exposing a significant security weakness. As a legacy protocol with well-documented weaknesses, SMBv1 can be exploited to facilitate lateral movement, allowing the attackers to expand their access following initial compromise.

Day 3: Lateral Movement, Command & Control, and Exfiltration

Two days later, the attacker escalated privileges and expanded their foothold using living-off-the-land (LOTL) techniques such as PSExec, WMI, and RDP. Concurrently, Darktrace identified command-and-control (C2)-style communications consistent with the Sliver framework, alongside rare outbound connections to cloud infrastructure indicating potential data exfiltration. The volume and severity of observed activity increased as attack behavior intensified.

The device was observed conducting extensive lateral movement, leveraging LOTL techniques to evade detection. Activity included WMI execution (e.g. ExecQuery), DCE-RPC activity, SMB sessions and file writes, most of which were successful, as well as the deployment of PSEXESVC.exe via ADMIN$ shares and prolonged RDP sessions. Darktrace identified this behavior as highly anomalous for the environment. Such activity is commonly associated with the transfer of attacker tooling, remote command execution, and the establishment of persistent access across compromised systems.  

Figure 4: Darktrace’s detection of a spike in RPC binding events indicative of potential lateral movement.

On the same day, Darktrace detected C2-style SSL communications originating from multiple internal devices to rare external endpoints. These connections exhibited anomalous characteristics, including invalid SSL certificates and repeated connection patterns resembling beaconing. Analysis of the observed JA3 fingerprint further linked the activity to Sliver, the adversary simulation framework referenced earlier, as the hash has previously been associated with Sliver-related infrastructure [3]. The use of this framework reflects a broader trend of attackers repurposing legitimate offensive security tools for stealthy C2 communications. Connections to 137[.]220[.]59[.]55 (ASN AS20473 AS-VULTR) indicated that the communications were likely routed via a virtual private server (VPS) hosted by Vultr. Attackers often utilize VPS infrastructure from legitimate cloud providers like Vultr to obscure their true origin, blend into benign traffic, and evade IP-based detection mechanisms [4].

Figure 5: Darktrace’s Cyber AI Analyst detection of two linked unusual connections to Vultr infrastructure.

Darktrace also observed a device initiating SSL connections to safedata.s3[.]wasabisys[.]com, an endpoint associated with Wasabi cloud storage. Darktrace recognized that neither the destination nor the associated IP address had previously been observed within the environment.  More than 200 MB of data was subsequently uploaded externally to endpoints sharing the same JA3 client hash, indicating a sustained transfer session and potential data exfiltration to third-party storage. The apparent exfiltration prior to encryption is consistent with a double-extortion ransomware strategy.

Figure 6: Darktrace’s Cyber AI Analyst detection of more than 30 rare outbound connections to a Wasabi cloud storage endpoint, indicative of potential data exfiltration

Day 4: Encryption

The attack culminated in ransomware deployment, marking the transition from suspicious network activity to a business-impacting incident. Using SMB-based propagation, the threat actor encrypted thousands of files across the network, affecting multiple systems and disrupting normal operations. Throughout the encryption event, the legacy SMBv1 protocol was used extensively across multiple internal systems, resulting in a significant increase in newly encrypted files.

Figure 7: Darktrace’s detection of abnormal spikes in SMB activity and associated model alerts, indicative of ransomware encryption and propagation.

Darktrace’s Cyber AI Analyst automatically investigated and correlated the encryption activity and related events into a single incident narrative, providing the customer with real-time visibility into the attack while significantly reducing investigation time.

Figure 8: Darktrace’s Cyber AI Analyst’s investigation into the encryption activity. AI Analyst incident detailing example encryption activity in real time. Related events are automatically correlated and summarized into a clear narrative, reducing investigation time.

Defender action recommendations

What Could Have Stopped the Attack Earlier?

Although the attack ultimately resulted in ransomware deployment, there were multiple opportunities to detect, contain, and disrupt the intrusion before encryption occurred. The following actions could have significantly reduced the overall impact:

Detect and investigate indicators of reconnaissance and lateral movement

  • Unusual scanning
  • Active Directory replication anomalies consistent with DCSync activity
  • Anomalous use of native tools and processes indicative of LOTL attacks
  • Unusual use of common reconnaissance tools such as Nmap and NetScan

Contain compromised credentials and affected devices

  • Disable and reset compromised VPN credentials
  • Isolate devices performing anomalous scanning and lateral movement activity

Block suspicious external communications and data exfiltration

  • Use anomaly-based detection to detect and block repeated outbound connections to rare external infrastructure
  • Prevent data exfiltration to unauthorized cloud storage services such as Wasabi

Conclusion

The incident highlights the importance of anomaly-based detection, particularly against attacks that primarily use native or legitimate tools to evade traditional security measures. Darktrace identified suspicious activity from the first day of the compromise, providing multiple opportunities to disrupt the intrusion before it progressed to lateral movement and data exfiltration.

In this instance, detection was not the limiting factor; response time was. Prompt investigation and containment of devices exhibiting anomalous behavior could have prevented lateral movement, data exfiltration, and ultimately ransomware deployment.

As adversaries increasingly prioritize stealth over custom malware, relying instead on legitimate tools, valid credentials, and trusted infrastructure, traditional signature-based detection becomes less effective. Identifying subtle behavioral deviations early remains critical to disrupting attacks before they escalate into full-scale ransomware incidents.

