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July 6, 2023

How Darktrace Foiled QR Code Phishing

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06
Jul 2023
Explore Darktrace's successful detection of QR code phishing. Understand the methods used to thwart these sophisticated cyber threats.

What is a QR Code?

Invented by a Japanese company in 1994 to label automobile parts, Quick Response codes, best known as QR codes, are rapidly becoming ubiquitous everywhere in the world. Their design, inspired by the board and black and white pieces of the game of Go, permits the storage of more information than regular barcodes and to access that information more quickly. The COVID-19 pandemic contributed to their increased popularity as it conveniently replaced physical media of all types for the purpose of content sharing. It is now common to see them in restaurant menus, plane tickets, advertisements and even in stickers containing minimal to no text pasted on lamp posts and other surfaces, enticing passers-by to scan its content. 

QR Code Phishing Attacks (Quishing)

Recently, threat actors have been identified using QR codes too to embed malicious URLs leading the unsuspecting user to compromised websites containing malware or designed to harvest credentials. In the past month, Darktrace has observed an increase in the number of phishing emails leveraging malicious QR codes for malware distribution and/or credential harvesting, a new form of social engineering attack labelled “Quishing” (i.e., QR code phishing).

Between June 13 and June 22, 2023, Darktrace protected a tech company against one such Quishing attack when five of its senior employees were sent malicious emails impersonating the company’s IT department. The emails contained a QR code that led to a login page designed to harvest the credentials of these senior staff members. Fortunately for the customer, Darktrace / EMAIL thwarted this phishing campaign in the first instance and the emails never reached the employee inboxes. 

Trends in Quishing Attacks

The Darktrace/Email team have noticed a recent and rapid increase in QR code abuse, suggesting that it is a growing tactic used by threat actors to deliver malicious payload links. This trend has also been observed by other security solutions [1] [2] [3] [4]. The Darktrace/Email team has identified malicious emails abusing QR codes in multiple ways. Examples include embedded image links which load a QR code and QR code images being delivered as attachments, such as those explored in this case study. Darktrace/Email is continually refining its detection of malicious QR codes and QR code extraction capabilities so that it can detect and block them regardless of their size and location within the email.   

Quishing Attack Overview

The attack consisted of five emails, each sent from different sender and envelope addresses, displayed common points between them. The emails all conveyed a sense of urgency, either via the use of words such as “urgent”, “now”, “required” or “important” in the subject field or by marking the email as high priority, thus making the recipient believe the message is pressing and requires immediate attention. 

Additionally, the subject of three of the emails directly referred to two factor authentication (2FA) enabling or QR code activation. Another particularity of these emails was that three of them attempted to impersonate the internal IT team of the company by inserting the company domain alongside strings, such as “it-desk” and “IT”, into the personal field of the emails. Email header fields like this are often abused by attackers to trick users by pretending to be an internal department or senior employee, thus avoiding more thorough validation checks. Both instilling a sense of urgency and including a known domain or name in the personal field are techniques that help draw attention to the email and maximize the chances that it is opened and engaged by the recipient. 

However, threat actors also need to make sure that the emails actually reach the intended inboxes, and this can be done in several ways. In this case, several tactics were employed. Two of the five emails were sent from legitimate sender addresses that successfully passed SPF validation, suggesting they were sent from compromised accounts. SPF is a standard email authentication method that tells the receiving email servers whether emails have been sent from authorized servers for a given domain. Without SPF validation, emails are more likely to be categorized as spam and be sent to the junk folder as they do not come from authorized sources.

Another of the malicious emails, which also passed SPF checks, used a health care facility company domain in the header-from address field but was actually sent from a different domain (i.e., envelope domain), which lowers the value of the SPF authentication. However, the envelope domain observed in this instance belonged to a company recently acquired by the tech company targeted by the campaign.

This shows a high level of targeting from the attackers, who likely hoped that this detail would make the email more familiar and less suspicious. In another case, the sender domain (i.e., banes-gn[.]com) had been created just 6 days prior, thus lowering the chances of there being open-source intelligence (OSINT) available on the domain. This reduces the chances of the email being detected by traditional email security solutions relying on signatures and known-bad lists.

Darktrace Detects Quishing Attack

Despite its novelty, the domain was detected and assessed as highly suspicious by Darktrace. Darktrace/Email was able to recognize all of the emails as spoofing and impersonation attempts and applied the relevant tags to them, namely “IT Impersonation” and “Fake Account Alert”, depending on the choice of personal field and subject. The senders of the five emails had no prior history or association with the recipient nor the company as no previous correspondence had been observed between the sender and recipient. The tags applied informed on the likely intent and nature of the suspicious indicators present in the email, as shown in Figure 1. 

Darktrace/Email UI
Figure 1: Email log overview page, displaying important information clearly and concisely. 

