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October 9, 2022

Piloting Airline Cyber Security With Artificial Intelligence (AI)

The airline industry is constantly exposed to cyber threats. Darktrace has some tips to help airline professionals bolster their cyber-security efforts.
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
Tony Jarvis
VP, Field CISO
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09
Oct 2022

A Thin Margin for Error

The airline industry has long been known for its thin profit margins, and the high costs of unexpected downtime. 2010’s Eyjafjallajökull eruption in Iceland and the resulting six-day airspace ban across Europe cost airlines $1.7 billion, just a taste of the impact that would come ten years later as a result of the pandemic. The industry collectively amassed more than $180 billion in debt in 2020, and some predictions suggest that by 2024 the industry's debt could exceed its revenue.

Given the impact that further sustained downtime could have on an already ailing industry, airlines are having to take cyber security seriously. Last year’s Colonial Pipeline ransomware attack in the US led to a six-day shutdown of pipeline operations – the same length of time that flights were grounded by the Eyjafjallajökull eruption. But while the industry hasn’t seen a volcanic eruption on that scale in over twelve years, ransomware attacks are striking airlines weekly. Just this year a ransomware attack on SpiceJet left hundreds of passengers stranded at airports across India, despite being contained relatively quickly.  

Fraud, Fines and Safety Risks

It isn’t just ransomware which is concerning many in the industry. Data breaches remain one of the biggest threats to airlines, organizations which are responsible at any one time for the personal and financial information of millions of customers. In 2019, British Airways had the data of 380,000 customers stolen, including addresses, birth dates and credit card information, and was fined £20 million (reduced from £183 million due in part to the impact of the pandemic) by the UK’s Information Commissioner’s Office (ICO), the largest issued fine in the ICO’s history. The European airline EasyJet is currently facing a class-action suit seeking £18 billion in damages after failing to properly disclose the loss of 2,208 customers’ credit-card information in 2020. 

Airlines are also losing out to card and air mile fraud, with thousands of fraudulent loyalty program accounts being sold on the dark web, as well as the usual roster of attacks including phishing and insider threats which affect businesses of every size and industry. The airlines themselves are not being complacent. In a 2021 report by SITA, 100% of airlines surveyed named cyber security as a key investment for the next three years. Making sure that those investments count will be the next challenge.

There are few industries for which safety and security measures are so important, and while no impact on flight safety as a result of a cyber-attack has yet been reported, agencies like Eurocontrol are already urging caution. Airlines and airports should look at smarter ways to proactively protect their digital environments. 

As attacks grow faster and less predictable, organizations are increasingly turning to preventative AI security measures. For airlines, which operate with broad attack surfaces and plenty of valuable data, using tools which can identify and monitor every asset and potential attack path in an organization and take the necessary steps to secure them is the best way to stay ahead of attackers.

Securing Airspace, Securing Cyberspace

As a recreational pilot myself, I understand the extent of the safety measures that go into every flight: the flight plans, pre-flight checks and all of the long-practiced, deep-embedded knowledge. It is this comprehensive and meticulous approach which ought to be reflected in organizations’ cyber security efforts – whether they be airlines, airports or any other type of business. The parallels between the processes of flying and running a digital organization safely give us a helpful way to understand what proper, AI-driven cyber security can do for any organization, airlines included.

Cleared for Takeoff 

For the pilot, safety measures start long before they’re sat in the cockpit. Flight planning, which includes planning heading and bearing, taking things like elevation, terrain, and weather conditions into consideration, must be completed in addition to plenty of pre-flight checks. The checklist the pilot works through when performing a walk around and pre-flight inspection will often be ordered so that they work in a circle around the perimeter of the whole plane. These checks prevent potential threats, covering everything from water having mixed with the fuel to birds making nests inside the engine cowling.

