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March 7, 2024

Defending Against the New Normal in Cybercrime: AI

This blog outlines research & data points on the evolving threat landscape, the impact of malicious AI, and why proactive cyber readiness is essential.
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
Max Heinemeyer
Global Field CISO
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07
Mar 2024

AI in Cyber Security

Over the last 18 months, discussions about artificial intelligence (AI) – specifically generative AI – ranged from excitement and optimism about its transformative potential to fear and uncertainty about the new risks it introduces.  

New research1 commissioned by Darktrace shows that 89 percent of IT security teams polled globally believe AI-augmented cyber threats will have a significant impact on their organization within the next two years, yet 60 percent believe they are currently unprepared to defend against these attacks. Their concerns include increased volume and sophistication of malware that targets known vulnerabilities and increased exposure of sensitive or proprietary information from using generative AI tools.  

At Darktrace, we monitor trends across our global customer base to understand how the challenges facing security teams are evolving alongside industry advancements in AI. We’ve observed that AI, automation, and cybercrime-as-a-service have increased the speed, sophistication and efficacy of cyber security attacks.  

How AI Impacts Phishing Attempts

Darktrace has observed immediate impacts on phishing, which remains one of the most common forms of attack. In April 2023, Darktrace shared research that found a 135 percent increase in ‘novel social engineering attacks’ in the first two months of 2023, corresponding with the widespread adoption of ChatGPT2. These phishing attacks showed a strong linguistic deviation – semantically and syntactically – compared to other phishing emails, which suggested to us that generative AI is providing an avenue for threat actors to craft sophisticated and targeted attacks at speed and scale. A year later, we’ve seen this trend continue. Darktrace customers received approximately 2,867,000 phishing emails in December 2023 alone, a 14 percent increase on what was observed months prior in September3. Between September and December 2023, phishing attacks that used novel social engineering techniques grew by 35 percent on average across the Darktrace customer base4.  

These observations reinforce trends that others in the industry have shared. For example, Microsoft and OpenAI recently published research on tactics, techniques, and procedures (TTPs) augmented by large language models (LLMs) that they have observed nation-state threat actors using. That includes using LLMs to draft and generate social engineering attacks, inform reconnaissance, assist with vulnerability research and more.  

The Rise of Cybercrime-as-as-a-Service

The increasing cyber challenge facing defenders cannot be attributed to AI alone. The rise of cybercrime as-a-service is also changing the dynamic. Darktrace’s 2023 End of Year Threat Report found that cybercrime-as-a-service continue to dominate the threat landscape, with malware-as-a-Service (MaaS) and ransomware-as-a-Service (RaaS) tools making up most malicious tools in use by attackers. The as-a-Service ecosystem can provide attackers with everything from pre-made malware to templates for phishing emails, payment processing systems and even helplines to enable bad actors to mount attacks with limited technical knowledge.  

These trends make it clear that attackers now have a more widely accessible toolbox that reduces their barriers.

AI Enabling Accidental Insider Threats

However, the new risks facing businesses aren’t from external threat actors alone. Use of generative AI tools within the enterprise introduces a new category of accidental insider threats. Employees using generative AI tools now have easier access to more organizational data than ever before. Even the most well-intentioned employee could unintentionally leak or access restricted, sensitive data via these tools. In the second half of 2023, we observed that approximately half of Darktrace customers had employees accessing generative AI services. As this continues to increase, organizations need policies in place to guide the use cases for generative AI tools as well as strong data governance and the ability to enforce these policies to minimize risk.  

It is inevitable that AI will increase the risks and threats facing an organization, but this is not an unsolvable challenge from a defensive perspective. While advancements in generative AI may be worsening issues like novel social engineering and creating new types of accidental insider threats, AI itself offers a strong defense.  

The Shift to Proactive Cyber Readiness

According to the World Economic Forum’s Global Cybersecurity Outlook 2024, the number of organizations that “maintain minimum viable cyber resilience is down 30 percent compared to 2023”, and “while large organizations have demonstrated gains in cyber resilience, small and medium-sized companies showed significant decline.” The importance of cyber resilience cannot be understated in the face of today’s increasingly as-a-service, automated, and AI-augmented threat landscape.  