Quishing Attack Tactics

Minimal Plain Text

Another characteristic shared by these emails was that they had little to no text included in the body of the email and they did not contain a plain text portion, as shown in Figure 2. For most normal emails sent by email clients and most automated programs, an email will contain an HTML component and a text component, in addition to any potential attachments present. All the emails had one image attachment, suggesting the bulk of the message was displayed in the image rather than the email body. This hinders textual analysis and filtering of the email for suspicious keywords and language that could reveal its phishing intent. Additionally, the emails were well-formatted and used the logo of the well-known corporation Microsoft, suggesting some level of technical ability on the part of the attackers. 

Figure 2: Email body properties giving additional insights into the content of the email. 

Attachment and link payloads

The threat actors employed some particularly innovative and novel techniques with regards to the attachments and link payloads within these emails. As previously stated, all emails contained an image attachment and one or two links. Figure 3 shows that Darktrace/Email detected that the malicious links present in these emails were located in the attachments, rather than the body of the email. This is a technique often employed by threat actors to bypass link analysis by security gateways. Darktrace/Email was also able to detect this link as a QR code link, as shown in Figure 4.

Figure 3: Further properties and metrics regarding the location of the link within the email. 
Figure 4: Darktrace / EMAIL analyzes multiple metrics and properties related to links, some of which are detailed here. 

The majority of the text, as well as the malicious payload, was contained within the image attachment, which for one of the emails looked like this: 

example of quishing email
Figure 5: Redacted screenshot of the image payload contained in one of the emails. 

Convincing Appearance

As shown, the recipient is asked to setup 2FA authentication for their account within two days if they don’t want to be locked out. The visual formatting of the image, which includes a corporate logo and Privacy Statement and Acceptable Use Policy notices, is well balanced and convincing. The payload, in this case the QR code containing a malicious link, is positioned in the centre so as to draw attention and encourage the user to scan and click. This is a type of email employees are increasingly accustomed to receiving in order to log into corporate networks and applications. Therefore, recipients of such malicious emails might assume represents expected business activity and thus engage with the QR code without questioning it, especially if the email is claiming to be from the IT department.  

Malicious Redirection

Two of the Quishing emails contained links to legitimate file storage and sharing solutions Amazon Web Services (AWS) and and InterPlanetary File System (IPFS), whose domains are less likely to be blocked by traditional security solutions. Additionally, the AWS domain link contained a redirect to a different domain that has been flagged as malicious by multiple security vendors [5]. Malicious redirection was observed in four of the five emails, initially from well-known and benign services’ domains such as bing[.]com and login[.]microsoftonline[.]com. This technique allows attackers to hide the real destination of the link from the user and increase the likelihood that the link is clicked. In two of the emails, the redirect domain had only recently been registered, and in one case, the redirect domain observed was hosted on the new .zip top level domain (i.e., docusafe[.]zip). The domain name suggests it is attempting to masquerade as a compressed file containing important documentation. As seen in Figure 6, a new Darktrace/Email feature allows customers to safely view the final destination of the link, which in this case was a seemingly fake Microsoft login page which could be used to harvest corporate credentials.

Figure 6: Safe preview available from the Darktrace/Email Console showing the destination webpage of one of the redirect links observed.

Gathering Account Credentials

Given the nature of the landing page, it is highly likely that this phishing campaign had the objective of stealing the recipients’ credentials, as further indicated by the presence of the recipients’ email addresses in the links. Additionally, these emails were sent to senior employees, likely in an attempt to gather high value credentials to use in future attacks against the company. Had they succeeded, this would have represented a serious security incident, especially considering that 61% of attacks in 2023 involved stolen or hacked credentials according to Verizon’s 2023 data breach investigations report [6]. However, these emails received the highest possible anomaly score (100%) and were held by Darktrace/Email, thus ensuring that their intended recipients were never exposed to them. 

Looking at the indicators of compromise (IoCs) identified in this campaign, it appears that several of the IPs associated with the link payloads have been involved in previous phishing campaigns. Exploring the relations tab for these IPs in Virus Total, some of the communicating files appear to be .eml files and others have generic filenames including strings such as “invoice” “remittance details” “statement” “voice memo”, suggesting they have been involved in other phishing campaigns seemingly related to payment solicitation and other fraud attempts.

Figure 7: Virus Total’s relations tab for the IP 209.94.90[.]1 showing files communicating with the IP. 

Conclusion

Even though the authors of this Quishing campaign used all the tricks in the book to ensure that their emails would arrive unactioned by security tools to the targeted high value recipients’ inboxes, Darktrace/Email was able to immediately recognize the phishing attempts for what they were and block the emails from reaching their destination. 

This campaign used both classic and novel tactics, techniques, and procedures, but ultimately were detected and thwarted by Darktrace/Email. It is yet another example of the increasing attack sophistication mentioned in a previous Darktrace blog [7], wherein the attack landscape is moving from low-sophistication, low-impact, and generic phishing tactics to more targeted, sophisticated and higher impact attacks. Darktrace/Email does not rely on historical data nor known-bad lists and is best positioned to protect organizations from these highly targeted and sophisticated attacks.

