Blog
/
/
July 7, 2021

How Cyber-Attacks Take Down Critical Infrastructure

Cyber-attacks can bypass IT/OT security barriers and threaten your organization's infrastructure. Here's how you can stay protected in today's threat landscape.
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
Oakley Cox
Director of Product
Default blog image
07
Jul 2021

Balancing Operational Continuity and Safety in Critical Infrastructure

The recent high-profile attacks against Colonial Pipeline and JBS Foods highlight that operational technology (OT) — the devices that drive gas flows and food processing, along with essentially all other machine-driven physical processes — does not need to be directly targeted in order to be shut down as the result of a cyber-attack.

Indeed, in the Colonial Pipeline incident, the information technology (IT) systems were reportedly compromised, with operations shut down intentionally out of an abundance of caution, that is, so as to not risk the attack spreading to OT and threatening safety. This highlights that threats to both human and environmental safety, along with uncertainty as to the scope of infection, present risk factors for these sensitive industrial environments.

Continuity through availability and integrity

In most countries, critical infrastructure (CI) — ranging from power grids and pipelines to transportation and health care — must maintain continuous activity. The recent ransomware attack against Colonial Pipeline demonstrates why this is the case, where gas shortages due to the compromise led to dangerous panic buys and long lines at the pumps.

Ensuring continuous operation of critical infrastructure requires safeguarding the availability and integrity of machinery. This means that organizations overseeing critical infrastructure must foresee any possible risks and implement systems, procedures, and technologies that mitigate or remove these risks so as to keep their operations running.

Operational demand versus safety

Alongside this requirement for operational continuity, and often in opposition to it, is the requirement for operational safety. These requirements can be in opposition because operational continuity demands that devices remain up and running at all costs, and operational safety demands that humans and the environment be protected at all costs.

Safety measures in critical infrastructure have improved and become increasingly prioritized over the last 50 years following numerous high-profile incidents, such as the Bhopal chemical disaster, the Texas City refinery explosion, and the Deepwater Horizon oil spill. Appropriate safety precautions could have likely prevented these incidents, but at the expense of operational continuity.

Consequently, administrators of critical infrastructure have to balance the very real threat that an incident may pose to both human life and the environment with the demand to remain operational at all times. More often than not, the final decision regarding what constitutes an acceptable risk is determined by budgets and cost-benefit analyses.

Cyber-attack: A rising risk profile for critical infrastructure

In 2010, the discovery of the Stuxnet malware — which resulted in a nuclear facility in Iran having its centrifuges ruined via compromised programmable logic controllers (PLCs) — demonstrated that critical infrastructure could be targeted by a cyber-attack.

At the time of Stuxnet, critical infrastructure industries used computers designed to ensure operational continuity with little regard for cyber security, as at the time the risk of a cyber-attack seemed either non-existent or vanishingly low. Since then, a number of attacks targeting industrial environments that have emerged on the global threat landscape.

Figure 1: An overview of distinctive methods used in attacks against industrial environments

Classic strains of industrial malware, such as Stuxnet, Triton, and Industroyer, have historically been installed via removable media, such as USB. This is because OT networks are traditionally segregated from the Internet in what is known as an ‘air gap.’ And this remains a prevalent vector of attack, with a study recently finding that cyber-threats installed via USB and other external media doubled in 2021, with 79% of these holding the potential to disrupt OT.

In many ways, operational demands in the subsequent 10 years have made critical infrastructure even more vulnerable. These include the convergence of information technology and operational technology (IT/OT convergence), the adoption of devices in the Industrial Internet of Things (IIoT), and the deprecation of manual back-up systems. This means that OT can be disrupted by cyber-attacks that first target IT systems, rather than having to be installed manually via external media.

At the same time, recent government initiatives — such as the Department of Energy’s 100-day ‘cyber sprint’ to protect electricity operations and President Biden’s Executive Order on Improving the Nation’s Cybersecurity — and regulatory frameworks and directives such as the EU’s NIS directive have either encouraged or mandated that critical infrastructure industries start addressing this new risk.

