The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners
Part 1: This blog outlines Darktrace’s State of AI Cybersecurity research report, showing key findings from our global survey, covering the impacts AI has on the cyber threat landscape, cyber security solutions, and perceptions and priorities for security practitioners.
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
Mitchell Bezzina
VP, Product and Solutions Marketing
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09
Apr 2024
What is the State of AI Cybersecurity Report?
We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.
Here are some of the key findings from the report:
What is the impact of AI on the cyber threat landscape?
Today’s security stakeholders are already seeing AI’s impact on the threat landscape.
"74% of survey respondents agree that AI-powered cyber threats are having a significant impact on their organizations. However, 60% of respondents fear that their organizations are not adequately prepared to defend against AI-powered threats and attacks."
How is AI being applied in cyber-attacks?
Generative AI can be used to create large volumes of highly personalized phishing attacks and to change the signatures and hashes associated with malware files. Other AI tools can also scan environments for exploitable vulnerabilities.
However, operationalizing AI in a cyber-attack requires sophistication. In most cases, attackers tend to begin using AI by addressing the simplest use cases or “lowest-hanging fruit.”
Identifying exactly when and where AI is being applied is not always possible since there are few methods for doing so. Thus, defenders will need to focus their effort on preparing for threats that are coming at them faster than ever before.
How does AI affect cyber risk?
"71% of organizations have already taken strides to reduce the risks that come with AI’s adoption."
In terms of cyber risk, adopting AI technologies into the business also generates concern for industry professionals given the increased risk of exposing sensitive or proprietary information through employee use of third-party generative AI tools. The access to publicly-available, text-based generative AI systems to increase productivity opens the door to “shadow AI” in which individuals use these popular AI tools without organizational approval or oversight.
What is the impact of AI on cybersecurity solutions?
AI is poised to transform not just the threat landscape but the solution landscape as well, a fact defenders understand.
"95% of cybersecurity professionals agree that AI-powered solutions will level up their organizations’ defenses."
Survey participants believe that AI-powered security solutions are a must-have for countering the risks posed by AI-powered threats. However, cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the increasing demands. But not all AI is created equal, and not all these solutions live up to the widespread hype.
"Improving threat detection (57%) and identifying exploitable vulnerabilities (50%) are the top ranked areas where respondents believe AI will make an impact."
However, survey participants may not fully understand how AI is applied to these aspects of cybersecurity. For example, generativeAI actually has little to no role to play in threat detection and proactive attack surface management. Generative AI does accelerate the data retrieval process within threat detection, can create quick incident summaries, automate low level tasks, and simulate phishing emails, but it does not improve the ability to detect novel attacks.
Understanding AI technologies in cybersecurity
A worldwide preoccupation with generativeAI may have colored perceptions of what AI is and where it’s most effectively applied.
"Only 26% of security professionals report a full understanding of the different types of AI in use within security products."
As the AI revolution unfolds, the speed at which vendors are introducing new AI-powered solutions far outpaces the rate at which practitioners are being trained how to use them.
There’s a strong need for greater vendor transparency, as well as efforts to educate end users so that they can better understand the technologies they are deploying.
Types of AI in cybersecurity
Supervised machine learning: Applied more often than any other type of AI in cybersecurity.Trained on human attack patterns and historical threat intelligence.
Natural language processing (NLP): Applies computational techniques to process and understand human language.
Large language models (LLMs): Applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. Used in generative AI tools. The integrity of their output depends upon the quality of the data on which they were trained.
Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.
The more attention AI technology gets in cybersecurity, the higher expectations tend to be. As leaders and practitioners discover more about AI, they will need to learn when and where to use it – and how to offset the potential risks that various models and approaches can bring.
Cybersecurity practitioners’ priorities and objectives
Although security stakeholders are aware that the rise of AI will require them to implement new tools and deploy more advanced capabilities in certain areas, they still entertain multiple different – and sometimes conflicting – opinions about planning for the future.
"88% of cybersecurity professionals prefer a platform approach over individual point products."
Respondents expressed a strong preference for a platform- centric approach in their cybersecurity solution stacks. This is undoubtedly due to a far-reaching desire to reduce cost and complexity.
Even more widespread was agreement that organizations prefer to purchase new security capabilities within a broader platform rather than as individual point products.
"Top priorities for improving their ability to defend against AI-driven threats include adding AI-powered tools to their solution stacks and improving toolset integration."
Many security teams are looking to their existing vendors first when thinking about adding AI-powered tools to their solution stack. This may be because:
It takes more time and effort to replace existing tooling than it does to add onto the exiting stack.
Trust has already been established within existing relationships. As long as this is valued, there will always be a need to integrate AI and non-AI solutions.
Download the report for more statistics and insight on the state of AI in cybersecurity.
Learn more about AI can help you secure your enterprise
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.
Always On, Always Defending: Inside the AI-Driven SOC
Security leaders from global organizations share how the SOC is being redefined under growing pressure. In this roundtable blog, they explore challenges facing today’s SOCs and how AI is transforming operations to drive resilience, efficiency, and business growth.
Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining
Introduction
In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.
As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution. Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.
This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.
The campaign
In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.
Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].
These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.
Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].
Case Study 1
Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.
Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.
This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.
Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.
Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.
Figure 3: File transfer event showing the download of an executable from the rare external endpoint 188.241.219[.]55.
Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.
Darktrace’s Autonomous Response
Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.
Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:
Blocked connections to the associated ports and external endpoints
Prevented all outgoing network connections from the device
Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.
Case Study 2
While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.
One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.
Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.
Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.
Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.
On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.
Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.
While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.
Darktrace’s Autonomous Response
For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.
Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.
Suspected cryptomining activity
As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.
Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.
On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.
Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.
The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.
Conclusion
Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.
Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.
The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.
Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst) Edited by Ryan Traill (Content Manager)