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Mitigating Sectop RAT: Darktrace's Strategy

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20
Nov 2023
20
Nov 2023
Read how Darktrace tackled the Sectop Remote Access Trojan. Gain insights into their advanced threat mitigation strategies.

Introduction

As malicious actors across the threat landscape continue to look for new ways to gain unauthorized access to target networks, it is unsurprising to see Remote Access Trojans (RATs) leveraged more and more. These RATs are downloaded discretely without the target’s knowledge, typically through seemingly legitimate software downloads, and are designed to gain highly privileged network credentials, ultimately allowing attackers to have remote control over compromised devices. [1]

SectopRAT is one pertinent example of a RAT known to adopt a number of stealth functions in order to gather and exfiltrate sensitive data from its targets including passwords, cookies, autofill and history data stores in browsers, as well as cryptocurrency wallet details and system hardware information. [2]

In early 2023, Darktrace identified a resurgence of the SectopRAT across customer environments, primarily targeting educational industries located in the United States (US), Europe, the Middle East and Africa (EMEA) and Asia-Pacific (APAC) regions. Darktrace DETECT™ was able to successfully identify suspicious activity related to SectopRAT at the network level, as well as any indicators of post-compromise on customer environments that did not have Darktrace RESPOND™ in place to take autonomous preventative action.

What is SectopRAT?

First discovered in early 2019, the SectopRAT is a .NET RAT that contains information stealing capabilities. It is also known under the alias ‘ArechClient2’, and is commonly distributed through drive-by downloads of illegitimate software and utilizes malvertising, including via Google Ads, to increase the chances of it being downloaded.

The malware’s code was updated at the beginning of 2021, which led to refined and newly implemented features, including command and control (C2) communication encryption with Advanced Encryption Stanard 256 (AES256) and additional commands. SectopRAT also has a function called "BrowserLogging", ultimately sending any actions it conducts on web browsers to its C2 infrastructure. When the RAT is executed, it then connects to a Pastebin associated hostname to retrieve C2 information; the requested file reaches out to get the public IP address of the infected device. To receive commands, it connects to its C2 server primarily on port 15647, although other ports have been highlighted by open source intelligence (OSINT), which include 15678, 15649, 228 and 80. Ultimately, sensitive data data gathered from target networks is then exfiltrated to the attacker’s C2 infrastructure, typically in a JSON file [3].

Darktrace Coverage

During autonomous investigations into affected customer networks, Darktrace DETECT was able to identify SSL connections to the endpoint pastebin[.]com over port 443, followed by failed connections to one of the IPs and ports (i.e., 15647, 15648, 15649) associated with SectopRAT. This resulted in the devices breaching the ‘Compliance/Pastebin and Anomalous Connection/Multiple Failed Connections to Rare Endpoint’ models, respectively.

In some instances, Darktrace observed a higher number of attempted connections that resulted in the additional breach of the model ‘Compromise / Large Number of Suspicious Failed Connections’.

Over a period of three months, Darktrace investigated multiple instances of SectopRAT infections across multiple clients, highlighting indicators of compromise (IoCs) through related endpoints.Looking specififically at one customer’s activity which centred on January 25, 2023, one device was observed initially making suspicious connections to a Pastebin endpoint, 104.20.67[.]143, likely in an attempt to receive C2 information.

Darktrace DETECT recognized this activity as suspicious, causing the 'Compliance / Pastebin' DETECT models to breach. In response to this detection, Darktrace RESPOND took swift action against the Pastebin connections by blocking them and preventing the device from carrying out further connections with Pastebin endpoints. Darktrace RESPOND actions related to blocking Pastebin connections were commonly observed on this device throughout the course of the attack and likely represented threat actors attempting to exfiltrate sensitive data outside the network.

Darktrace UI image
Figure 1: Model breach event log highlighting the Darktrace DETECT model breach ‘Compliance / Pastebin’.