With the severe and persistent threat that cyber-attacks pose to critical infrastructure, including maritime cybersecurity, and the increasing calls to address the issue, the question remains as to how to best achieve robust cyber defense.

Assessing the risk

To claim administrators of critical infrastructure are ignorant or oblivious to the threat posed by cyber-attacks would be unfair. Many organizations have implemented changes to mitigate or remove the risk either as a result of regulation or their own forward thinking.

However, these projects can take years, even decades. High costs and ever-changing operational demand also mean that these projects may never fully remove the risk.

As a result, many operators may understand the threat of a cyber-attack but not be in a position to do anything about it in the short or medium term. Instead, procedures have to be put in place to minimize risk even if this threatens operational continuity.

For example, a risk assessment may decide it is best to shut down all OT operations in the event of a cyber-attack in order to avoid a major accident. This abundance of caution is forced upon operators, who do not have the ability to immediately confirm the boundaries of a compromise. The prevalence of cyber insurance provides this option with further appeal. Any losses incurred by stopping operations can theoretically be recouped and the risk is therefore transferred.

While the full details of the Colonial Pipeline ransomware incident are still to be determined, the sequence of events outlined below provides a plausible explanation for how a cyber-attack could take down critical infrastructure, even when that cyber-attack does not reach or even target OT systems. Indeed, the CEO of Colonial Pipeline, in a testimony to congress, confirmed “the imperative to isolate and contain the attack to help ensure the malware did not spread to the operational technology network, which controls our pipeline operations, if it had not already.”

Figure 2: A sequence of events which may lead to critical infrastructure being shut down by a cyber-attack, even when that cyber-attack doesn’t directly impact OT networks

The limits of securing IT or OT in isolation

The emergence of OT cyber security solutions in the last five years demonstrates that critical infrastructure industries are trying to find a way to address the risks posed by cyber-attacks. But these solutions have limited scope, as they assume IT and OT are separated and use legacy security techniques such as malware signatures and patch management.

The 2021 SANS ICS Security Summit highlighted how the OT security community suffers from a lack of visibility in knowing and understanding their networks. For many organizations, simply determining whether an unusual incident is an attack or the result of a software error is a challenge.

Given that most OT cyber-attacks actually start in IT networks before pivoting into OT, investing in an IT security solution rather than an OT-specific solution may at first seem like a better business decision. But IT solutions fall short if an attacker successfully pivots into the OT network, or if the attacker is a rogue insider who already has direct access to the OT network. A siloed approach to securing either IT or OT in isolation will thus fall short of the full scope needed to safeguard industrial systems.

It is clear that a mature security posture for critical infrastructure would include security solutions for both IT and OT. Even then, using separate solutions to protect the IT and OT networks is limited, as it presents challenges when defending network boundaries and detecting incidents when an attacker pivots from IT to OT. Under time pressure, a security team does not want changes in visibility, detection, language or interface while trying to determine whether a threat crossed the ‘boundary’ between IT and OT.

Separate solutions can also make detecting an attacker abusing traditional IT attack TTPs within an OT network much harder if the security team is relying on a purely OT solution to defend the OT environment. Examples of this include the abuse of IT remote management tools to affect industrial environments, such as in the suspected cyber-attack at the Florida water facility earlier this year. Cybersecurity for utilities is becoming increasingly important as these sectors face growing cyber threats that can disrupt essential services.

Using AI to minimize cyber risk and maximize cyber safety

In contrast, Darktrace AI is able to defend an entire cyber ecosystem estate, building a ‘pattern of life’ across IT and OT, as well as the points at which they converge. Consequently, cyber security teams can use a single pane of glass to detect and respond to cyber-attacks as they emerge and develop, regardless of where they are in the environment.

Use cases for Darktrace’s Self-Learning AI include containing pre-existing threats to maintain continuous operations. This was seen when Darktrace’s AI detected pre-existing infections and acted autonomously to contain the threat, allowing the operator to leave infected IIoT devices active while waiting for replacements. Darktrace can also thwart ransomware in IT before it can spread into OT, as when Darktrace detected a ransomware attack targeting a supplier for critical infrastructure in North America at its earliest stages.

