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February 29, 2024

Protecting Against AlphV BlackCat Ransomware

Learn how Darktrace AI is combating AlphV BlackCat ransomware, including the details of this ransomware and how to protect yourself from it.
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
Sam Lister
Specialist Security Researcher
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29
Feb 2024

As-a-Service malware trending

Throughout the course of 2023, “as-a-Service” strains of malware remained the most consistently observed threat type to affect Darktrace customers, mirroring their overall prominence across the cyber threat landscape. With this trend expected to continue throughout 2024, organizations and their security teams should be prepared to defend their network against increasingly versatile and tailorable malware-as-a-service (MaaS) and ransomware-as-a-service (RaaS) strains [1].

What is ALPHV ransomware?

The ALPHV ransomware, also known as ‘BlackCat’ or ‘Noberus’, is one example of a RaaS strain that has been prominent across the threat landscape over the last few years.

ALPHV is a ransomware strain coded in the Rust programming language. The ransomware is sold as part of the RaaS economy [2], with samples of the ransomware being provided and sold by a criminal group (the RaaS ‘operator’) to other cybercriminals (the RaaS ‘affiliates’) who then gain entry to organizations' networks with the intention of detonating the ransomware and demanding ransom payments.

ALPHV was likely first used in the wild back in November 2021 [3]. Since then, it has become one of the most prolific ransomware strains, with the Federal Bureau of Investigation (FBI) reporting nearly USD 300 million in ALPHV ransom payments as of September 2023 [4].

In December 2023, the FBI and the US Department of Justice announced a successful disruption campaign against the ALPHV group, which included a takedown of the their data leak site, and the release of a decryption tool for the ransomware strain [5], and in February 2024, the US Department of State announced  a reward of up to USD 10 million for information leading to the identification or location of anyone occupying a key leadership position in the group operating the ALPHV ransomware strain [6].

The disruption campaign against the ransomware group appeared to have been successful, as evidenced by the recent, significant decline in ALPHV attacks, however, it would not be surprising for the group to simply return with new branding, in a similar vein to its apparent predecessors, DarkSide and BlackMatter [7].

How does ALPHV ransomware work?

ALPHV affiliates have been known to employ a variety of methods to progress towards their objective of detonating ALPHV ransomware [4]. In the latter half of 2023, ALPHV affiliates were observed using malicious advertising (i.e, malvertising) to deliver a Python-based backdoor-dropper known as 'Nitrogen' to users' devices [8][12]. These malvertising operations consisted in affiliates setting up malicious search engine adverts for tools such as WinSCP and AnyDesk.

Users' interactions with these adverts led them to sites resembling legitimate software distribution sites. Users' attempts to download software from these spoofed sites resulted in the delivery of a backdoor-dropping malware sample dubbed 'Nitrogen' to their devices. Nitrogen has been observed dropping a variety of command-and-control (C2) implants onto users' devices, including Cobalt Strike Beacon and Sliver C2. ALPHV affiliates often used the backdoor access afforded to them by these C2 implants to conduct reconnaissance and move laterally, in preparation for detonating ALPHV ransomware payloads.

Darktrace Detection of ALPHV Ransomware

During October 2023, Darktrace observed several cases of ALPHV affiliates attempting to infiltrate organizations' networks via the use of malvertising to socially engineer users into downloading and installing Nitrogen from impersonation websites such as 'wireshhark[.]com' and wìnscp[.]net (i.e, xn--wnscp-tsa[.]net).

While the attackers managed to bypass traditional security measures and evade detection by using a device from the customer’s IT team to perform its malicious activity, Darktrace DETECT™ swiftly identified the subtle indicators of compromise (IoCs) in the first instance. This swift detection of ALPHV, along with Cyber AI Analyst™ autonomously investigating the wide array of post-compromise activity, provided the customer with full visibility over the attack enabling them to promptly initiate their remediation and recovery efforts.

Unfortunately, in this incident, Darktrace RESPOND™ was not fully deployed within their environment, hindering its ability to autonomously counter emerging threats. Had RESPOND been fully operational here, it would have effectively contained the attack in its early stages, avoiding the eventual detonation of the ALPHV ransomware.

Figure 1: Timeline of the ALPHV ransomware attack.

In mid-October, a member of the IT team at a US-based Darktrace customer attempted to install the network traffic analysis software, Wireshark, onto their desktop. Due to the customer’s configuration, Darktrace's visibility over this device was limited to its internal traffic, despite this it was still able to identify and alert for a string of suspicious activity conducted by the device.