Around the same time, Darktrace observed the device making a large number of failed connections to an unusual exernal location in the Netherlands, 5.75.147[.]135, via port 15647. Darktrace recognized that this endpoint had never previously been observed on the customer’s network and that the frequency of the failed connections could be indicative of beaconing activity. Subsequent investigation into the endpoint using OSINT indicated it had links to malware, though Darktrace’s successful detection did not need to rely on this intelligence.

Darktrace model breach event log
Figure 2: Model breach event log highlighting the multiple failed connectiosn to the suspicious IP address, 5.75.147[.]135 on January 25, 2023, causing the Darktrace DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach.

After these initial set of breaches on January 25, the same device was observed engaging in further external connectivity roughly a month later on February 27, including additional failed connections to the IP 167.235.134[.]14 over port 15647. Once more, multiple OSINT sources revealed that this endpoint was indeed a malicious C2 endpoint.

Darktrace model breach event log 2
Figure 3: Model breach event log highlighting the multiple failed connectiosn to the suspicious IP address, 167.235.134[.]14 on February 27, 2023, causing the Darktrace DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach.

While the initial Darktrace coverage up to this point has highlighted the attempted C2 communication and how DETECT was able to alert on the suspicious activity, Pastebin activity was commonly observed throughout the course of this attack. As a result, when enabled in autonomous response mode, Darktrace RESPOND was able to take swift mitigative action by blocking all connections to Pastebin associated hostnames and IP addresses. These interventions by RESPOND ultimately prevented malicious actors from stealing sensitive data from Darktrace customers.

Darktrace RESPOND action list
Figure 4: A total of nine Darktrace RESPOND actions were applied against suspicious Pastebin activity during the course of the attack.

In another similar case investigated by the Darktrace, multiple devices were observed engaging in external connectivity to another malicious endpoint,  88.218.170[.]169 (AS207651 Hosting technology LTD) on port 15647.  On April 17, 2023, at 22:35:24 UTC, the breach device started making connections; of the 34 attempts, one connection was successful – this connection lasted 8 minutes and 49 seconds. Darktrace DETECT’s Self-Learning AI understood that these connections represented a deviation from the device’s usual pattern of behavior and alerted on the activity with the ‘Multiple Connections to new External TCP Port’ model.

Darktrace model breach event log
Figure 5: Model breach event log highlighting the affected device successfully connecting to the suspicious endpoint, 88.218.170[.]169.
Darktrace advanced search query
Figure 6: Advanced Search query highlighting the one successful connection to the endpoint 88.218.170[.]169 out of the 34 attempted connections.

A few days later, on April 20, 2023, at 12:33:59 (UTC) the source device connected to a Pastebin endpoint, 172.67.34[.]170 on port 443 using the SSL protocol, that had never previously be seen on the network. According to Advanced Search data, the first SSL connection lasted over two hours. In total, the device made 9 connections to pastebin[.]com and downloaded 85 KB of data from it.

Darktrace UI highlighting connections
Figure 7: Screenshot of the Darktrace UI highlighting the affected device making multiple connections to Pastebin and downloading 85 KB of data.

Within the same minute, Darktrace detected the device beginning to make a large number of failed connections to another suspicious endpoints, 34.107.84[.]7 (AS396982 GOOGLE-CLOUD-PLATFORM) via port 15647. In total the affected device was observed initiating 1,021 connections to this malicious endpoint, all occurring over the same port and resulting the failed attempts.

Darktrace advanced search query 2
Figure 8: Advanced Search query highlighting the affected device making over one thousand connections to the suspicious endpoint 34.107.84[.]7, all of which failed.

Conclusion

Ultimately, thanks to its Self-Learning AI and anomaly-based approach to threat detection, Darktrace was able to preemptively identify any suspicious activity relating to SectopRAT at the network level, as well as post-compromise activity, and bring it to the immediate attention of customer security teams.

In addition to the successful and timely detection of SectopRAT activity, when enabled in autonomous response mode Darktrace RESPOND was able to shut down suspicious connections to endpoints used by threat actors as malicious infrastructure, thus preventing successful C2 communication and potential data exfiltration.

