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September 23, 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
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23
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

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May 20, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

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Jamie Bali
Technical Author (AI) Developer

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May 20, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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