References

[1] https://www.infosecurity-magazine.com/opinions/qr-codes-vulnerability-cybercrimes/ 

[2] https://www.helpnetsecurity.com/2023/03/21/qr-scan-scams/ 

[3] https://www.techtarget.com/searchsecurity/feature/Quishing-on-the-rise-How-to-prevent-QR-code-phishing 

[4] https://businessplus.ie/tech/qr-code-phishing-hp/ 

[5] https://www.virustotal.com/gui/domain/fistulacure.com

[6] https://www.verizon.com/business/en-gb/resources/reports/dbir/ ; https://www.verizon.com/business/en-gb/resources/reports/dbir/

[7] https://darktrace.com/blog/shifting-email-conversation 

Darktrace Model Detections 

Association models

No Sender or Content Association

New Sender

Unknown Sender

Low Sender Association

Link models

Focused Link to File Storage

Focused Rare Classified Links

New Unknown Hidden Redirect

High Risk Link + Low Sender Association

Watched Link Type

High Classified Link

File Storage From New

Hidden Link To File Storage

New Correspondent Classified Link

New Unknown Redirect

Rare Hidden Classified Link

Rare Hidden Link

Link To File Storage

Link To File Storage and Unknown Sender

Open Redirect

Unknown Sender Isolated Rare Link

Visually Prominent Link

Visually Prominent Link Unexpected For Sender

Low Link Association

Low Link Association and Unknown Sender

Spoof models

Fake Support Style

External Domain Similarities

Basic Known Entity Similarities

Unusual models

Urgent Request Banner

Urgent Request Banner + Basic Suspicious Sender

Very Young Header Domain

Young Header Domain

Unknown User Tracking

Unrelated Personal Name Address

Unrelated Personal Name Address + Freemail

Unusual Header TLD

Unusual Connection From Unknown

Unbroken Personal

Proximity models

Spam + Unknown Sender

Spam

Spam models

Unlikely Freemail Correspondence

Unlikely Freemail Personalization

General Indicators models

Incoming Mail Security Warning Message

Darktrace Model Tags

Credential Harvesting

Internal IT Impersonation

Multistage payload

Lookalike Domain

Phishing Link

Email Account Takeover

Fake Account Alert

Low Mailing History

No Association

Spoofing Indicators

Unknown Correspondent

VIP

Freemail

IoC - Type - Description & Confidence

fistulacure[.]com

domain

C2 Infrastructure

docusafe[.]zip

domain

Possible C2 Infrastructure

mwmailtec[.]com

domain

Possible C2 Infrastructure

czeromedia[.]com

domain

Possible C2 Infrastructure

192.40.165[.]109

IP address

Probable C2 Infrastructure

209.94.90[.]1

IP address

C2 Infrastructure

52.61.107[.]58

IP address

Possible C2 Infrastructure

40.126.32[.]133

IP address

Possible C2 Infrastructure

211.63.158[.]157

IP address

Possible C2 Infrastructure

119.9.27[.]129

IP address

Possible C2 Infrastructure

184.25.204[.]33

IP address

Possible C2 Infrastructure

40.107.8[.]107

IP address

Probable C2 Infrastructure

40.107.212[.]111

IP address

Possible Infrastructure

27.86.113[.]2

IP address

Possible C2 Infrastructure

192.40.191[.]19

IP address

Possible C2 Infrastructure

157.205.202[.]217

IP address

Possible C2 Infrastructure

a31f1f6063409ecebe8893e36d0048557142cbf13dbaf81af42bf14c43b12a48

SHA256 hash

Possible Malicious File

4c4fb35ab6445bf3749b9d0ab1b04f492f2bc651acb1bbf7af5f0a47502674c9

SHA256 hash

Possible Malicious File

f9c51d270091c34792b17391017a09724d9a7890737e00700dc36babeb97e252

SHA256 hash

Possible Malicious File

9f8ccfd616a8f73c69d25fd348b874d11a036b4d2b3fc7dbb99c1d6fa7413d9a

SHA256 hash

Possible Malicious File

b748894348c32d1dc5702085d70d846c6dd573296e79754df4857921e707c439

SHA256 hash

Possible Malicious File

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.
Author
Alexandra Sentenac
Cyber Analyst
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February 11, 2025

NIS2 Compliance: Interpreting 'State-of-the-Art' for Organisations

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NIS2 Background

17 October 2024 marked the deadline for European Union (EU) Member States to implement the NIS2 Directive into national law. The Directive aims to enhance the EU’s cybersecurity posture by establishing a high common level of cybersecurity for critical infrastructure and services. It builds on its predecessor, the 2018 NIS Directive, by expanding the number of sectors in scope, enforcing greater reporting requirements and encouraging Member States to ensure regulated organisations adopt ‘state-of-the-art' security measures to protect their networks, OT and IT systems.  