In the face of a Remote Access Trojan, like SectopRAT, designed to steal sensitive corporate and personal information, the Darktrace suite of products is uniquely placed to offer organizations full visibility over any emerging activity on their networks and respond to it without latency, safeguarding their digital estate whilst causing minimal disruption to business operations.

Credit to Justin Torres, Cyber Analyst, Brianna Leddy, Director of Analysis

Appendices

Darktrace Model Detection:

  • Compliance / Pastebin
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Large Number of Suspicious Failed Connections
  • Anomalous Connection / Multiple Connections to New External TCP Port

List of IoCs

IoC - Type - Description + Confidence

5.75.147[.]135 - IP - SectopRAT C2 Endpoint

5.75.149[.]1 - IP - SectopRAT C2 Endpoint

34.27.150[.]38 - IP - SectopRAT C2 Endpoint

34.89.247[.]212 - IP - SectopRAT C2 Endpoint

34.107.84[.]7 - IP - SectopRAT C2 Endpoint

34.141.16[.]89 - IP - SectopRAT C2 Endpoint

34.159.180[.]55 - IP - SectopRAT C2 Endpoint

35.198.132[.]51 - IP - SectopRAT C2 Endpoint

35.226.102[.]12 - IP - SectopRAT C2 Endpoint

35.234.79[.]173 - IP - SectopRAT C2 Endpoint

35.234.159[.]213 - IP - SectopRAT C2 Endpoint

35.242.150[.]95 - IP - SectopRAT C2 Endpoint

88.218.170[.]169 - IP - SectopRAT C2 Endpoint

162.55.188[.]246 - IP - SectopRAT C2 Endpoint

167.235.134[.]14 - IP - SectopRAT C2 Endpoint

MITRE ATT&CK Mapping

Model: Compliance / Pastebin

ID: T1537

Tactic: EXFILTRATION

Technique Name: Transfer Data to Cloud Account

Model: Anomalous Connection / Multiple Failed Connections to Rare Endpoint

ID: T1090.002

Sub technique of: T1090

Tactic: COMMAND AND CONTROL

Technique Name: External Proxy

ID: T1095

Tactic: COMMAND AND CONTROL

Technique Name: Non-Application Layer Protocol

ID: T1571

Tactic: COMMAND AND CONTROL

Technique Name: Non-Standard Port

Model: Compromise / Large Number of Suspicious Failed Connections

ID: T1571

Tactic: COMMAND AND CONTROL

Technique Name: Non-Standard Port

ID: T1583.006

Sub technique of: T1583

Tactic: RESOURCE DEVELOPMENT

Technique Name: Web Services

Model: Anomalous Connection / Multiple Connections to New External TCP Port

ID: T1095        

Tactic: COMMAND AND CONTROL    

Technique Name: Non-Application Layer Protocol

ID: T1571

Tactic: COMMAND AND CONTROL    

Technique Name: Non-Standard Port

References

1.     https://www.techtarget.com/searchsecurity/definition/RAT-remote-access-Trojan

2.     https://malpedia.caad.fkie.fraunhofer.de/details/win.sectop_rat

3.     https://threatfox.abuse.ch/browse/malware/win.sectop_rat

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.
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Justin Torres
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Inside the SOC

Disarming the WarmCookie Backdoor: Darktrace’s Oven-Ready Solution

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26
Jul 2024

What is WarmCookie malware?

WarmCookie, also known as BadSpace [2], is a two-stage backdoor tool that provides functionality for threat actors to retrieve victim information and launch additional payloads. The malware is primarily distributed via phishing campaigns according to multiple open-source intelligence (OSINT) providers.

Backdoor malware: A backdoor tool is a piece of software used by attackers to gain and maintain unauthorized access to a system. It bypasses standard authentication and security mechanisms, allowing the attacker to control the system remotely.

Two-stage backdoor malware: This means the backdoor operates in two distinct phases:

1. Initial Stage: The first stage involves the initial infection and establishment of a foothold within the victim's system. This stage is often designed to be small and stealthy to avoid detection.