Darktrace PREVENT, released in July 2022, serves a similar purpose. The AI autonomously identifies and tests every single user and asset that makes up a business in order to spot potential vulnerabilities and harden defenses where necessary. Like a walk around, PREVENT/Attack Surface Management examines the full range of external assets for threats. Then, by identifying and testing potential attack pathways and mitigating against weak points and worst-case scenarios, PREVENT/End-to-End takes steps to win the fight before an attack has been launched. 

Maintaining Good Visibility

When you’re piloting a plane, first and foremost you need a way to detect key variables. Your fundamental flight instruments in the cockpit are known as the six pack:

1. Airspeed Indicator
2. Attitude Indicator or Artificial Horizon 
3. Altimeter
4. Turn Coordinator 
5. Heading Indicator
6. Vertical Speed Indicator

These six instruments provide the critical information needed by any pilot to safely fly the aircraft. While additional instruments are required to conduct flights In low-visibility or ‘Instrument Meteorological Conditions’ (IMC) conditions, these will be essential when getting out of dangerous situations such as inadvertently flying into cloud.

Understanding an environment and adapting to its changes is also fundamental to Darktrace DETECT: an AI-driven technology which focuses on building a comprehensive knowledge of an organization’s environment in order to spot threats the moment they appear. Because it understands what is ‘normal’ for the organization, Darktrace DETECT is able to correlate multiple subtle anomalies in order to expose emerging attacks – even those which have never been seen before. Like those essential flight instruments, DETECT offers visibility into otherwise obscure regions of the environment, and ensures that any potential problems are spotted as early as possible. 

Mayday, Mayday

In aviation and security, moving quickly once a threat has been detected is critical. When an engine stalls at 3,000 feet above ground level, you don’t have time to get the training books out and start figuring out what to do. Pilots are taught to “always have an out” and be ready to use it.

In aviation, an effective response relies for the most part on the knowledge and quick reactions of the pilot, but in cyber security, AI is making response faster and more effective than ever. Darktrace RESPOND uses DETECT’s contextual understanding in order to take the optimum action to mitigate a threat. Adaptability of this response is crucial: a single cyber-attack can come in any number of configurations, and Darktrace RESPOND is able to tailor its actions appropriately. Attacks today move too fast for human teams to be expected to keep up, but with AI taking actions at machine speed organizations can remain protected. 

Always Learning

One of the best pieces of advice a pilot can take is to always be learning. Every flight is an opportunity to learn something new and become a better and safer pilot.

Darktrace DETECT, RESPOND, and PREVENT are all driven by Self-Learning AI, a technology which not only builds but continuously evolves its understanding of each business. This means that as an organization grows, adding more users, assets, or applications, its Darktrace coverage grows too, using each new data point to enhance its understanding and the accuracy of its actions and detections. Darktrace’s separate technologies also learn from each other. Each of the three product families continuously feeds data into the others, helping to enhance their capabilities and improving their ability to keep organizations secured against threats. 

As cyber-attacks proliferate and increase in sophistication, they will continue to target organizations like airlines, which have large attack surfaces and copious amounts of customer data, and which cannot afford to weather sustained downtime. But with AI offering effective, proactive measures and clear-sky visibility, security teams can be confident in their ability to fight back.

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
Tony Jarvis
VP, Field CISO

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May 19, 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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May 18, 2026

AI Insider Threats: How Generative AI is Changing Insider Risk

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How generative AI changes insider behavior

AI systems, especially generative platforms such as chatbots, are designed for engagement with humans. They are equipped with extraordinary human-like responses that can both confirm, and inflate, human ideas and ideology; offering an appealing cognitive partnership between machine and human.  When considering this against the threat posed by insiders, the type of diverse engagement offered by AI can greatly increase the speed of an insider event, and can facilitate new attack platforms to carry out insider acts.  

This article offers analysis on how to consider this new paradigm of insider risk, and outlines key governance principles for CISOs, CSOs and SOC managers to manage the threats inherent with AI-powered insider risk.

What is an insider threat?

There are many industry or government definitions of what constitutes insider threat. At its heart, it relates to the harm created when trusted access to sensitive information, assets or personnel is abused bywith malicious intent, or through negligent activities.  

