Blog
/
/
April 9, 2024

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.
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
Mitchell Bezzina
VP, Product and Solutions Marketing
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
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:

  1. It takes more time and effort to replace existing tooling than it does to add onto the exiting stack.
  2. 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

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
Mitchell Bezzina
VP, Product and Solutions Marketing

Blog

/

/

March 5, 2026

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

Forensic Acquisition and investigationDefault blog imageDefault blog image

Investigating cloud attacks with Darktrace/ Forensic Acquisition & Investigation

Darktrace / Forensic Acquisition & Investigation™ is the industry’s first truly automated forensic solution purpose-built for the cloud. This blog will demonstrate how an investigation can be carried out against a compromised cloud server in minutes, rather than hours or days.

The compromised server investigated in this case originates from Darktrace’s Cloudypots system, a global honeypot network designed to observe adversary activity in real time across a wide range of cloud services. Whenever an attacker successfully compromises one of these honeypots, a forensic copy of the virtual server's disk is preserved for later analysis. Using Forensic Acquisition & Investigation, analysts can then investigate further and obtain detailed insights into the compromise including complete attacker timelines and root cause analysis.

Forensic Acquisition & Investigation supports importing artifacts from a variety of sources, including EC2 instances, ECS, S3 buckets, and more. The Cloudypots system produces a raw disk image whenever an attack is detected and stores it in an S3 bucket. This allows the image to be directly imported into Forensic Acquisition & Investigation using the S3 bucket import option.

As Forensic Acquisition & Investigation runs cloud-natively, no additional configuration is required to add a specific S3 bucket. Analysts can browse and acquire forensic assets from any bucket that the configured IAM role is permitted to access. Operators can also add additional IAM credentials, including those from other cloud providers, to extend access across multiple cloud accounts and environments.

Figure 1: Forensic Acquisition & Investigation import screen.

Forensic Acquisition & Investigation then retrieves a copy of the file and automatically begins running the analysis pipeline on the artifact. This pipeline performs a full forensic analysis of the disk and builds a timeline of the activity that took place on the compromised asset. By leveraging Forensic Acquisition & Investigation’s cloud-native analysis system, this process condenses hour of manual work into just minutes.

Successful import of a forensic artifact and initiation of the analysis pipeline.
Figure 2: Successful import of a forensic artifact and initiation of the analysis pipeline.

Once processing is complete, the preserved artifact is visible in the Evidence tab, along with a summary of key information obtained during analysis, such as the compromised asset’s hostname, operating system, cloud provider, and key event count.

The Evidence overview showing the acquired disk image.
Figure 3: The Evidence overview showing the acquired disk image.

Clicking on the “Key events” field in the listing opens the timeline view, automatically filtered to show system- generated alarms.

The timeline provides a chronological record of every event that occurred on the system, derived from multiple sources, including:

  • Parsed log files such as the systemd journal, audit logs, application specific logs, and others.
  • Parsed history files such as .bash_history, allowing executed commands to be shown on the timeline.
  • File-specific events, such as files being created, accessed, modified, or executables being run, etc.

This approach allows timestamped information and events from multiple sources to be aggregated and parsed into a single, concise view, greatly simplifying the data review process.

Alarms are created for specific timeline events that match either a built-in system rule, curated by Darktrace’s Threat Research team or an operator-defined rule  created at the project level. These alarms help quickly filter out noise and highlight on events of interest, such as the creation of a file containing known malware, access to sensitive files like Amazon Web Service (AWS) credentials, suspicious arguments or commands, and more.

 The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.
Figure 4: The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.

In this case, several alarms were generated for suspicious Base64 arguments being passed to Selenium. Examining the event data, it appears the attacker spawned a Selenium Grid session with the following payload:

"request.payload": "[Capabilities {browserName: chrome, goog:chromeOptions: {args: [-cimport base64;exec(base64...], binary: /usr/bin/python3, extensions: []}, pageLoadStrategy: normal}]"

This is a common attack vector for Selenium Grid. The chromeOptions object is intended to specify arguments for how Google Chrome should be launched; however, in this case the attacker has abused the binary field to execute the Python3 binary instead of Chrome. Combined with the option to specify command-line arguments, the attacker can use Python3’s -c option to execute arbitrary Python code, in this instance, decoding and executing a Base64 payload.