Timeline of NIS2
Figure 1: Timeline of NIS2

The challenge of NIS2 & 'state-of-the-art'

Preamble (51) - "Member States should encourage the use of any innovative technology, including artificial intelligence, the use of which could improve the detection and prevention of cyberattacks, enabling resources to be diverted towards cyberattacks more effectively."
Article 21 - calls on Member States to ensure that essential and important entities “take appropriate and proportionate” cyber security measures, and that they do so by “taking into account the state-of-the-art and, where applicable, relevant European and international standards, as well as the cost of implementation.”

Regulartory expectations and ambiguity of NIS2

While organisations in scope can rely on technical guidance provided by ENISA1 , the EU’s agency for cybersecurity, or individual guidelines provided by Member States or Public-Private Partnerships where they have been published,2 the mention of ‘state-of-the-art' remains up to interpretation in most Member States. The use of the phrase implies that cybersecurity measures must evolve continuously to keep pace with emerging threats and technological advancements without specifying what ‘state-of-the-art’ actually means for a given context and risk.3  

This ambiguity makes it difficult for organisations to determine what constitutes compliance at any given time and could lead to potential inconsistencies in implementation and enforcement. Moreover, the rapid pace of technological change means that what is considered "state-of-the-art" today will become outdated, further complicating compliance efforts.

However, this is not unique to NIS regulation. As EU scholars have noted, while “state-of-the-art" is widely referred to in legal text relating to technology, there is no standardised legal definition of what it actually constitutes.4

Defining state-of-the-art cybersecurity

In this blog, we outline technical considerations for state-of-the-art cybersecurity. We draw from expertise within our own business and in academia as well as guidelines and security standards set by national agencies, such as Germany’s Federal Office for Information Security (BSI) or Spain’s National Security Framework (ENS), to put forward five criteria to define state-of-the-art cybersecurity.

The five core criteria include:

  • Continuous monitoring
  • Incident correlation
  • Detection of anomalous activity
  • Autonomous response
  • Proactive cyber resilience

These principles build on long-standing security considerations, such as business continuity, vulnerability management and basic security hygiene practices.  

Although these considerations are written in the context of the NIS2 Directive, they are likely to also be relevant for other jurisdictions. We hope these criteria help organisations understand how to best meet their responsibilities under the NIS2 Directive and assist Competent Authorities in defining compliance expectations for the organisations they regulate.  

Ultimately, adopting state-of-the-art cyber defences is crucial for ensuring that organisations are equipped with the best tools to combat new and fast-growing threats. Leading technical authorities, such as the UK National Cyber Security Centre (NCSC), recognise that adoption of AI-powered cyber defences will offset the increased volume and impact of AI on cyber threats.5

State of the art cybersecurity in the context of NIS2

1. Continuous monitoring

Continuous monitoring is required to protect an increasingly complex attack surface from attackers.

First, organisations' attack surfaces have expanded following the widespread adoption of hybrid or cloud infrastructures and the increased adoption of connected Internet of Things (IoT) devices.6 This exponential growth creates a complex digital environment for organisations, making it difficult for security teams to track all internet-facing assets and identify potential vulnerabilities.

Second, with the significant increase in the speed and sophistication of cyber-attacks, organisations face a greater need to detect security threats and non-compliance issues in real-time.  

Continuous monitoring, defined by the U.S. National Institute of Standards and Technology (NIST) as the ability to maintain “ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions,”7 has therefore become a cornerstone of an effective cybersecurity strategy. By implementing continuous monitoring, organisations can ensure a real-time understanding of their attack surface and that new external assets are promptly accounted for. For instance, Spain’s technical guidelines for regulation, as set forth by the National Security Framework (Royal Decree 311/2022), highlight the importance of adopting continuous monitoring to detect anomalous activities or behaviours and to ensure timely responses to potential threats (article 10).8  

This can be achieved through the following means:  

All assets that form part of an organisation's estate, both known and unknown, must be identified and continuously monitored for current and emerging risks. Germany’s BSI mandates the continuous monitoring of all protocol and logging data in real-time (requirement #110).9 This should be conducted alongside any regular scans to detect unknown devices or cases of shadow IT, or the use of unauthorised or unmanaged applications and devices within an organisation, which can expose internet-facing assets to unmonitored risks. Continuous monitoring can therefore help identify potential risks and high-impact vulnerabilities within an organisation's digital estate and eliminate potential gaps and blind spots.

Organisations looking to implement more efficient continuous monitoring strategies may turn to automation, but, as the BSI notes, it is important for responsible parties to be immediately warned if an alert is raised (reference 110).10 Following the BSI’s recommendations, the alert must be examined and, if necessary, contained within a short period of time corresponding with the analysis of the risk at hand.