Initially, Darktrace observed the device making type A DNS requests for 'wiki.wireshark[.]org' immediately before making type A DNS requests for the domain names 'www.googleadservices[.]com', 'allpcsoftware[.]com', and 'wireshhark[.]com' (note the two 'h's). This pattern of activity indicates that the device’s user was redirected to the website, wireshhark[.]com, as a result of the user's interaction with a sponsored Google Search result pointing to allpcsoftware[.]com.

At the time of analysis, navigating to wireshhark[.]com directly from the browser search bar led to a YouTube video of Rick Astley's song "Never Gonna Give You Up". This suggests that the website, wireshhark[.]com, had been configured to redirect users to this video unless they had arrived at the website via the relevant sponsored Google Search result [8].

Although it was not possible to confirm this with certainty, it is highly likely that users who visited the website via the appropriate sponsored Google Search result were led to a fake website (wireshhark[.]com) posing as the legitimate website, wireshark[.]com. It seems that the actors who set up this fake version of wireshark[.]com were inspired by the well-known bait-and-switch technique known as 'rickrolling', where users are presented with a desirable lure (typically a hyperlink of some kind) which unexpectedly leads them to a music video of Rick Astley's "Never Gonna Give You Up".

After being redirected to wireshhark[.]com, the user unintentionally installed a malware sample which dropped what appears to be Cobalt Strike onto their device. The presence of Cobalt Strike on the user's desktop was evidenced by the subsequent type A DNS requests which the device made for the domain name 'pse[.]ac'. These DNS requests were responded to with the likely Cobalt Strike C2 server address, 194.169.175[.]132. Given that Darktrace only had visibility over the device’s internal traffic, it did not observe any C2 connections to this Cobalt Strike endpoint. However, the desktop's subsequent behavior suggests that a malicious actor had gained 'hands-on-keyboard' control of the device via an established C2 channel.

Figure 2: Advanced Search data showing an customer device being tricked into visiting the fake website, wireshhark[.]com.

Since the malicious actor had gained control of an IT member's device, they were able to abuse the privileged account credentials to spread Python payloads across the network via SMB and the Windows Management Instrumentation (WMI) service. The actor was also seen distributing the Windows Sys-Internals tool, PsExec, likely in an attempt to facilitate their lateral movement efforts. It was normal for this IT member's desktop to distribute files across the network via SMB, which meant that this malicious SMB activity was not, at first glance, out of place.

Figure 3: Advanced Search data showing that it was normal for the IT member's device to distribute files over SMB.

However, Darktrace DETECT recognized that the significant spike in file writes being performed here was suspicious, even though, on the surface, it seemed ‘normal’ for the device. Furthermore, Darktrace identified that the executable files being distributed were attempting to masquerade as a different file type, potentially in an attempt to evade the detection of traditional security tools.

Figure 4: Event Log data showing several Model Breaches being created in response to the IT member's DEVICE's SMB writes of Python-based executables.

An addition to DETECT’s identification of this unusual activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing compromise and was able to link the SMB writes and the sharing of the executable Python payloads, viewing the connections as one lateral movement incident rather than a string of isolated events. After completing its investigation, Cyber AI Analyst was able to provide a detailed summary of events on one pane of glass, ensuring the customer could identify the affected device and begin their remediation.

Figure 5: Cyber AI Analyst investigation summary highlighting the IT member's desktop’s lateral movement activities.

C2 Activity

The Python payloads distributed by the IT member’s device were likely related to the Nitrogen malware, as evidenced by the payloads’ names and by the network behaviours which they engendered.  

Figure 6: Advanced Search data showing the affected device reaching out to the C2 endpoint, pse[.]ac, and then distributing Python-based executable files to an internal domain controller.

The internal devices to which these Nitrogen payloads were distributed immediately went on to contact C2 infrastructure associated with Cobalt Strike. These C2 connections were made over SSL on ports 443 and 8443.  Darktrace identified the attacker moving laterally to an internal SQL server and an internal domain controller.

Figure 7: Advanced Search data showing an internal SQL server contacting the Cobalt Strike C2 endpoint, 194.180.48[.]169, after receiving Python payloads from the IT member’s device.
Figure 8: Event Log data showing several DETECT model breaches triggering in response to an internal SQL server’s C2 connections to 194.180.48[.]169.

Once more, Cyber AI Analyst launched its own investigation into this activity and was able to successfully identify a series of separate SSL connections, linking them together into one wider C2 incident.