Darktrace’s unified protection, including visibility and early detection of zero-days, empowers security teams to overcome uncertainty and make a confident decision not to shut down operations. Darktrace has already demonstrated this ability in the wild, and allows organizations to understand normal machine and human behavior in order to enforce this behavior, even in the face of an emerging cyber-attack.

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
Oakley Cox
Director of Product

More in this series

No items found.

Blog

/

Email

/

May 26, 2026

Journey of a Threat: How Multi-Layered AI Works in Darktrace / EMAIL

Man at a computerDefault blog imageDefault blog image

Darktrace / EMAIL is an implementation of the Darktrace methodology – a multi-layered AI system built into a single product. As with other Darktrace products, Darktrace / EMAIL learns the expected behaviours of an organization and its employees to identify novel threats and anomalous activity.

The diagram below represents the architecture of Darktrace / EMAIL’s multi-layered AI: a structured visualization of how intelligence is built, step by step, from raw data to actionable insight. Each layer plays a distinct role, feeding into the next: collecting data, understanding behaviour, analysing intent, making decisions, and presenting clear outcomes.

It all starts with an email

In this blog, we’ll follow a malicious email as it passes through the Darktrace / EMAIL system, showing exactly what happens as it travels through each layer of the pyramid, from basic data extraction to AI-powered metric creation, and finally deciding on any autonomous actions.

Let’s take this example email. As an end-user, you can see that this is an obvious extortion attempt where an adversary is threatening legal action if money isn’t paid within 24 hours, but how does Darktrace figure that out?

Part 1: Data Gathering

Processing of an email begins on point-of-transit for all inbound, outbound, or lateral emails. The first step is to extract information directly. This includes taking information from the headers (such as sending and receiving addresses, sender IP address, routing, and authentication protocols), as well as extraction of raw HTML and CSS data from the email itself.

This directly extracted information only allows for immediate surface level analysis, such as identifying signature-based attacks (known malicious addresses / domains), but is insufficient for identifying novel threats, complex attacks, or potential email or vendor compromise. This is where Darktrace’s AI analysis shines.

In this example, the SPF, DKIM, and DMARC authentication all passed successfully, showing that even malicious emails can still bypass these signature-based checks. Even with this success, Darktrace will continue to analyse the email.

Diving deeper into the technical information, we can see further information extracted from the headers, including aggregations from the header information, historical calculations such as the frequency and volume of emails to and from a particular domain, and much more.

Part 2: Social Graphing

Social Graphing involves the analysis of sending and receiving behaviours of different mailboxes to create peer-groups. Mailboxes who often send and receive to and from the same mailboxes, or exhibit other correlated behaviours, will be clustered together using a collection of unsupervised AI clustering systems. These groups may represent uses in the same teams who perform similar activity, groups of external facing mailboxes which often receive unsolicited emails, or groups of VIP users (such as C-suite or executives).

Social graphing is an essential component of Darktrace’s pattern of life analysis. This clustering allows Darktrace to understand the responsibilities of individuals – for example, behaviours which are anomalous for one group of users may be completely expected of another group.

In our example, the email was sent to 3 different users within the organization. As part of the social graphing, an “Association Anomaly” is calculated which indicates the likelihood that these users would receive emails from this user or domain, based on historical patterns.

Part 3: Metric Calculation

Metrics are calculated for every email, representing more complex characteristics of an email which can’t be directly extracted. Darktrace / EMAIL features over 1000 unique metrics, calculated both algorithmically and using an ensemble of AI systems.

Algorithmically calculated (non-AI) metrics include further historical calculations, and counts of features such as code blocks, and hidden text, to name a few.

AI-driven metrics include Inducement Classification which uses Natural Language Processing to identify potential phishing, solicitation, or extortion attempts; Named Entity Recognition to identify PII and other sensitive data within an email to support Data Loss Prevention; and many more.