Finally, risk scoring and vulnerability mapping are also essential parts of this process. Looking across the Atlantic, the US’ National Institute of Standards and Technology (NIST) defines continuous monitoring as “maintaining ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions”.11 Continuous monitoring helps identify potential risks and significant vulnerabilities within an organisation's digital assets, fostering a dynamic understanding of risk. By doing so, risk scoring and vulnerability mapping allows organisations to prioritise the risks associated with their most critically exposed assets.

2. Correlation of incidents across your entire environment

Viewing and correlating incident alerts when working with different platforms and tools poses significant challenges to SecOps teams. Security professionals often struggle to cross-reference alerts efficiently, which can lead to potential delays in identifying and responding to threats. The complexity of managing multiple sources of information can overwhelm teams, making it difficult to maintain a cohesive understanding of the security landscape.

This fragmentation underscores the need for a centralised approach that provides a "single pane of glass" view of all cybersecurity alerts. These systems streamline the process of monitoring and responding to incidents, enabling security teams to act more swiftly and effectively. By consolidating alerts into a unified interface, organisations can enhance their ability to detect and mitigate threats, ultimately improving their overall security posture.  

To achieve consolidation, organisations should consider the role automation can play when reviewing and correlating incidents. This is reflected in Spain’s technical guidelines for national security regulations regarding the requirements for the “recording of activity” (reinforcement R5).12 Specifically, the guidelines state that:  

"The system shall implement tools to analyses and review system activity and audit information, in search of possible or actual security compromises. An automatic system for collection of records, correlation of events and automatic response to them shall be available”.13  

Similarly, the German guidelines stress that automated central analysis is essential not only for recording all protocol and logging data generated within the system environment but also to ensure that the data is correlated to ensure that security-relevant processes are visible (article 115).14

Correlating disparate incidents and alerts is especially important when considering the increased connectivity between IT and OT environments driven by business and functional requirements. Indeed, organisations that believe they have air-gapped systems are now becoming aware of points of IT/OT convergence within their systems. It is therefore crucial for organisations managing both IT and OT environments to be able to visualise and secure devices across all IT and OT protocols in real-time to identify potential spillovers.  

By consolidating data into a centralised system, organisations can achieve a more resilient posture. This approach exposes and eliminates gaps between people, processes, and technology before they can be exploited by malicious actors. As seen in the German and Spanish guidelines, a unified view of security alerts not only enhances the efficacy of threat detection and response but also ensures comprehensive visibility and control over the organisation's cybersecurity posture.

3. Detection of anomalous activity  

Recent research highlights the emergence of a "new normal" in cybersecurity, marked by an increase in zero-day vulnerabilities. Indeed, for the first time since sharing their annual list, the Five Eyes intelligence alliance reported that in 2023, the majority of the most routinely exploited vulnerabilities were initially exploited as zero-days.15  

To effectively combat these advanced threats, policymakers, industry and academic stakeholders alike recognise the importance of anomaly-based techniques to detect both known and unknown attacks.

As AI-enabled threats become more prevalent,16 traditional cybersecurity methods that depend on lists of "known bads" are proving inadequate against rapidly evolving and sophisticated attacks. These legacy approaches are limited because they can only identify threats that have been previously encountered and cataloged. However, cybercriminals are constantly developing new, never-before-seen threats, such as signatureless ransomware or living off the land techniques, which can easily bypass these outdated defences.

The importance of anomaly detection in cybersecurity can be found in Spain’s technical guidelines, which states that “tools shall be available to automate the prevention and response process by detecting and identifying anomalies17” (reinforcement R4 prevention and automatic response to "incident management”).  

Similarly, the UK NCSC’s Cyber Assessment Framework (CAF) highlights how anomaly-based detection systems are capable of detecting threats that “evade standard signature-based security solutions” (Principle C2 - Proactive Security Event Discovery18). The CAF’s C2 principle further outlines:  

“The science of anomaly detection, which goes beyond using pre-defined or prescriptive pattern matching, is a challenging area. Capabilities like machine learning are increasingly being shown to have applicability and potential in the field of intrusion detection.”19

By leveraging machine learning and multi-layered AI techniques, organisations can move away from static rules and signatures, adopting a more behavioural approach to identifying and containing risks. This shift not only enhances the detection of emerging threats but also provides a more robust defence mechanism.

A key component of this strategy is behavioral zero trust, which focuses on identifying unauthorized and out-of-character attempts by users, devices, or systems. Implementing a robust procedure to verify each user and issuing the minimum required access rights based on their role and established patterns of activity is essential. Organisations should therefore be encouraged to follow a robust procedure to verify each user and issue the minimum required access rights based on their role and expected or established patterns of activity. By doing so, organisations can stay ahead of emerging threats and embrace a more dynamic and resilient cybersecurity strategy.  