2. Secondary Stage: Once the initial stage has successfully compromised the system, it retrieves or activates the second stage payload. This stage provides more advanced functionalities for the attacker, such as extensive data exfiltration, deeper system control, or the deployment of additional malicious payloads.

How does WarmCookie malware work?

Reported attack patterns include emails attempting to impersonate recruitment firms such as PageGroup, Michael Page, and Hays. These emails likely represented social engineering tactics, with attackers attempting to manipulate jobseekers into engaging with the emails and following malicious links embedded within [3].

This backdoor tool also adopts stealth and evasion tactics to avoid the detection of traditional security tools. Reported evasion tactics included custom string decryption algorithms, as well as dynamic API loading to prevent researchers from analyzing and identifying the core functionalities of WarmCookie [1].

Before this backdoor makes an outbound network request, it is known to capture details from the target machine, which can be used for fingerprinting and identification [1], this includes:

- Computer name

- Username

- DNS domain of the machine

- Volume serial number

WarmCookie samples investigated by external researchers were observed communicating communicated over HTTP to a hardcoded IP address using a combination of RC4 and Base64 to protect its network traffic [1]. Ultimately, threat actors could use this backdoor to deploy further malicious payloads on targeted networks, such as ransomware.

Darktrace Coverage of WarmCookie

Between April and June 2024, Darktrace’s Threat Research team investigated suspicious activity across multiple customer networks indicating that threat actors were utilizing the WarmCookie backdoor tool. Observed cases across customer environments all included the download of unusual executable (.exe) files and suspicious outbound connectivity.

Affected devices were all observed making external HTTP requests to the German-based external IP, 185.49.69[.]41, and the URI, /data/2849d40ade47af8edfd4e08352dd2cc8.

The first investigated instance occurred between April 23 and April 24, when Darktrace detected a a series of unusual file download and outbound connectivity on a customer network, indicating successful WarmCookie exploitation. As mentioned by Elastic labs, "The PowerShell script abuses the Background Intelligent Transfer Service (BITS) to download WarmCookie and run the DLL with the Start export" [1].

Less than a minute later, the same device was observed making HTTP requests to the rare external IP address: 185.49.69[.]41, which had never previously been observed on the network, for the URI /data/b834116823f01aeceed215e592dfcba7. The device then proceeded to download masqueraded executable file from this endpoint. Darktrace recognized that these connections to an unknown endpoint, coupled with the download of a masqueraded file, likely represented malicious activity.

Following this download, the device began beaconing back to the same IP, 185.49.69[.]41, with a large number of external connections observed over port 80.  This beaconing related behavior could further indicate malicious software communicating with command-and-control (C2) servers.

Darktrace’s model alert coverage included the following details:

[Model Alert: Device / Unusual BITS Activity]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:10:23 UTC

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Microsoft BITS/7.8

[Model Alert: Anomalous File / EXE from Rare External Location]

[Model Alert: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:11:18 UTC

- Destination IP: 185.49.69[.]41

- Destination port: 80

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)

- Event details: File: http[:]//185.49.69[.]41/data/b834116823f01aeceed215e592dfcba7, total seen size: 144384B, direction: Incoming

- SHA1 file hash: 4ddf0d9c750bfeaebdacc14152319e21305443ff

- MD5 file hash: b09beb0b584deee198ecd66976e96237

[Model Alert: Compromise / Beaconing Activity To External Rare]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:15:24 UTC

- Destination IP: 185.49.69[.]41

- Destination port: 80

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH  

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)

Between May 7 and June 4, Darktrace identified a wide range of suspicious external connectivity on another customer’s environment. Darktrace’s Threat Research team further investigated this activity and assessed it was likely indicative of WarmCookie exploitation on customer devices.

Similar to the initial use case, BITS activity was observed on affected devices, which is utilized to download WarmCookie [1]. This initial behavior was observed with the device after triggering the model: Device / Unusual BITS Activity on May 7.