We can follow our example email through this process and view the outcome of these metric calculations. Looking at the language metrics for this email, we can see that our email has reported a high extortion inducement, along with identification of banking information and language indicating urgency.

Part 4: Evaluation and Combination Engine (models)

Once all metrics have been calculated for an email, it gets sent to an evaluation and combination engine where the metrics are compared against blocks of logic to determine if an email contains a threat. One key model which alerted for this example message was a model to tag and block extortion attempts.

Since our example email has a high inducement score for extortion, along the presence of a bitcoin wallet address in the message, this model alerts. When a model in the engine is activated, actions are taken – in this case adding a tag to the email to flag it as extortion in the console and hold the email to prevent it from reaching the end-user mailbox.

Part 5: Meta-Modelling and Actions

Once the models have been run, the actions are taken against the email. If the email hasn’t been blocked or held, this is the point where it will reach the end-user's mailbox.

In the Darktrace / EMAIL UI, all actions models which alerted for an email and actions taken as a result can be seen. At the top of this page, you can see the alert indicating an extortion attempt along with the action to hold the message.

Alongside this, a meta-classifier is used to calculate an overall anomaly score for each email, based on how much the email differs from the pattern of life for the user. The score of the email is boosted by any actions that have taken place.

Part 6: Campaign Clustering

All emails are passed through the Darktrace / EMAIL campaign clustering system. This system creates clusters based on related features within the emails to identify groups of emails with the same sender or intent.

In our case, the email was identified as part of a campaign, alongside other emails which were also identified as extortion attempts against a small group of recipients.

Email campaigns may have additional actions applied to them if the campaign is deemed malicious, and in this case, you can see that the autonomous response was to hold all emails in the campaign. This means that if an email manages to avoid being blocked in the evaluation and combination engine but gets identified as part of the campaign, the hold action will be applied to it retroactively.

Part 7: Cyber AI Analyst

Darktrace’s Cyber AI Analyst presents key information and anomaly indicators for each email, such as further information about authentication, specific metrics, or other identified anomalies and mismatches.

Cyber AI Analyst can also utilize data from Darktrace / EMAIL to enhance its investigation of incidents from other Darktrace products, correlating relevant information to build a fuller picture. More information about the Cyber AI Analyst is available in the Darktrace AI Arsenal.

Part 8: Data Presentation (UI)

Once all processing has taken place against the email, it is presented in the Darktrace / EMAIL UI. Here, members of the SOC team can investigate incidents and anomalies, interact with malicious emails to see why they were blocked, and much more.

Our email stands out here with its 100 anomaly score. Every email which passes through a Darktrace / EMAIL will undergo the same thorough and rigorous analysis to identify potential risks, apply autonomous actions where required, and will ultimately be assigned a score to be displayed here. By providing a single overall score in the UI, rather than presenting emails in full, Darktrace / EMAIL allows SOC teams to more easily identify which emails are most important to investigate, increasing efficiency and reducing alert fatigue.

Take the next step

Many email security tools on the market that claim to be AI-driven are in fact bolting AI onto attack-centric approaches, which rely on automating the identification of known threats. These approaches struggle, and will continue to struggle, with adapting to novel, AI-generated threats.

By analyzing every email within its deeply integrated, multi-layered AI system, Darktrace / EMAIL is able to identify the subtle threats that others miss. This depth not only improves detection accuracy, but enables confident, autonomous action, giving security teams clearer insight into AI outcomes and greater control while supporting users.

For a full deep dive into each stage of the AI system, check out the white paper: A Guide to the Multi-Layered AI in Darktrace / EMAIL

Learn more about securing AI in your enterprise.

[related-resource]

Continue reading
About the author
Jamie Bali
Technical Author (AI) Developer

Blog

/

Network

/

May 26, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

garnter ndr magic quadrantDefault blog imageDefault blog image

Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

[related-resource]

Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

Continue reading
About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response
Your data. Our AI.
Elevate your network security with Darktrace AI