Selenium’s logs truncate the Arguments field automatically, so an alternate method is required to retrieve the full payload. To do this, the search bar can be used to find all events that occurred around the same time as this flagged event.

Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].
Figure 5: Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].

Scrolling through the search results, an entry from Java’s systemd journal can be identified. This log contains the full, unaltered payload. GCHQ’s CyberChef can then be used to decode the Base64 data into the attacker’s script, which will ultimately be executed.

Decoding the attacker’s payload in CyberChef.
Figure 6: Decoding the attacker’s payload in CyberChef.

In this instance, the malware was identified as a variant of a campaign that has been previously documented in depth by Darktrace.

Investigating Perfctl Malware

This campaign deploys a malware sample known as ‘perfctl to the compromised host. The script executed by the attacker downloads a Go binary named “promocioni.php” from 200[.]4.115.1. Its functionality is consistent with previously documented perfctl samples, with only minor changes such as updated filenames and a new command-and-control (C2) domain.

Perfctl is a stealthy malware that has several systems designed  to evade detection. The main binary is packed with UPX, with the header intentionally tampered with to prevent unpacking using regular tools. The binary also avoids executing any malicious code if it detects debugging or tracing activity, or if artifacts left by earlier stages are missing.

To further aid its evasive capabilities, perfctl features a usermode rootkit using an LD preload. This causes dynamically linked executables to load perfctl’s rootkit payload before other system modules, allowing it to override functions, such as intercepting calls to list files and hiding output from the returned list. Perfctl uses this to hide its own files, as well as other files like the ld.so.preload file, preventing users from identifying that a rootkit is present in the first place.

This also makes it difficult to dynamically analyze, as even analysts aware of the rootkit will struggle to get around it due to its aggressiveness in hiding its components. A useful trick is to use the busybox-static utilities, which are statically linked and therefore immune to LD preloading.

Perfctl will attempt to use sudo to escalate its permissions to root if the user it was executed as has the required privileges. Failing this, it will attempt to exploit the vulnerability CVE-2021-4034.

Ultimately, perfctl will attempt to establish a C2 link via Tor and spawn an XMRig miner to mine the Monero cryptocurrency. The traffic to the mining pool is encapsulated within Tor to limit network detection of the mining traffic.

Darktrace’s Cloudypots system has observed 1,959 infections of the perfctl campaign across its honeypot network in the past year, making it one of the most aggressive campaigns seen by Darktrace.

Key takeaways

This blog has shown how Darktrace / Forensic Acquisition & Investigation equips defenders in the face of a real-world attacker campaign. By using this solution, organizations can acquire forensic evidence and investigate intrusions across multiple cloud resources and providers, enabling defenders to see the full picture of an intrusion on day one. Forensic Acquisition & Investigation’s patented data-processing system takes advantage of the cloud’s scale to rapidly process large amounts of data, allowing triage to take minutes, not hours.

Darktrace / Forensic Acquisition & Investigation is available as Software-as-a-Service (SaaS) but can also be deployed on-premises as a virtual application or natively in the cloud, providing flexibility between convenience and data sovereignty to suit any use case.

Support for acquiring traditional compute instances like EC2, as well as more exotic and newly targeted platforms such as ECS and Lambda, ensures that attacks taking advantage of Living-off-the-Cloud (LOTC) strategies can be triaged quickly and easily as part of incident response. As attackers continue to develop new techniques, the ability to investigate how they use cloud services to persist and pivot throughout an environment is just as important to triage as a single compromised EC2 instance.

Credit to Nathaniel Bill (Malware Research Engineer)

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer

Blog

/

Network

/

February 19, 2026

CVE-2026-1731: How Darktrace Sees the BeyondTrust Exploitation Wave Unfolding

Default blog imageDefault blog image

Note: Darktrace's Threat Research team is publishing now to help defenders. We will continue updating this blog as our investigations unfold.