Historically, organizations wait for incidents to happen and rely on known attack data for threat detection and response, making it nearly impossible to identify never-before-seen threats. The traditional security stack has also relied heavily on point solutions focused on protecting different pieces of the digital environment, with individual tools for endpoint, email, network, on-premises data centers, SaaS applications, cloud, OT and beyond. These point solutions fail to correlate disparate incidents to form a complete picture of an orchestrated attack. Even with the addition of tools that can stitch together events from across the enterprise, they are in a reactive state that focuses heavily on threat detection and response.  

Organizations need to evolve from a reactive posture to a stance of proactive cyber readiness. To do so, they need an approach that proactively identifies internal and external vulnerabilities, identifies gaps in security policy and process before an attack occurs, breaks down silos to investigate all threats (known and unknown) during an attack, and uplifts the human analyst beyond menial tasks to incident validation and recovery after an attack.  

AI can help break down silos within the SOC and provide a more proactive approach to scale up and augment defenders. It provides richer context when it is fed information from multiple systems, data sets, and tools within the stack and can build an in-depth, real-time behavioural understanding of a business that humans alone cannot.

Lessons From AI in the SOC

At Darktrace, we’ve been applying AI to the challenge of cyber security for more than ten years, and we know that proactive cyber readiness requires the right mix of people, process, and technology.  

When the right AI is applied responsibly to the right cyber security challenge, the impact on both the human security team and the business is profound.

AI can bring machine speed and scale to some of the most time-intensive, error-prone, and psychologically draining components of cyber security, helping humans focus on the value-added work that only they can provide. Incident response and continuous monitoring are two areas where AI has already been proven to effectively augment defenders. For example, a civil engineering company used Darktrace’s AI to uplift its SOC team from the repetitive, manual tasks of analyzing and responding to email incidents. The analysts estimated they were each spending 10 hours per week on email incident analysis. With AI autonomously analyzing and responding to email incidents, the analysts could gain approximately 20 percent of their time back to focus on proactive cyber security measures

An effective human-AI partnership is key to proactive cyber readiness and can directly benefit the work-life of defenders. It can help to reduce burnout, support data-driven decision-making, and reduce the reliance on hard-to-find, specialized talent that has created a skills shortage in cyber security for many years. Most importantly, AI can free up team members to focus on more meaningful tasks, such as compliance initiatives, user education, and sophisticated threat hunting.  

Advancements in AI are happening at a rapid pace. As we’ve already observed, attackers will be watching these developments and looking for ways to use it to their advantage. Luckily, AI has already proved to be an asset for defenders, and embracing a proactive approach to cyber resilience can help organizations increase their readiness for this next phase. Prioritizing cyber security will be an enabler of innovation and progress as AI development continues.  

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Join Darktrace on 9 April for a virtual event to explore the latest innovations needed to get ahead of the rapidly evolving threat landscape. Register today to hear more about our latest innovations coming to Darktrace’s offerings.

References

[1] The survey was undertaken by AimPoint Group & Dynata on behalf Darktrace between December 2023 & January 2024. The research polled 1773 security professionals in positions across the security team from junior roles to CISOs, across 14 countries – Australia, Brazil, France, Germany, Italy, Japan, Mexico, Netherlands, Singapore, Spain, Sweden, UAE, UK, and USA.

[2] Based on the average change in email attacks between January and February 2023 detected across Darktrace/Email deployments with control of outliers.

[3] Average calculated across Darktrace customers from 31st August to 21st December.

[4] Average calculated across Darktrace customers from 31st August to 21st December. Novel social engineering attacks use linguistic techniques that are different to techniques used in the past, as measured by a combination of semantics, phrasing, text volume, punctuation, and sentence length.

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
Max Heinemeyer
Global Field CISO

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September 3, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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September 4, 2026

Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device

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Key takeaways

  • Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
  • The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
  • The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.

Mirai and the Katana variant

Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].

Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].

In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access.  Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].

Darktrace’s detection of Mirai Botnet activity on a camera device

In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.

In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.

The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.

Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.

Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.