4. Autonomous response

The speed at which cyber-attacks occur means that defenders must be equipped with tools that match the sophistication and agility of those used by attackers. Autonomous response tools are thus essential for modern cyber defence, as they enable organisations to respond to both known and novel threats in real time.  

These tools leverage a deep contextual and behavioral understanding of the organisation to take precise actions, effectively containing threats without disrupting business operations.

To avoid unnecessary business disruptions and maintain robust security, especially in more sensitive networks such as OT environments, it is crucial for organisations to determine the appropriate response depending on their environment. This can range from taking autonomous and native actions, such as isolating or blocking devices, or integrating their autonomous response tool with firewalls or other security tools to taking customized actions.  

Autonomous response solutions should also use a contextual understanding of the business environment to make informed decisions, allowing them to contain threats swiftly and accurately. This means that even as cyber-attacks evolve and become more sophisticated, organisations can maintain continuous protection without compromising operational efficiency.  

Indeed, research into the adoption of autonomous cyber defences points to the importance of implementing “organisation-specific" and “context-informed” approaches.20  To decide the appropriate level of autonomy for each network action, it is argued, it is essential to use evidence-based risk prioritisation that is customised to the specific operations, assets, and data of individual enterprises.21

By adopting autonomous response solutions, organisations can ensure their defences are as dynamic and effective as the threats they face, significantly enhancing their overall security posture.

5. Proactive cyber resilience  

Adopting a proactive approach to cybersecurity is crucial for organisations aiming to safeguard their operations and reputation. By hardening their defences enough so attackers are unable to target them effectively, organisations can save significant time and money. This proactive stance helps reduce business disruption, reputational damage, and the need for lengthy, resource-intensive incident responses.

Proactive cybersecurity incorporates many of the strategies outlined above. This can be seen in a recent survey of information technology practitioners, which outlines four components of a proactive cybersecurity culture: (1) visibility of corporate assets, (2) leveraging intelligent and modern technology, (3) adopting consistent and comprehensive training methods and (4) implementing risk response procedures.22 To this, we may also add continuous monitoring which allows organisations to understand the most vulnerable and high-value paths across their architectures, allowing them to secure their critical assets more effectively.  

Alongside these components, a proactive cyber strategy should be based on a combined business context and knowledge, ensuring that security measures are aligned with the organisation's specific needs and priorities.  

This proactive approach to cyber resilience is reflected in Spain’s technical guidance (article 8.2): “Prevention measures, which may incorporate components geared towards deterrence or reduction of the exposure surface, should eliminate or reduce the likelihood of threats materializing.”23 It can also be found in the NCSC’s CAF, which outlines how organisations can achieve “proactive attack discovery” (see Principle C2).24 Likewise, Belgium’s NIS2 transposition guidelines mandate the use of preventive measures to ensure the continued availability of services in the event of exceptional network failures (article 30).25  

Ultimately, a proactive approach to cybersecurity not only enhances protection but also lowers regulatory risk and supports the overall resilience and stability of the organisation.

Looking forward

The NIS2 Directive marked a significant regulatory milestone in strengthening cybersecurity across the EU.26 Given the impact of emerging technologies, such as AI, on cybersecurity, it is to see that Member States are encouraged to promote the adoption of ‘state-of-the-art' cybersecurity across regulated entities.  

In this blog, we have sought to translate what state-of-the-art cybersecurity may look like for organisations looking to enhance their cybersecurity posture. To do so, we have built on existing cybersecurity guidance, research and our own experience as an AI-cybersecurity company to outline five criteria: continuous monitoring, incident correlation, detection of anomalous activity, autonomous response, and proactive cyber resilience.

By embracing these principles and evolving cybersecurity practices in line with the state-of-the-art, organisations can comply with the NIS2 Directive while building a resilient cybersecurity posture capable of withstanding evolutions in the cyber threat landscape. Looking forward, it will be interesting to see how other jurisdictions embrace new technologies, such as AI, in solving the cybersecurity problem.

References

[1] https://www.enisa.europa.eu/publications/implementation-guidance-on-nis-2-security-measures

[2] https://www.teletrust.de/fileadmin/user_upload/2023-05_TeleTrusT_Guideline_State_of_the_art_in_IT_security_EN.pdf

[3] https://kpmg.com/uk/en/home/insights/2024/04/what-does-nis2-mean-for-energy-businesses.html

[4] https://orbilu.uni.lu/bitstream/10993/50878/1/SCHMITZ_IFIP_workshop_sota_author-pre-print.pdf

[5]https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[6] https://www.sciencedirect.com/science/article/pii/S2949715923000793

[7]https://csrc.nist.gov/glossary/term/information_security_continuous_monitoring

[8]https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[10] https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/KRITIS/Konkretisierung_Anforderungen_Massnahmen_KRITIS.html

[11] https://nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-137.pdf

[12] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[13] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[14] https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/KRITIS/Konkretisierung_Anforderungen_Massnahmen_KRITIS.html