Background

On February 6, 2026, the Identity & Access Management solution BeyondTrust announced patches for a vulnerability, CVE-2026-1731, which enables unauthenticated remote code execution using specially crafted requests.  This vulnerability affects BeyondTrust Remote Support (RS) and particular older versions of Privileged Remote Access (PRA) [1].

A Proof of Concept (PoC) exploit for this vulnerability was released publicly on February 10, and open-source intelligence (OSINT) reported exploitation attempts within 24 hours [2].

Previous intrusions against Beyond Trust technology have been cited as being affiliated with nation-state attacks, including a 2024 breach targeting the U.S. Treasury Department. This incident led to subsequent emergency directives from  the Cybersecurity and Infrastructure Security Agency (CISA) and later showed attackers had chained previously unknown vulnerabilities to achieve their goals [3].

Additionally, there appears to be infrastructure overlap with React2Shell mass exploitation previously observed by Darktrace, with command-and-control (C2) domain  avg.domaininfo[.]top seen in potential post-exploitation activity for BeyondTrust, as well as in a React2Shell exploitation case involving possible EtherRAT deployment.

Darktrace Detections

Darktrace’s Threat Research team has identified highly anomalous activity across several customers that may relate to exploitation of BeyondTrust since February 10, 2026. Observed activities include:

Outbound connections and DNS requests for endpoints associated with Out-of-Band Application Security Testing; these services are commonly abused by threat actors for exploit validation.  Associated Darktrace models include:

  • Compromise / Possible Tunnelling to Bin Services

Suspicious executable file downloads. Associated Darktrace models include:

  • Anomalous File / EXE from Rare External Location

Outbound beaconing to rare domains. Associated Darktrace models include:

  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Beacon to Young Endpoint
  • Anomalous Server Activity / Rare External from Server
  • Compromise / SSL Beaconing to Rare Destination

Unusual cryptocurrency mining activity. Associated Darktrace models include:

  • Compromise / Monero Mining
  • Compromise / High Priority Crypto Currency Mining

And model alerts for:

  • Compromise / Rare Domain Pointing to Internal IP

IT Defenders: As part of best practices, we highly recommend employing an automated containment solution in your environment. For Darktrace customers, please ensure that Autonomous Response is configured correctly. More guidance regarding this activity and suggested actions can be found in the Darktrace Customer Portal.  

Appendices

Potential indicators of post-exploitation behavior:

·      217.76.57[.]78 – IP address - Likely C2 server

·      hXXp://217.76.57[.]78:8009/index.js - URL -  Likely payload

·      b6a15e1f2f3e1f651a5ad4a18ce39d411d385ac7  - SHA1 - Likely payload

·      195.154.119[.]194 – IP address – Likely C2 server

·      hXXp://195.154.119[.]194/index.js - URL – Likely payload

·      avg.domaininfo[.]top – Hostname – Likely C2 server

·      104.234.174[.]5 – IP address - Possible C2 server

·      35da45aeca4701764eb49185b11ef23432f7162a – SHA1 – Possible payload

·      hXXp://134.122.13[.]34:8979/c - URL – Possible payload

·      134.122.13[.]34 – IP address – Possible C2 server

·      28df16894a6732919c650cc5a3de94e434a81d80 - SHA1 - Possible payload

References:

1.        https://nvd.nist.gov/vuln/detail/CVE-2026-1731

2.        https://www.securityweek.com/beyondtrust-vulnerability-targeted-by-hackers-within-24-hours-of-poc-release/

3.        https://www.rapid7.com/blog/post/etr-cve-2026-1731-critical-unauthenticated-remote-code-execution-rce-beyondtrust-remote-support-rs-privileged-remote-access-pra/

Continue reading
About the author
Emma Foulger
Global Threat Research Operations Lead
Your data. Our AI.
Elevate your network security with Darktrace AI