Traditional methodologies to manage insider threat have relied on two main concepts: assurance of individuals with access to sensitive assets, and a layered defense system to monitor for any breach of vulnerability. This is often done both before, and after access has been granted.  In the pre-access state, assurance is gained through security or recruitment checks. Once access is granted, controls such as privileged access, and zero-trust architecture offer defensive layers.

How does AI change the insider threat paradigm?

While these two concepts remain central to the management of insider threats, the introduction of AI offers three key new aspects that will re-shape the paradigm:.  

AI can act as a cognitive amplifier, influencing and affecting the motivations that can lead to insider-related activity. This is especially relevant for the deliberate insider - someone who is considering an act of insider harm. These individuals can now turn to AI systems to validate their thinking, provide unique insights, and, crucially, offer encouragement to act. With generative systems hard-wired to engage and agree with users, this can turn a helpful AI system into a dangerous AI hype machine for those with harmful insider intent.  

AI can act as an operational enabler. AI can now develop and increase the range of tools needed to carry out insider acts. New social engineering platforms such as vishing and deepfakes give adversaries a new edge to create insider harm. AI can generate solutions and operational platforms at increasing speeds; often without the need for human subject matter expertise to execute the activities. As one bar for advanced AI capabilities continues to be raised, the bar needed to make use of those platforms has become significantly lower.

AI can act as a semi-autonomous insider, particularly when agentic AI systems or non-human identities are provided broad levels of autonomy; creating a vector of insider acts with little-to-no human oversight or control. As AI agents assume many of the orchestration layers once reserved for humans, they do so without some of the restricted permissions that generally bind service accounts. With broad levels of accessibility and authority, these non-human identities (NHIs) can themselves become targets of insider intent.  Commonly, this refers to the increasing risks of prompt injection, poisoning, or other types of embedded bias. In many ways, this mirrors the risks of social engineering traditionally faced by humans. Even without deliberate or malicious efforts to corrupt them, AI systems and AI agents can carry out unintended actions; creating vulnerabilities and opportunities for insider harm.

How to defend against AI-powered insider threats

The increasing attack surfaces created or facilitated by AI is a growing concern.  In Darktrace’s own AI cybersecurity research, the risks introduced, and acknowledged, through the proliferation of AI tools and systems continues to outstrip traditional policies and governance guardrails. 22% of respondents in the survey cited ‘insider misuse aided by generative AI’ as a major threat concern.  And yet, in the same survey, only 37% of all respondents have formal policies in place to manage the safe and responsible use of AI.  This draws a significant and worrying delta between the known risks and threat concerns, and the ability (and resources) to mitigate them.

What can CISOs and SOC leaders do to protect their organization from AI insider threats?  

Given the rapid adaptation, adoption, and scale of AI systems, implementing the right levels of AI governance is non-negotiable. Getting the correct balance between AI-driven productivity gains and careful compliance will lead to long-term benefits. Adapting traditional insider threat structures to account for newer risks posed through the use of AI will be crucial. And understanding the value of AI systems that add to your cybersecurity resilience rather than imperil it will be essential.

For those responsible for the security and protection of their business assets and data holdings, the way AI has changed the paradigm of insider threats can seem daunting.  Adopting strong, and suitable AI governance can become difficult to introduce due to the volume and complexity of systems needed to be monitored. As well as traditional insider threat mitigations such as user monitoring, access controls and active management, the speed and autonomy of some AI systems need different, as well as additional layers of control.  

How Darktrace helps protect against AI-powered insider threats

Darktrace has demonstrated that, through platforms such as our proprietary Cyber AI Analyst, and our latest product Darktrace / SECURE AI, there are ways AI systems can be self-learning, self-critical and resilient to unpredictable AI behavior whilst still offering impressive returns; complementing traditional SOC and CISO strategies to combat insider threat.  