[15] https://therecord.media/surge-zero-day-exploits-five-eyes-report

[16] https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[17] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[18] https://www.ncsc.gov.uk/collection/cyber-assessment-framework/caf-objective-c-detecting-cyber-security-events/principle-c2-proactive-security-event-discovery

[19] https://www.ncsc.gov.uk/collection/cyber-assessment-framework/caf-objective-c-detecting-cyber-security-events/principle-c2-proactive-security-event-discovery

[20] https://cetas.turing.ac.uk/publications/autonomous-cyber-defence-autonomous-agents

[21] https://cetas.turing.ac.uk/publications/autonomous-cyber-defence-autonomous-agents

[22] https://www.researchgate.net/publication/376170443_Cultivating_Proactive_Cybersecurity_Culture_among_IT_Professional_to_Combat_Evolving_Threats

[23] https://ens.ccn.cni.es/es/docman/documentos-publicos/39-boe-a-2022-7191-national-security-framework-ens/file

[24] https://www.ncsc.gov.uk/collection/cyber-assessment-framework/caf-objective-c-detecting-cyber-security-events/principle-c2-proactive-security-event-discovery

[25] https://www.ejustice.just.fgov.be/mopdf/2024/05/17_1.pdf#page=49

[26] ENISA, NIS Directive 2

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Livia Fries
Public Policy Manager, EMEA

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February 10, 2025

From Hype to Reality: How AI is Transforming Cybersecurity Practices

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AI is everywhere, predominantly because it has changed the way humans interact with data. AI is a powerful tool for data analytics, predictions, and recommendations, but accuracy, safety, and security are paramount for operationalization.

In cybersecurity, AI-powered solutions are becoming increasingly necessary to keep up with modern business complexity and this new age of cyber-threat, marked by attacker innovation, use of AI, speed, and scale. The emergence of these new threats calls for a varied and layered approach in AI security technology to anticipate asymmetric threats.

While many cybersecurity vendors are adding AI to their products, they are not always communicating the capabilities or data used clearly. This is especially the case with Large Language Models (LLMs). Many products are adding interactive and generative capabilities which do not necessarily increase the efficacy of detection and response but rather are aligned with enhancing the analyst and security team experience and data retrieval.

Consequently, many  people erroneously conflate generative AI with other types of AI. Similarly, only 31% of security professionals report that they are “very familiar” with supervised machine learning, the type of AI most often applied in today’s cybersecurity solutions to identify threats using attack artifacts and facilitate automated responses. This confusion around AI and its capabilities can result in suboptimal cybersecurity measures, overfitting, inaccuracies due to ineffective methods/data, inefficient use of resources, and heightened exposure to advanced cyber threats.

Vendors must cut through the AI market and demystify the technology in their products for safe, secure, and accurate adoption. To that end, let’s discuss common AI techniques in cybersecurity as well as how Darktrace applies them.

Modernizing cybersecurity with AI

Machine learning has presented a significant opportunity to the cybersecurity industry, and many vendors have been using it for years. Despite the high potential benefit of applying machine learning to cybersecurity, not every AI tool or machine learning model is equally effective due to its technique, application, and data it was trained on.

Supervised machine learning and cybersecurity

Supervised machine models are trained on labeled, structured data to facilitate automation of a human-led trained tasks. Some cybersecurity vendors have been experimenting with supervised machine learning for years, with most automating threat detection based on reported attack data using big data science, shared cyber-threat intelligence, known or reported attack behavior, and classifiers.

In the last several years, however, more vendors have expanded into the behavior analytics and anomaly detection side. In many applications, this method separates the learning, when the behavioral profile is created (baselining), from the subsequent anomaly detection. As such, it does not learn continuously and requires periodic updating and re-training to try to stay up to date with dynamic business operations and new attack techniques. Unfortunately, this opens the door for a high rate of daily false positives and false negatives.

Unsupervised machine learning and cybersecurity

Unlike supervised approaches, unsupervised machine learning does not require labeled training data or human-led training. Instead, it independently analyzes data to detect compelling patterns without relying on knowledge of past threats. This removes the dependency of human input or involvement to guide learning.

However, it is constrained by input parameters, requiring a thoughtful consideration of technique and feature selection to ensure the accuracy of the outputs. Additionally, while it can discover patterns in data as they are anomaly-focused, some of those patterns may be irrelevant and distracting.

When using models for behavior analytics and anomaly detection, the outputs come in the form of anomalies rather than classified threats, requiring additional modeling for threat behavior context and prioritization. Anomaly detection performed in isolation can render resource-wasting false positives.

LLMs and cybersecurity

LLMs are a major aspect of mainstream generative AI, and they can be used in both supervised and unsupervised ways. They are pre-trained on massive volumes of data and can be applied to human language, machine language, and more.

