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May 11, 2023

Securing OT Systems: The Limits of the Air Gap Approach

Air-gapped security measures are not enough for resilience against cyber attacks. Read about how to gain visibility & reduce your cyber vulnerabilities.
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
Max Lesser
Head of U.S. Policy Analysis and Engagement
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11
May 2023

At a Glance:

  • Air gaps reduce cyber risk, but they do not prevent modern cyber attacks
  • Having visibility into an air-gapped network is better than assuming your defenses are impenetrable and having zero visibility
  • Darktrace can provide visibility and resiliency without jeopardizing the integrity of the air gap

What is an 'Air Gap'?

Information technology (IT) needs to fluidly connect with the outside world in order channel a flow of digital information across everything from endpoints and email systems to cloud and hybrid infrastructures. At the same time, this high level of connectivity makes IT systems particularly vulnerable to cyber-attacks.  

Operational technology (OT), which controls the operations of physical processes, are considerably more sensitive. OT often relies on a high degree of regularity to maintain continuity of operations. Even the slightest disturbance can lead to disastrous results. Just a few seconds of delay on a programmable logic controller (PLC), for example, can significantly disrupt a manufacturing assembly line, leading to downtime at a considerable cost. In worst-case scenarios, disruptions to OT can even threaten human safety. 

An air gap is a ‘digital moat’ where data cannot enter or leave OT environments unless it is transferred manually.

Organizations with OT have traditionally tried to reconcile this conflict between IT and OT by attempting to separate them completely. Essentially, the idea is to let IT do what IT does best — facilitate activities like communication and data transfer at rapid speeds, thus allowing people to connect with each other and access information and applications in an efficient capacity. But at the same time, erect an air gap between IT and OT so that any cyber threats that slip into IT systems do not then spread laterally into highly sensitive, mission-critical OT systems. This air gap is essentially a ‘digital moat’ where data cannot enter or leave OT environments unless it is transferred manually.

Limitations of the Air Gap

The air gap approach makes sense, but it is far from perfect. First, many organizations that believe they have completely air-gapped systems in fact have unknown points of IT/OT convergence, that is, connections between IT and OT networks of which they are unaware. 

Many organizations today are also intentionally embracing IT/OT convergence to reap the benefits of digital transformation of their OT, in what is often called Industry 4.0. Examples include the industrial cloud (or ICSaaS), the industrial internet of things (IIoT), and other types of cyber-physical systems that offer increased efficiency and expanded capabilities when compared to more traditional forms of OT. Organizations may also embrace IT/OT convergence due to a lack of human capital, as convergence can make processes simpler and more efficient.

Even when an organization does have a true air gap (which is nearly impossible to confirm without full visibility across IT and OT environments), the fact is that there are a variety of ways for attackers to ‘jump the air gap'. Full visibility across IT and OT ecosystems in a single pane of glass is thus essential for organizations seeking to secure their OT. This is not only to illuminate any points of IT/OT convergence and validate the fact that an air gap exists in the first place, but also to see when an attack slips through the air gap.

Figure 1: Darktrace/OT's unified view of IT and OT environments.

Air Gap Attack Vectors

Even a perfect air gap will be vulnerable to a variety of different attack vectors, including (but not limited to) the following: 

  • Physical compromise: An adversary bypasses physical security and gains access directly to the air-gapped network devices. Physical access is by far the most effective and obvious technique.
  • Insider threats: Someone who is part of an organization and has access to air-gapped secure systems intentionally or unintentionally compromises a system.
  • Supply chain compromise: A vendor with legitimate access to air-gapped systems unwittingly is compromised and brings infected devices into a network. 
  • Misconfiguration: Misconfiguration of access controls or permissions allows an attacker to access the air-gapped system through a separate device on the network.
  • Social engineering (media drop): If an attacker was able to successfully conduct a malicious USB/media drop and an employee was to use that media within the air-gapped system, the network could be compromised. 
  • Other advanced tactics: Thermal manipulation, covert surface vibrations, LEDs, ultrasonic transmissions, radio signals, and magnetic fields are among a range of advanced tactics documented and demonstrated by researchers at Ben Gurion University. 