Just moments later, the same device was observed making HTTP requests to the aforementioned German IP address, 185.49.69[.]41 using the same URI /data/2849d40ade47af8edfd4e08352dd2cc8, before downloading a suspicious executable file.

Just like the first use case, this device followed up this suspicious download with a series of beaconing connections to 185.49.69[.]41, again with a large number of connections via port 80.

Similar outgoing connections to 185.49.69[.]41 and model alerts were observed on additional devices during the same timeframe, indicating that numerous customer devices had been compromised.

Darktrace’s model alert coverage included the following details:

[Model Alert: Device / Unusual BITS Activity]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:23 UTC

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Microsoft BITS/7.8

[Model Alert: Anomalous File / EXE from Rare External Location]

[Model Alert: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:35 UTC  

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- ASN: AS28753 Leaseweb Deutschland GmbH

- Event details: File: http[:]//185.49.69[.]41/data/2849d40ade47af8edfd4e08352dd2cc8, total seen size: 72704B, direction: Incoming

- SHA1 file hash: 5b0a35c574ee40c4bccb9b0b942f9a9084216816

- MD5 file hash: aa9a73083184e1309431b3c7a3e44427  

[Model Alert: Anomalous Connection / New User Agent to IP Without Hostname]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:04:14 UTC  

- Destination IP: 185.49.69[.]41  

- Application protocol: HTTP  

- URI: /data/2849d40ade47af8edfd4e08352dd2cc8

- User agent: Microsoft BITS/7.8  

[Model Alert: Compromise / HTTP Beaconing to New Endpoint]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:08:47 UTC

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- Application protocol: HTTP  

- ASN: AS28753 Leaseweb Deutschland GmbH  

- URI: /  

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705) \

Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
Figure 1: Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.
Figure 2: External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.

Fortunately, this particular customer was subscribed to Darktrace’s Proactive Threat Notification (PTN) service and the Darktrace Security Operation Center (SOC) promptly investigated the activity and alerted the customer. This allowed their security team to address the activity and begin their own remediation process.

In this instance, Darktrace’s Autonomous Response capability was configured in Human Confirmation mode, meaning any mitigative actions required manual application by the customer’s security team.

Despite this, Darktrace recommended two actions to contain the activity: blocking connections to the suspicious IP address 185.49.69[.]41 and any IP addresses ending with '69[.]41', as well as the ‘Enforce Pattern of Life’ action. By enforcing a pattern of life, Darktrace can restrict a device (or devices) to its learned behavior, allowing it to continue regular business activities uninterrupted while blocking any deviations from expected activity.

Actions suggested by Darktrace to contain the emerging activity, including blocking connections to the suspicious endpoint and restricting the device to its ‘pattern of life’.
Figure 3: Actions suggested by Darktrace to contain the emerging activity, including blocking connections to the suspicious endpoint and restricting the device to its ‘pattern of life’.

Conclusion

Backdoor tools like WarmCookie enable threat actors to gather and leverage information from target systems to deploy additional malicious payloads, escalating their cyber attacks. Given that WarmCookie’s primary distribution method seems to be through phishing campaigns masquerading as trusted recruitments firms, it has the potential to affect a large number of organziations.

In the face of such threats, Darktrace’s behavioral analysis provides organizations with full visibility over anomalous activity on their digital estates, regardless of whether the threat bypasses by human security teams or email security tools. While threat actors seemingly managed to evade customers’ native email security and gain access to their networks in these cases, Darktrace identified the suspicious behavior associated with WarmCookie and swiftly notified customer security teams.

Had Darktrace’s Autonomous Response capability been fully enabled in these cases, it could have blocked any suspicious connections and subsequent activity in real-time, without the need of human intervention, effectively containing the attacks in the first instance.

