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August 24, 2022

Detecting Unknown Ransomware: A Darktrace Case Study

Learn how Darktrace uncovered uncategorized ransomware threats in the Summer of 2021 with Darktrace SOC. Stay ahead of cyber threats with Darktrace technology.
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
Emma Foulger
Global Threat Research Operations Lead
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24
Aug 2022

Uncategorized attacks happen frequently, with new threat groups and malware continually coming to light. Novel and known threat groups alike are changing their C2 domains, file hashes and other threat infrastructure, allowing them to avoid detection through traditional signature and rule-based techniques. Zero-day exploitation has also become increasingly apparent – a recent Mandiant report revealed that the number of identified zero-days in 2021 had dramatically increased from 2020 (80 vs 32). More specifically, the number of zero-days exploited by ransomware groups was, and continues to be, on an upward trend [1]. This trend appears to have continued into 2022. Given the unknown nature of these attacks, it is challenging to defend against them using traditional signature and rule-based approaches. Only those anomaly-based solutions functioning via deviations from normal behavior in a network, will effectively detect these threats. 

It is particularly important that businesses can quickly identify threats like ransomware before the end-goal of encryption is reached. As the variety of ransomware strains increases, so do the number which are uncategorized. Whilst zero-days have recently been explored in another Darktrace blog, this blog looks at an example of a sophisticated novel ransomware attack that took place during Summer 2021 which Darktrace DETECT/Network detected ahead of it being categorized or found on popular OSINT. This occurred within the network of an East African financial organization.

Figure 1- Timeline of (then-uncategorized) Blackbyte ransom attack 

On the 6th of July 2021, multiple user accounts were brute-forced on an external-facing VPN server via NTLM. Notably this included attempted logins with the generic account ‘Administrator’. Darktrace alerted to this initial bruteforcing activity, however as similar attempts had been made against the server before, it was not treated as a high-priority threat.

Following successful bruteforcing on the VPN, the malicious actor created a new user account which was then added to an administrative group on an Active Directory server. This new user account was subsequently used in an RDP session to an internal Domain Controller. Cyber AI Analyst picked up on the unusual nature of these administrative connections in comparison to normal activity for these devices and alerted on it (Figure 2).

Figure 2: AI Analyst detected the suspicious nature of the initial lateral movement. RDP, DCE-RPC, and SMB connections were seen from the VPN server to the domain controller using the newly created account. Note: this screenshot is from DETECT/Network v.5

Less than 20 minutes later, significant reconnaissance began on the domain controller with the new credential. This involved SMB enumeration with various file shares accessed including sensitive files such as the Security Account Manager (samr). This was followed by a two-day period of downtime where the threat actor laid low. 

On the 8th of July, suspicious network behavior resumed – the default Administrator credential seen previously was also used on a second internal domain controller. Connections to a rare external IP were made by this device a few hours later. OSINT at the time suggested these connections may have been related to the use of penetration testing tools, in particular the tool Process Hacker [2].

Over the next two days reconnaissance and lateral movement activities occurred on a wider scale, originating from multiple network devices. A wide variety of techniques were used during this period: 

·      Exploitation of legitimate administrative services such as PsExec for remote command execution.

·      Taking advantage of legacy protocols still in use on the network like SMB version 1.

·      Bruteforcing login attempts via Kerberos.

·      The use of other penetration testing tools including Metasploit and Nmap. These were intended to probe for vulnerabilities.

On the 10th of July, ransomware was deployed. File encryption occurred, with the extension ‘.blackbyte’ being appended to multiple files. At the time there were no OSINT references to this file extension or ransomware type, therefore any signature-based solution would have struggled to detect it. It is now apparent that BlackByte ransomware had only appeared a few weeks earlier and,  since then, the Ransomware-as-a-Service group has been attacking businesses and critical infrastructure worldwide [3]. A year later they still pose an active threat.

The use of living-off-the-land techniques, popular penetration testing tools, and a novel strain of ransomware meant the attackers were able to move through the environment without giving away their presence through known malware-signatures. Although a traditional security solution would identify some of these actions, it would struggle to link these separate activities. The lack of attribution, however, had no bearing on Darktrace’s ability to detect the unusual behavior with its anomaly-based methods. 

While this customer had RESPOND enabled at the time of this attack, its manual configuration meant that it was unable to act on the devices engaging in encryption. Nevertheless, a wide range of high-scoring Darktrace DETECT/Network models breached which were easily visible within the customer’s threat tray. This included multiple Enhanced Monitoring models that would have led to Proactive Threat Notifications (PTN) being alerted had the customer subscribed to the service. Whilst the attack was not prevented in this case, Darktrace analysts were able to give support to the customer via Ask the Expert (ATE), providing in-depth analysis of the compromise including a list of likely compromised devices and credentials. This helped the customer to work on post-compromise recovery effectively and ensured the ransomware had reduced impact within their environment. 

Conclusion 

While traditional security solutions may be able to deal well with ransomware that uses known signatures, AI is needed to spot new or unknown types of attack – a reliance on signatures will lead to these types of attack being missed.  

Remediation can also be far more difficult if a victim doesn’t know how to identify the compromised devices or credentials because there are no known IOCs. Darktrace model breaches will highlight suspicious activity in each part of the cyber kill chain, whether involving a known IOC or not, helping the customer to efficiently identify areas of compromise and effectively remediate (Figure 3).  

Figure 3: An example of the various stages of the attack on one of the compromise servers being identified by Cyber AI Analyst. Note: this screenshot is from DETECT/Network v.5 

As long as threat actors continue to develop new methods of attack, the ability to detect uncategorized threats is required. As demonstrated above, Darktrace’s anomaly-based approach lends itself perfectly to detecting these novel or uncategorized threats. 

Thanks to Max Heinemeyer for his contributions to this blog.

Appendices

Model Breaches

·      Anomalous Connection / SMB Enumeration

·      Anomalous Connection / Suspicious Activity On High Risk Device

·      Anomalous Server Activity / Anomalous External Activity from Critical Network Device

·      Compliance / Default Credential Usage

·      Device / SMB Session Bruteforce

·      Anomalous Connection / Sustained MIME Type Conversion

·      Anomalous Connection / Unusual SMB Version 1 Connectivity

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

·      Compliance / Possible Unencrypted Password File on Server

·      Compliance / SMB Drive Write

·      Compliance / Weak Active Directory Ticket Encryption

·      Compromise / Ransomware / Possible Ransom Note Write

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

·      Compromise / Ransomware / SMB Reads then Writes with Additional Extensions

·      Compromise / Ransomware / Suspicious SMB Activity

·      Device / Attack and Recon Tools in SMB

·      Device / Multiple Lateral Movement Model Breaches

·      Device / New or Unusual Remote Command Execution

·      Device / SMB Lateral Movement

·      Device / Suspicious File Writes to Multiple Hidden SMB Shares

·      Device / Suspicious Network Scan Activity

·      Unusual Activity / Anomalous SMB Read & Write

·      Unusual Activity / Anomalous SMB to Server

·      User / Kerberos Password Bruteforce

References

[1] https://www.mandiant.com/resources/zero-days-exploited-2021

[2] https://www.virustotal.com/gui/ip-address/162.243.25.33/relations

[3] https://www.zscaler.com/blogs/security-research/analysis-blackbyte-ransomwares-go-based-variants

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
Emma Foulger
Global Threat Research Operations Lead

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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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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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