Vulnerabilities of Air-Gapped Systems

Aside from susceptibility to advanced techniques, tactics, and procedures (TTPs) such as thermal manipulation and magnetic fields, more common vulnerabilities associated with air-gapped environments include factors such as unpatched systems going unnoticed, lack of visibility into network traffic, potentially malicious devices coming on the network undetected, and removable media being physically connected within the network. 

Once the attack is inside OT systems, the consequences can be disastrous regardless of whether there is an air gap or not. However, it is worth considering how the existence of the air gap can affect the time-to-triage and remediation in the case of an incident. For example, the existence of an air gap may seriously limit an incident response vendor’s ability to access the network for digital forensics and response. 

Kremlin Hackers Jumping the Air Gap 

In 2018, the U.S. Department of Homeland Security (DHS) issued an alert documenting the TTPs used by Russian threat actors known as Dragonfly and Energetic Bear. Further reporting alleged that these groups ‘jumped the air gap,’ and, concerningly, gained the ability to disable the grid at the time of their choosing. 

These attackers successfully gained access to sensitive air-gapped systems across the energy sector and other critical infrastructure sectors by targeting vendors and suppliers through spear-phishing emails and watering hole attacks. These vendors had legitimate access to air-gapped systems, and essentially brought the infection into these systems unintentionally when providing support services such as patch deployment.

This incident reveals that even if a sensitive OT system has complete digital isolation, this robust air gap still cannot fully eliminate one of the greatest vulnerabilities of any system—human error. Human error would still hold if an organization went to the extreme of building a faraday cage to eliminate electromagnetic radiation. Air-gapped systems are still vulnerable to social engineering, which exploits human vulnerabilities, as seen in the tactics that Dragonfly and Energetic Bear used to trick suppliers, who then walked the infection right through the front door. 

Ideally, a technology would be able to identify an attack regardless of whether it is caused by a compromised supplier, radio signal, or electromagnetic emission. By spotting subtle deviations from a device, human, or network’s normal ‘pattern of life’, Self-Learning AI detects even the most nuanced forms of threatening behavior as they emerge — regardless of the source or cause of the threat.

Darktrace/OT for Air-Gapped Environments

Darktrace/OT for air-gapped environments is a physical appliance that deploys directly to the air-gapped system. Using raw digital data from an OT network to understand the normal pattern of life, Darktrace/OT does not need any data or threat feeds from external sources because the AI builds an innate understanding of self without third-party support. 

Because all data-processing and analytics are performed locally on the Darktrace appliance, there is no requirement for Darktrace to have a connection out to the internet. As a result, Darktrace/OT provides visibility and threat detection to air-gapped or highly segmented networks without jeopardizing their integrity. If a human or machine displays even the most nuanced forms of threatening behavior, the solution can illuminate this in real time. 

Security professionals can then securely access Darktrace alerts from anywhere within the network, using a web browser and encrypted HTTPS, and in line with your organization’s network policies.

Figure 2: Darktrace/OT detecting anomalous connections to a SCADA ICS workstation.

With this deployment, Darktrace offers all the critical insights demonstrated in other Darktrace/OT deployments, including (but not limited to) the following:

Organizations seeking to validate whether they have an air gap in the first place and maintain the air gap as their IT and OT environments evolve will greatly benefit from the comprehensive visibility and continuous situational awareness offered by Darktrace’s Self-Learning AI. Also, organizations looking to poke holes in their air gap to embrace the benefits of IT/OT convergence will find that Self-Learning AI’s vigilance spots cyber-attacks that slip through. 

Whatever your organizations goals—be it embracing IIoT or creating a full-blown DMZ—by learning ‘you’, Darktrace’s Self-Learning AI can help you achieve them safely and securely. 

Learn more about Darktrace/OT

Credit to: Daniel Simonds and Oakley Cox for their contribution to this 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
Max Lesser
Head of U.S. Policy Analysis and Engagement

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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About the author
Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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