Credit to Justin Torres, Cyber Security Analyst and Dylan Hinz, Senior Cyber Security Analyst

Appendices

Darktrace Model Detections

- Anomalous File / EXE from Rare External Location

- Anomalous File / Masqueraded File Transfer  

- Compromise / Beacon to Young Endpoint  

- Compromise / Beaconing Activity To External Rare  

- Compromise / HTTP Beaconing to New Endpoint  

- Compromise / HTTP Beaconing to Rare Destination

- Compromise / High Volume of Connections with Beacon Score

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Quick and Regular Windows HTTP Beaconing

- Compromise / SSL or HTTP Beacon

- Compromise / Slow Beaconing Activity To External Rare

- Compromise / Sustained SSL or HTTP Increase

- Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

- Anomalous Connection / Multiple Failed Connections to Rare Endpoint

- Anomalous Connection / New User Agent to IP Without Hostname

- Compromise / Sustained SSL or HTTP Increase

AI Analyst Incident Coverage:

- Unusual Repeated Connections

- Possible SSL Command and Control to Multiple Endpoints

- Possible HTTP Command and Control

- Suspicious File Download

Darktrace RESPOND Model Detections:

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

- Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

List of IoCs

IoC - Type - Description + Confidence

185.49.69[.]41 – IP Address – WarmCookie C2 Endpoint

/data/2849d40ade47af8edfd4e08352dd2cc8 – URI – Likely WarmCookie URI

/data/b834116823f01aeceed215e592dfcba7 – URI – Likely WarmCookie URI

4ddf0d9c750bfeaebdacc14152319e21305443ff  - SHA1 Hash  – Possible Malicious File

5b0a35c574ee40c4bccb9b0b942f9a9084216816  - SHA1 Hash – Possiblem Malicious File

MITRE ATT&CK Mapping

(Technique Name) – (Tactic) – (ID) – (Sub-Technique of)

Drive-by Compromise - INITIAL ACCESS - T1189

Ingress Tool Transfer - COMMAND AND CONTROL - T1105

Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

Lateral Tool Transfer - LATERAL MOVEMENT - T1570

Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

Web Services - RESOURCE DEVELOPMENT - T1583.006 - T1583

Browser Extensions - PERSISTENCE - T1176

Application Layer Protocol - COMMAND AND CONTROL - T1071

Fallback Channels - COMMAND AND CONTROL - T1008

Multi-Stage Channels - COMMAND AND CONTROL - T1104

Non-Standard Port - COMMAND AND CONTROL - T1571

One-Way Communication - COMMAND AND CONTROL - T1102.003 - T1102

Encrypted Channel - COMMAND AND CONTROL - T1573

External Proxy - COMMAND AND CONTROL - T1090.002 - T1090

Non-Application Layer Protocol - COMMAND AND CONTROL - T1095

References

[1] https://www.elastic.co/security-labs/dipping-into-danger

[2] https://www.gdatasoftware.com/blog/2024/06/37947-badspace-backdoor

[3] https://thehackernews.com/2024/06/new-phishing-campaign-deploys.html

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Justin Torres
Cyber Analyst

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Thought Leadership

The State of AI in Cybersecurity: Understanding AI Technologies

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24
Jul 2024

About the State of AI Cybersecurity Report

Darktrace 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.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners”. This blog will focus on security professionals’ understanding of AI technologies in cybersecurity tools.

To access download the full report, click here.

How familiar are security professionals with supervised machine learning

Just 31% of security professionals report that they are “very familiar” with supervised machine learning.

Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).

Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.

Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.  

If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.  

Different types of AI

Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.  

Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.  

Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.  

Natural language processing (NLP): Applies computational techniques to process and understand human language.  

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.  

What impact will generative AI have on the cybersecurity field?

More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.

Chart showing the types of AI expected to impact security the most
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report

Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.

AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.

Do security professionals fully understand the different types of AI in security products?

Only 26% of security professionals report a full understanding of the different types of AI in use within security products.

Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.

Key findings include:

  • Executives and managers report higher understanding than practitioners.
  • Larger organizations have better understanding due to greater specialization.

As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.

To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.  

Do security professionals believe generative AI alone is enough to stop zero-day threats?

No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats

This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.

Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.

Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.

To access the full report, click here.

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