Credit to Alexandra Evzona (Cyber Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://industrialcyber.co/ransomware/check-point-reports-ransomware-attacks-jump-48-year-over-year-despite-decline-in-overall-cyberattack-activity/

[2] https://www.europol.europa.eu/media-press/newsroom/news/law-enforcement-disrupt-worlds-biggest-ransomware-operation

[3] https://fieldeffect.com/blog/field-effect-mitigates-not-so-simplehelp-exploits-enabling-deployment-of-backdoors

[4] https://www.darktrace.com/blog/from-vps-to-phishing-how-darktrace-uncovered-saas-hijacks-through-virtual-infrastructure-abuse

Indicators of Compromise (IoCs)

IP Addresses

  • 137[.]220[.]59[.]55 – Potential C2 infrastructure (Vultr AS20473)
  • 38[.]27[.]106[.]123 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]128 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]117 – Wasabi cloud storage endpoint associated with potential data exfiltration

Domains

  • safedata[.]s3[.]wasabisys[.]com – Potential data exfiltration endpoint
  • *.wasabisys[.]com – Associated Wasabi cloud storage infrastructure

JA3 Fingerprint

  • d6828e30ab66774a91a96ae93be4ae4c – Associated with the Sliver adversary emulation framework

Files

  • Delete[.]me – File observed during reconnaissance activity, commonly associated with NetScan

Darktrace Model Coverage

·       Anomalous Connection / Active Remote Desktop Tunnel

·       Anomalous Connection / Anomalous Remote Registry Service Control

·       Anomalous Connection / Multiple Failed Windows UDP

·       Anomalous Connection / New or Uncommon Service Control

·       Anomalous Connection / New or Uncommon Service Enumeration

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / SMB Enumeration

·       Anomalous Connection / Suspicious Activity On High Risk Device

·       Anomalous Connection / Suspicious Read Write Ratio

·       Anomalous Connection / Sustained MIME Type Conversion

·       Anomalous Connection / Uncommon 1 GiB Outbound

·       Anomalous Connection / Unusual Admin RDP Session

·       Anomalous Connection / Unusual Admin SMB Session

·       Anomalous Connection / Unusual SMB Version 1 Connectivity

·       Anomalous File / EXE from Rare External Location

·       Anomalous File / Internal / Additional Extension Appended to SMB File

·       Anomalous Server Activity / Outgoing from Server

·       Compromise / Beaconing Activity To External Rare

·       Compromise / Ransomware / Possible Ransom Note Read

·       Compromise / Ransomware / Ransom or Offensive Words Written to SMB

·       Compromise / Ransomware / Suspicious SMB Activity

·       Device / Anonymous NTLM Logins

·       Device / Attack and Recon Tools

·       Device / Initial Attack Chain Activity

·       Device / Large Number of Model Alerts

·       Device / Long Agent Connection to New Endpoint

·       Device / Multiple Lateral Movement Model Alerts

·       Device / Network Scan

·       Device / New or Uncommon SMB Named Pipe

·       Device / New or Unusual Remote Command Execution

·       Device / New User Agent

·       Device / Possible RPC Lateral Movement

·       Device / Possible SMB/NTLM Brute Force

·       Device / RDP Scan

·       Device / SMB Lateral Movement

·       Device / SMB Session Brute Force (Non-Admin)

·       Device / SMB Version 1 Access Failures

·       Device / Suspicious File Delete Activity

·       Device / Suspicious Network Scan Activity

·       Device / Unusual SMB Error Detected

·       Device / Unusual SMB To Critical Resource

·       Device / Unusual Winreg Operation

·       Unusual Activity / Multiple Failed Internal Connections

·       Unusual Activity / Possible RPC Recon Activity

·       Unusual Activity / Sustained Anomalous SMB Activity

·       Unusual Activity / Unusual External Data to New Endpoint

·       Unusual Activity / Unusual External Data Transfer

·       Unusual Activity / Unusual File Storage Data Transfer

·       Unusual Activity / Unusual Large Internal Transfer

·       User / New Admin Credentials on Client

·       User / NTLM Login from Unauthenticated Device

Autonomous Response Model Alerts

·       Antigena / Network / External Threat / Antigena Ransomware Block

·       Antigena / Network / External Threat / Antigena Suspicious File Block

·       Antigena / Network / Insider Threat / Antigena Active Threat SMB Write Block

·       Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

·       Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

·       Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

·       Antigena / Network / Manual / Quarantine Device

·       Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·       Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block 100

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK MAPPING

Command and control - Protocol Tunneling - T1572

Command and control – Web Protocols – T1071.001

Credential Access- Exploitation for Credential Access- T1212

Credential Access- Password Guessing- T1110

Discovery- File and Directory Discovery- T1083

Discovery- Network Service Discovery- T1046

Discovery- Network Share Discovery- T1135

Discovery- Remote System Discovery- T1018

Exfiltration- Exfiltration Over C2 Channel- T1041

Exfiltration- Exfiltration to Cloud Storage- T1567

Impact- Data Encrypted for Impact- T1486

Impact- Data Encrypted for Impact- T1486

Impact- Service Stop - T1489

Initial Access- Public-Facing Application- T1190

Lateral Movement- Exploitation of Remote Services- T1210

Lateral Movement- Remote Desktop Protocol- T1021

Lateral Movement- SMB/Windows Admin Shares- T1021

Lateral Movement- Taint Shared Content- T1080

Persistence- Modify Registry- T1112

Privilege Escalation- Exploitation for Privilege Escalation- T0890

Privilege Escalation- Valid Accounts- T1078

Reconnaissance- Scanning IP Blocks- T1595

Reconnaissance- Vulnerability Scanning- T1595

Resource Development- Malware- T1588

Stealth- File Deletion- T1070

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Alexandra Evzona
Cyber Analyst

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
Your data. Our AI.
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