Figure 9: Cyber AI Analyst investigation summary highlighting C2 connections from the SQL server.

Darktrace observed the attacker using their 'hands-on-keyboard' access to these systems to elevate their privileges, conduct network reconnaissance (primarily port scanning), spread Python payloads further across the network, exfiltrate data from the domain controller and transfer a payload from GitHub to the domain controller.

Figure 10: Cyber AI Analyst investigation summary an IP address scan carried out by an internal domain controller.
Figure 12: Event Log data showing an internal domain controller contacting GitHub around the time that it was in communication with the C2 endpoint, 194.180.48[.]169.
Figure 13: Event Log data showing a DETECT model breach being created in response to an internal domain controller's large data upload to the C2 endpoint, 194.180.48[.]169.

After conducting extensive reconnaissance and lateral movement activities, the attacker was observed detonating ransomware with the organization's VMware environment, resulting in the successful encryption of the customer’s VMware vCenter server and VMware virtual machines. In this case, the attacker took around 24 hours to progress from initial access to ransomware detonation.  

If the targeted organization had been signed up for Darktrace's Proactive Threat Notification (PTN) service, they would have been promptly notified of these suspicious activities by the Darktrace Security Operations Center (SOC) in the first instance, allowing them to quickly identify affected devices and quarantine them before the compromise could escalate.

Additionally, given the quantity of high-severe alerts that triggered in response to this attack, Darktrace RESPOND would, under normal circumstances, have inhibited the attacker's activities as soon as they were identified by DETECT. However, due to RESPOND not being configured to act on server devices within the customer’s network, the attacker was able to seamlessly move laterally through the organization's server environment and eventually detonate the ALPHV ransomware.

Nevertheless, Darktrace was able to successfully weave together multiple Cyber AI Analyst incidents which it generated into a thread representing the chain of behavior that made up this attack. The thread of Incident Events created by Cyber AI Analyst provided a substantial account of the attack and the steps involved in it, which significantly facilitated the customer’s post-incident investigation efforts.  

Figure 14: Darktrace's AI Analyst weaved together 33 of the Incident Events it created together into a thread representing the attacker’s chain of behavior.

Conclusion

It is expected for malicious cyber actors to revise and upgrade their methods to evade organizations’ improving security measures. The continued improvement of email security tools, for example, has likely created a need for attackers to develop new means of Initial Access, such as the use of Microsoft Teams-based malware delivery.

This fast-paced ALPHV ransomware attack serves as a further illustration of this trend, with the actor behind the attack using malvertising to convince an unsuspecting user to download the Python-based malware, Nitrogen, from a fake Wireshark site. Unbeknownst to the user, this stealthy malware dropped a C2 implant onto the user’s device, giving the malicious actor the ‘hands-on-keyboard’ access they needed to move laterally, conduct network reconnaissance, and ultimately detonate ALPHV ransomware.

Despite the non-traditional initial access methods used by this ransomware actor, Darktrace DETECT was still able to identify the unusual patterns of network traffic caused by the attacker’s post-compromise activities. The large volume of alerts created by Darktrace DETECT were autonomously investigated by Darktrace’s Cyber AI Analyst, which was able to weave together related activities of different devices into a comprehensive timeline of the attacker’s operation. Given the volume of DETECT alerts created in response to this ALPHV attack, it is expected that Darktrace RESPOND would have autonomously inhibited the attacker’s operation had the capability been appropriately configured.

As the first post-compromise activities Darktrace observed in this ALPHV attack were seemingly performed by a member of the customer’s IT team, it may have looked normal to a human or traditional signature and rules-based security tools. To Darktrace’s Self-Learning AI, however, the observed activities represented subtle deviations from the device’s normal pattern of life. This attack, and Darktrace’s detection of it, is therefore a prime illustration of the value that Self-Learning AI can bring to the task of detecting anomalies within organizations’ digital estates.