With the recent explosion of LLMs in the market, many vendors are rushing to add generative AI to their products, using it for chatbots, Retrieval-Augmented Generation (RAG) systems, agents, and embeddings. Generative AI in cybersecurity can optimize data retrieval for defenders, summarize reporting, or emulate sophisticated phishing attacks for preventative security.

But, since this is semantic analysis, LLMs can struggle with the reasoning necessary for security analysis and detection consistently. If not applied responsibly, generative AI can cause confusion by “hallucinating,” meaning referencing invented data, without additional post-processing to decrease the impact or by providing conflicting responses due to confirmation bias in the prompts written by different security team members.

Combining techniques in a multi-layered AI approach

Each type of machine learning technique has its own set of strengths and weaknesses, so a multi-layered, multi-method approach is ideal to enhance functionality while overcoming the shortcomings of any one method.

Darktrace’s multi-layered AI engine is powered by multiple machine learning approaches, which operate in combination for cyber defense. This allows Darktrace to protect the entire digital estates of the organizations it secures, including corporate networks, cloud computing services, SaaS applications, IoT, Industrial Control Systems (ICS), and email systems.

Plugged into the organization’s infrastructure and services, our AI engine ingests and analyzes the raw data and its interactions within the environment and forms an understanding of the normal behavior, right down to the granular details of specific users and devices. The system continually revises its understanding about what is normal based on evolving evidence, continuously learning as opposed to baselining techniques.

This dynamic understanding of normal partnered with dozens of anomaly detection models means that the AI engine can identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. Understanding anomalies through the lens of many models as well as autonomously fine-tuning the models’ performances gives us a higher understanding and confidence in anomaly detection.

The next layer provides event correlation and threat behavior context to understand the risk level of an anomalous event(s). Every anomalous event is investigated by Cyber AI Analyst that uses a combination of unsupervised machine learning models to analyze logs with supervised machine learning trained on how to investigate. This provides anomaly and risk context along with investigation outcomes with explainability.

The ability to identify activity that represents the first footprints of an attacker, without any prior knowledge or intelligence, lies at the heart of the AI system’s efficacy in keeping pace with threat actor innovations and changes in tactics and techniques. It helps the human team detect subtle indicators that can be hard to spot amid the immense noise of legitimate, day-to-day digital interactions. This enables advanced threat detection with full domain visibility.

Digging deeper into AI: Mapping specific machine learning techniques to cybersecurity functions

Visibility and control are vital for the practical adoption of AI solutions, as it builds trust between human security teams and their AI tools. That is why we want to share some specific applications of AI across our solutions, moving beyond hype and buzzwords to provide grounded, technical explanations.

Darktrace’s technology helps security teams cover every stage of the incident lifecycle with a range of comprehensive analysis and autonomous investigation and response capabilities.

  1. Behavioral prediction: Our AI understands your unique organization by learning normal patterns of life. It accomplishes this with multiple clustering algorithms, anomaly detection models, Bayesian meta-classifier for autonomous fine-tuning, graph theory, and more.
  2. Real-time threat detection: With a true understanding of normal, our AI engine connects anomalous events to risky behavior using probabilistic models. 
  3. Investigation: Darktrace performs in-depth analysis and investigation of anomalies, in particular automating Level 1 of a SOC team and augmenting the rest of the SOC team through prioritization for human-led investigations. Some of these methods include supervised and unsupervised machine learning models, semantic analysis models, and graph theory.
  4. Response: Darktrace calculates the proportional action to take in order to neutralize in-progress attacks at machine speed. As a result, organizations are protected 24/7, even when the human team is out of the office. Through understanding the normal pattern of life of an asset or peer group, the autonomous response engine can isolate the anomalous/risky behavior and surgically block. The autonomous response engine also has the capability to enforce the peer group’s pattern of life when rare and risky behavior continues.
  5. Customizable model editor: This layer of customizable logic models tailors our AI’s processing to give security teams more visibility as well as the opportunity to adapt outputs, therefore increasing explainability, interpretability, control, and the ability to modify the operationalization of the AI output with auditing.

See the complete AI architecture in the paper “The AI Arsenal: Understanding the Tools Shaping Cybersecurity.”

Figure 1. Alerts can be customized in the model editor in many ways like editing the thresholds for rarity and unusualness scores above.

Machine learning is the fundamental ally in cyber defense

Traditional security methods, even those that use a small subset of machine learning, are no longer sufficient, as these tools can neither keep up with all possible attack vectors nor respond fast enough to the variety of machine-speed attacks, given their complexity compared to known and expected patterns.

Security teams require advanced detection capabilities, using multiple machine learning techniques to understand the environment, filter the noise, and take action where threats are identified.

Darktrace’s multi-layered AI comes together to achieve behavioral prediction, real-time threat detection and response, and incident investigation, all while empowering your security team with visibility and control.

Download the full report

Discover specifically how Darktrace applies different types of AI to improve cybersecurity efficacy and operations in this technical paper.

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