With / SECURE AI, some of the ephemeral risks drawn through AI use can be more easily governed.  Specifically, the ability to monitor conversational prompts (which can both affect AI outputs as well as highlight potential attempts at manipulation of AI; raising early flags of insider intent); the real-time observation of AI usage and development (highlighting potential blind-spots between AI development and deployment); shadow AI detection (surfacing unapproved tools and agents across your IT stack) and; the ability to know which identities (human or non-human) have permission access. All these features build on the existing foundations of strong insider threat management structures.  

How to take a defense-in-depth approach to AI-powered insider threats

Even without these tools, there are four key areas where robust, more effective controls can mitigate AI-powered insider threat.  Each of the below offers a defencce-in-depth approach: layering acknowledgement and understanding of an insider vector with controls that can bolster your defenses.  

Identity and access controls

Having a clear understanding of the entities that can access your sensitive information, assets and personnel is the first step in understanding the landscape in which insider harm can occur.  AI has shown that it is not just flesh and bone operators who can administer insider threats; Non-Human Identities (such as agentic AI systems) can operate with autonomy and freedom if they have the right credentials. By treating NHIs in the same way as human operators (rather than helpful machine-based tools), and adding similar mitigation and management controls, you can protect both your business, and your business-based identities from insider-related attention.

Visibility and shadow AI detection

Configuring AI systems carefully, as well as maintaining internal monitoring, can help identify ‘shadow AI’ usage; defined as the use of unsanctioned AI tools within the workplace1 (this topic was researched in Darktrace’s own paper on "How to secure AI in the enterprise". The adoption of shadow AI could be the result of deliberate preference, or ‘shortcutting’; where individuals use systems and models they are familiar with, even if unsanctioned. As well as some performance risks inherent with the use of shadow AI (such as data leakage and unwanted actions), it could also be a dangerous precursor for insider-related harm (either through deliberate attempts to subvert regular monitoring, or by opening vulnerabilities through unpatched or unaccredited tooling).

Prompt and Output Guardrails

The ability to introduce guardrails for AI systems offers something of a traditional “perimeter protection” layer in AI defense architecture; checking prompts and outputs against known threat vectors, or insider threat methodologies. Alone, such traditional guardrails offer limited assurance.  But, if tied with behavior-centric threat detection, and an enforcement system that deters both malicious and accidental insider activities, this would offer considerable defense- in- depth containment.  

Forensic logging and incident readiness response

The need for detection, data capture, forensics, and investigation are inherent elements of any good insider threat strategy. To fully understand the extent or scope of any suspected insider activity (such as understanding if it was deliberate, targeted, or likely to occur again), this rich vein of analysis could prove invaluable.  As the nature of business increasingly turns ephemeral; with assets secured in remote containers, information parsed through temporary or cloud-based architecture, and access nodes distributed beyond the immediate visibility of internal security teams, the development of AI governance through containment, detection, and enforcement will grow ever more important.

Enabling these controls can offer visibility and supervision over some of the often-expressed risks about AI management. With the right kind of data analytics, and with appropriate human oversight for high-risk actions, it can illuminate the core concerns expressed through a new paradigm of AI-powered insider threats by:

  • Ensuring deliberately mis-configured AI systems are exposed through regular monitoring.
  • Highlighting changes in systems-based activity that might indicate harmful insider actions; whether malicious or accidental.
  • Promoting a secure-by-design process that discourages and deters insider-related ambitions.
  • Ensuring the control plane for identity-based access spans humans, NHIs and AI models, and:
  • Offering positive containment strategies that will help curate the extent of AI control, and minimize unwanted activities.

Why insider threat remains a human challenge

At its root, and however it has been configured, AI is still an algorithmic tool; something designed to automate, process and manage computational functions at machine speed, and boost productivity.  Even with the best cybersecurity defenses in place, the success of an insider threat management program will still depend on the ability of human operators to identify, triage, and manage the insider threat attack surface.  

AI governance policies, human-in-the-loop break points, and automated monitoring functions will not guard against acts of insider harm unless there is intention to manage this proactively, and through a strong culture of how to guard against abuses of trust and responsibility.

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Jason Lusted
AI Governance Advisor
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