Figure 2:  Darktrace’s detection of the unusual data upload activity by the affected camera device.

Darktrace continued to observe similar data transfers to multiple rare endpoints  including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).

Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.

Exploitation continued

Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.

The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.

The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].

Figure 4: Darktrace’s  detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.

A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”,  from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.

Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address,  66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.

Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.

Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.

Conclusion

This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.

This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.

As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.

Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Device / Initial Attack Chain Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Anomalous Connection / Data Sent to Rare Domain

·      Unusual Activity / Enhanced Unusual External Data Transfer

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Device / Significant UDP Increase

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Unusual Activity / Unusual External Activity

·      Compliance / SSH to Rare External Destination

·      Unusual Activity / Unusual DNS

·      Device / External Network Scan

·      Device / Suspicious DNS Activity

·      Device / Large Number of Model Alerts

List of Indicators of Compromise (IoCs)

Indicator of Compromise Type Description
195.177.94[.]105 IP C2 endpoint
50.7.49[.]4:30171 IP Possible C2 endpoint
129.121.114[.]124 IP C2 endpoint
hxxp://195.177.94[.]105/n3 URL Likely C2 endpoint
hxxp://195.177.94[.]105/n2 URL Likely C2 endpoint
hxxp://129.121.114[.]124/lil URL Likely C2 endpoint
hxxp://129.121.114[.]124/HHn URL Possible C2 endpoint
hxxp://129.121.114[.]124/JFc URL Possible C2 endpoint
hxxp://129.121.114[.]124/jum URL Likely C2 endpoint
hxxp://129.121.114[.]124/OaSf URL Likely C2 endpoint
hxxp://129.121.114[.]124/OPWg URL Possible C2 endpoint
hxxp://129.121.114[.]124/vHwK URL Possible C2 endpoint
hxxp://129.121.114[.]124/VLv URL Possible C2 endpoint
hxxp://129.121.114[.]124/WbJ URL Possible C2 endpoint
hxxp://129.121.114[.]124/zkR URL Possible C2 endpoint
Ab17883ae4c3bc6afa18c439166eeeb4b03186e3093d984e3a95f573e0fcb7d8 SHA-256 Mirai payload
3d587e809dac49d34a3f717e072fd0aebe5e71db63333e45c81577d6b4266f87 SHA-256 Mirai payload
Bf6e81733a7e209d3dce80d15bf3c5d300752d961fae6b45d90c9bbe7f8c89a2 SHA-256 Possible payload
f25488303813ab1ec0eaa71562938601aac185e8aaf93adb84522557f7cf4dd6 SHA-256 Possible payload
0cb4ff6b71f4423184bfa35c34e9090297637208b0e30205d4b224e56abde2ef SHA-256 Possible payload
19c24cbeaf06b2e7697083f33a85521a9315105c784691bde7420fde4cc69410 SHA-256 Likely Mirai payload
1e74f734fff8df91f4f7172d0de10c421eca78aeb800e8a48e16bc5dbde5d20e SHA-256 Possible payload
6e71f7763d1f29d5712106ebb122e281c32787540aa2342b0fe5351d585d18d7 SHA-256 Possible payload
71f4ff7cdb6d6a7d2673c543c5d2535093afbd707b20a5b9ddf735466c1105c1 SHA-256 Possible payload
76db7ee73ebf15e48a3cb24a074d92248671ef2c6ed3bc3e708377341fb7674d SHA-256 Possible payload
da87a65f7beb438e61f0b61964fed8aa305a380f569042f84c55eca8fa7929b8 SHA-256 Possible payload
e15809eb6ba66477175270d62cfa53e4bf278595f69938708c81c4bc457930fe SHA-256 Mirai payload

MITRE ATT&CK Mapping

Tactic Technique ID Technique / Sub-technique
Initial Access T1659 Content Injection
T1189 Drive-by Compromise
Exfiltration T1041 Exfiltration Over C2 Channel
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol
Command and Control T1105 Ingress Tool Transfer
T1095 Non-Application Layer Protocol
T1571 Non-Standard Port
Reconnaissance T1595.001 Scanning IP Blocks
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About the author
Parvatha Ananthakannan
Cyber Analyst
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