Credit to Sam Lister, Senior Cyber Analyst, Emma Foulger, Principal Cyber Analyst

Appendices

Darktrace DETECT Model Breaches

- Compliance / SMB Drive Write

- Compliance / High Priority Compliance Model Breach

- Anomalous File / Internal / Masqueraded Executable SMB Write

- Device / New or Uncommon WMI Activity

- Anomalous Connection / New or Uncommon Service Control

- Anomalous Connection / High Volume of New or Uncommon Service Control

- Device / New or Uncommon SMB Named Pipe

- Device / Multiple Lateral Movement Model Breaches

- Device / Large Number of Model Breaches  

- SMB Writes of Suspicious Files (Cyber AI Analyst)

- Suspicious Remote WMI Activity (Cyber AI Analyst)

- Suspicious DCE-RPC Activity (Cyber AI Analyst)

- Compromise / Connection to Suspicious SSL Server

- Compromise / High Volume of Connections with Beacon Score

- Anomalous Connection / Suspicious Self-Signed SSL

- Anomalous Connection / Anomalous SSL without SNI to New External

- Compromise / Suspicious TLS Beaconing To Rare External

- Compromise / Beacon to Young Endpoint

- Compromise / SSL or HTTP Beacon

- Compromise / Agent Beacon to New Endpoint

- Device / Long Agent Connection to New Endpoint

- Compromise / SSL Beaconing to Rare Destination

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Slow Beaconing Activity To External Rare

- Anomalous Server Activity / Outgoing from Server

- Device / Multiple C2 Model Breaches

- Possible SSL Command and Control (Cyber AI Analyst)

- Unusual Repeated Connections (Cyber AI Analyst)

- Device / ICMP Address Scan

- Device / RDP Scan

- Device / Network Scan

- Device / Suspicious Network Scan Activity

- Scanning of Multiple Devices (Cyber AI Analyst)

- ICMP Address Scan (Cyber AI Analyst)

- Device / Anomalous Github Download

- Unusual Activity / Unusual External Data Transfer

- Device / Initial Breach Chain Compromise

MITRE ATT&CK Mapping

Resource Development techniques:

- Acquire Infrastructure: Malvertising (T1583.008)

Initial Access techniques:

- Drive-by Compromise (T1189)

Execution techniques:

- User Execution: Malicious File (T1204.002)

- System Services: Service Execution (T1569.002)

- Windows Management Instrumentation (T1047)

Defence Evasion techniques:

- Masquerading: Match Legitimate Name or Location (T1036.005)

Discovery techniques:

- Remote System Discovery (T1018)

- Network Service Discovery (T1046)

Lateral Movement techniques:

- Remote Services: SMB/Windows Admin Shares

- Lateral Tool Transfer (T1570)

Command and Control techniques:

- Application Layer Protocol: Web Protocols (T1071.001)

- Encrypted Channel: Asymmetric Cryptography (T1573.002)

- Non-Standard Port (T1571)

- Ingress Tool Channel (T1105)

Exfiltration techniques:

- Exfiltration Over C2 Channel (T1041)

Impact techniques:

- Data Encrypted for Impact (T1486)

List of Indicators of Compromise

- allpcsoftware[.]com

- wireshhark[.]com

- pse[.]ac • 194.169.175[.]132

- 194.180.48[.]169

- 193.42.33[.]14

- 141.98.6[.]195

References  

[1] https://darktrace.com/threat-report-2023

[2] https://www.microsoft.com/en-us/security/blog/2022/05/09/ransomware-as-a-service-understanding-the-cybercrime-gig-economy-and-how-to-protect-yourself/

[3] https://www.bleepingcomputer.com/news/security/alphv-blackcat-this-years-most-sophisticated-ransomware/

[4] https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-353a

[5] https://www.justice.gov/opa/pr/justice-department-disrupts-prolific-alphvblackcat-ransomware-variant

[6] https://www.state.gov/u-s-department-of-state-announces-reward-offers-for-criminal-associates-of-the-alphv-blackcat-ransomware-variant/

[7] https://www.bleepingcomputer.com/news/security/blackcat-alphv-ransomware-linked-to-blackmatter-darkside-gangs/

[8] https://www.trendmicro.com/en_us/research/23/f/malvertising-used-as-entry-vector-for-blackcat-actors-also-lever.html

[9] https://news.sophos.com/en-us/2023/07/26/into-the-tank-with-nitrogen/

[10] https://www.esentire.com/blog/persistent-connection-established-nitrogen-campaign-leverages-dll-side-loading-technique-for-c2-communication

[11] https://www.esentire.com/blog/nitrogen-campaign-2-0-reloads-with-enhanced-capabilities-leading-to-alphv-blackcat-ransomware

[12] https://www.esentire.com/blog/the-notorious-alphv-blackcat-ransomware-gang-is-attacking-corporations-and-public-entities-using-google-ads-laced-with-malware-warns-esentire

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
Sam Lister
Specialist Security Researcher

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 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.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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