Blog
/
/
September 24, 2020

Detecting OT Threats: ICS Attack at International Airport

Learn how Darktrace's OT Threat Detection technology identified a sophisticated ICS attack on an international airport. Read more on Darktrace's blog.
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
David Masson
VP, Field CISO
Default blog image
24
Sep 2020

As Industrial Control Systems (ICS) and traditional IT networks converge, the number of cyber-attacks that start in the corporate network before spreading to operational technology has increased dramatically in the last 12 months. From North Korean hackers targeting a nuclear power plant in India to ransomware shutting down operations at a US gas facility, and across Honda’s manufacturing sites, 2020 has been the year OT attacks have become mainstream.

Darktrace recently detected a simulation of a state-of-the-art attack at an international airport, identifying ICS reconnaissance, lateral movement, vulnerability scanning and protocol fuzzing – a technique in which the attacker sends nonsensical commands over an ICS communication channel in order to confuse the target device, causing it to fail or reboot.

Darktrace’s Industrial Immune System detected every stage of the sophisticated attack, using AI-powered anomaly detection to identify ICS attack vectors without a list of known exploits, company assets, or firmware versions. The attacker leveraged tools at every stage of the ICS kill chain, including ICS-specific attack techniques.

Any unusual attempts to read or reprogram single coils, objects, or other data blocks were detected by Cyber AI, and Darktrace’s Cyber AI Analyst also automatically identified the activity and created summary reports detailing the key actions taken.

The attack spanned multiple days and targeted the Building Management System (BMS) and the Baggage Reclaim network, with attackers utilizing two common ICS protocols (BacNet and S7Comm) and leveraging legitimate tools (such as ICS reprogramming commands and connections through SMB service pipes) to evade traditional, signature-based security tools.

Attack details

Figure 1: Timeline of the attack

In the first stage of the attack, a new device was introduced to the network, using ARP spoofing to evade detection from traditional security tools. At 11.40am, the attacker scanned a target device and attempted to bruteforce open services. Once the target device had been hijacked, the attacker then sought to establish an external connection to the Internet. External connections should not be possible in ICS networks, but attackers often seek to bypass firewalls and network segregation rules in order to create a command and control (C2) channel.

Figure 2: Darktrace Threat Tray 15 minutes after the pentest commenced. High level model breaches have already alerted the analyst team to the attack device.

The hijacked device then began performing ICS reconnaissance using Discover and Read commands. Darktrace identified new objects and data blocks being targeted as part of this reconnaissance, and detected ICS devices targeted with unusual BacNet and Siemens S7Comm protocol commands.

Figure 3: Model alerts associated with ICS reconnaissance over BacNet. Machine learning at the ICS command level detected new and unusual BacNet objects being targeted by the attacker.

The attacker enumerated through multiple ICS devices in order to perform lateral movement throughout the ICS system. Once they had learned device settings and configurations, they used ICS Reprogram and Write commands to reconfigure machines. The attacker attempted to use known vulnerabilities to exploit the target devices, such as the use of SMB, SMBv1, HTTP, RDP, and ICS protocol fuzzing.

Figure 4: Visualization of the device enumeration performed by the attacker against multiple ICS controllers. The attacker used ICS Discover commands as part of the initial reconnaissance.

The attacker took deliberate actions to evade the airport’s cyber security stack, including making connections using ICS protocols commonly used on the network to devices which commonly use those protocols. While legacy security tools failed to pick up on this activity, Darktrace’s deep packet inspection was able to identify unusual commands used by the attacker within those ‘normal’ connections.

The attacker used ARP spoofing to slow any investigation using asset management-based security tools – including two other solutions being trialed by the airport at the time of the attack. They also used multiple devices throughout the intrusion to throw defense teams off the scent.

Darktrace’s AI technology also launched an automated investigation into the incident. The Cyber AI Analyst identified all of the attack devices and produced summary reports for each, showcasing its ability to not only save crucial time for security teams, but bridge the skills gap between IT teams and ICS engineers.

Figure 5: The Cyber AI Analyst threat tray at the end of day 1. Both devices used by the attacker have been identified.

The Cyber AI Analyst immediately began investigating after the first model breach, and continued to stitch together disparate events across the network to produce a natural language summary of the incident, including recommendations for action.

Figure 6: AIA incident summary at the end of day 2, detailing the use of SMB exploits as part of the attack chain against one of the ICS devices.

Potential ramifications

Had the attack been allowed to continue, the attackers – potentially activist groups, terrorist organizations, and organized criminals – could have caused significant operational disruption to the airport. For example, the BMS is likely to manage temperature settings, the sprinkler system, fire alarms and fire exits, lighting, and doors in and out of secure access areas. Meddling with any one of these could cause severe disruption at an airport, with significant financial and reputational effects. Similarly, access to baggage reclaim networks could be used by criminals seeking to smuggle illegal goods or steal valuable cargo.

This simulation showcases the possibilities for an advanced cyber-criminal looking to compromise integrated IT and OT networks. The majority of leading ICS ‘security’ vendors are signature-based, and fail to pick up on novel techniques and utilization of common protocols to pursue malicious ends – this is why ICS attacks have continued to hit the headlines this year.

The incident showcases the extent of Cyber AI’s detections in a real-world ICS environment, and the level of detail Darktrace can provide following an attack. As Industrial Control Systems become increasingly integrated with the wider IT network, the importance of securing these critical systems is paramount. Darktrace provides a unified security umbrella with visibility and detection across the entire digital environment.

Thanks to Darktrace analyst Oakley Cox for his insights on the above investigation.

Learn more about the Industrial Immune System

Darktrace model detections:

  • ICS / Unusual ICS Commands
  • ICS / Multiple New Reprograms
  • ICS / Multiple New Discover Commands
  • ICS / Rare External from OT Device
  • ICS / Uncommon ICS Protocol Warning
  • ICS / Multiple Failed Connections to ICS Device
  • ICS / Anomalous IT to ICS Connection
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
David Masson
VP, Field CISO

More in this series

No items found.

Blog

/

Email

/

July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

Default blog imageDefault blog image

Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

/

July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

behavioral detectionDefault blog imageDefault blog image

Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

[related-resource]

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI