Learn how Darktrace’s AI identified the Mirai malware in an Internet-connected CTV camera, breaking down each stage of the attack life cycle.
Introduction: Mirai Malware attack on IoT devices
The rise of Internet of Things (IoT) devices, like Internet-connected cameras used in CCTV surveillance, has introduced new vulnerabilities to both personal and business environments.
With attackers exploiting the inadequate security measures typically associated with IoT devices, traditional antivirus and legacy security solutions fall short. To address this critical gap, Darktrace’s Cyber AI Platform provides advanced protection for these increasingly essential yet vulnerable technologies.
Attack Overview: Mirai Malware
In late May, Darktrace detected the Mirai malware infecting an internet-facing DVR camera owned by a logistics company in Canada.
Mirai, first discovered in 2016, continuously scans the Internet for the IP addresses of vulnerable devices in the Internet of Things (IoT), and then turns these devices into bots that can be used as part of botnets for large-scale network attacks. These attacks are often difficult to detect, as IoT devices seamlessly integrate into digital infrastructure, creating a vastly expanded attack surface for organizations.
By drawing on a bespoke, evolving understanding of what is normal for the network, Darktrace caught each stage in this attack’s lifecycle. However, because this company was still conducting their 30-day Proof of Value, Darktrace's Autonomous Response was not in active and the attack continued past the point of initial compromise. Had Darktrace's Autonomous Response been in active mode, the attack would not have advanced past initial compromise.
Attack Timeline
Figure 1: This timeline roughly outlines the major attack phases over three days of activity
Technical analysis
At the time of the initial breach, this specific botnet’s infrastructure was not yet known to open source intelligence (OSINT). Darktrace, however, detected an EXE download from a location not previously visited by the network.
After the first anomalous EXE download, another was downloaded approximately twenty minutes later. The malware then reached out to multiple IP addresses that were statistically rare for the network. Specifically, the compromised device began transferring large amounts of data to an IP address in China.
Figure 2: An overview of Darktrace detections
Darktrace, by leveraging machine learning algorithms in a protocol agnostic capacity, analyzed this individual device’s transfers within the context of a continuously evolving understanding of what is normal both for this device and for the wider organization. It was therefore able to immediately flag all of these transfers as unusual.
This activity was fully investigated and reported on by Darktrace’s Cyber AI Analyst. A sample of the AI Analyst’s report is shown below. The Suspicious File Download, the Unusual Repeated Connections, and the Unusual External Data Transfer are all presented as unexpected events that call for further investigation. The destination IP of the suspicious download was determined to have 100% hostname rarity relative to what is normal for the organization.
Figure 3: Darktrace’s Cyber AI Analyst autonomously triages the attack
Moreover, the hash of the file, highlighted in a red box in the figure above, revealed that it was a well-known file related to the Mirai Botnet. However, with no antivirus or other security defending the IoT camera, this had gone undetected.
A one-click analysis of the infected device shows a timeline of the model breaches that occurred and graphs the activity to give the report’s readers a quick understanding of the successive stages of the attack. Here, we see the second and third stages of the attack’s lifecycle, in which it starts DDoS against other devices in order to complete its mission while simultaneously continuing outgoing connections to rare destinations in order to sustain its presence.
Figure 4: The device event log showing the list of model breaches on May 23
Conclusion
Interestingly, the client saw no indicators of this activity beyond a sluggish network. This change in network activity was only explained after being identified by Darktrace. Once the client was promptly notified, the compromise was deescalated, and discovering it was a DVR security camera, the client took the device offline.
As this customer was still concluding their trial deployment, Darktrace was not in full autonomous mode. However, if it had been, Darktrace would have responded with a two-tiered action to prevent the device from communicating with the malicious endpoint, cutting the compromised connection before the attack had gained its foothold.
Darktrace model breaches:
Anomalous Connection / Uncommon 1GiB Outbound
Unusual Activity / Unusual External Activity
Unusual Activity / Enhanced Unusual External Data Transfer
Unusual Activity / Unusual External Data to New IPs
Device / Initial Breach Chain Compromise
Anomalous Server Activity / Outgoing from Server
Anomalous Connection / Data Sent to New External Device
Anomalous Connection / Multiple Connections to New External UDP Port
Anomalous Connection / Data Sent to Rare Domain
Anomalous File / EXE from Rare External Location
Anomalous File / Internet Facing System File Download
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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
Max Heinemeyer
Global Field CISO
Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.
An Advanced Persistent Threat (APT) describes an adversary with sophisticated levels of expertise and significant resources, with the ability to carry out targeted cyber campaigns. These campaigns may penetrate an organization and remain undetected for long periods, allowing attackers to gather intelligence or cause damage over time.
Over the last few decades, the term APT has evolved from being almost exclusively associated with nation-state actors to a broader definition that includes highly skilled, well-resourced threat groups. While still distinct from mass, opportunistic cybercrime or "spray and pray" attacks, APT now refers to the elite tier of adversaries, whether state-sponsored or not, who demonstrate advanced capabilities, persistence, and a clear strategic focus. This shift reflects the growing sophistication of cyber threats, where non-state actors can now rival nation-states in executing covert, methodical intrusions to achieve long-term objectives.
These attacks are resource-intensive for threat actors to execute, but the potential rewards—ranging from financial gain to sensitive data theft—can be significant. In 2020, Business Email Compromise (BEC) attacks netted cybercriminals over $1.8 billion.1
And recently, the advent of AI has helped to automate launching these attacks, lowering the barriers to entry and making it more efficient to orchestrate the kind of attack that might previously have taken weeks to create. Research shows that AI can do 90% of a threat actor’s work2 – reducing time-to-target by automating tasks rapidly and avoiding errors in phishing communications. Email remains the most popular vector for initiating these sophisticated attacks, making it a critical battleground for cyber defense.
What makes APTs so successful?
The success of Advanced Persistent Threats (APTs) lies in their precision, persistence, and ability to exploit human and technical vulnerabilities. These attacks are carefully tailored to specific targets, using techniques like social engineering and spear phishing to gain initial access.
Once inside, attackers move laterally through networks, often remaining undetected for months or even years, silently gathering intelligence or preparing for a decisive strike. Alternatively, they might linger inside an account within the M365 environment, which could be even more valuable in terms of gathering information – in 2023 the average time to identify a breach in 2023 was 204 days.3
The subtle and long-term outlook nature of APTs makes them highly effective, as traditional security measures often fail to identify the subtle signs of compromise.
How Darktrace’s approach is designed to catch the most advanced threats
Luckily for our customers, Darktrace’s AI approach is uniquely equipped to detect and neutralize APTs. Unlike the majority of email security solutions that rely on static rules and signatures, or that train their AI on previous known-bad attack patterns, Darktrace leverages Self-Learning AI that baselines normal patterns of behavior within an organization, to immediately detect unusual activity that may signal an APT in progress.
But in the modern era of email threats, no email security solution can guarantee 100% effectiveness. Because attackers operate with great sophistication, carefully adapting their tactics to evade detection – whether by altering attachments, leveraging compromised accounts, or moving laterally across an organization – a siloed security approach risks missing these subtle, multi-domain threats. That’s why a robust defense-in-depth strategy is essential to mitigate APTs.
Real-world threat finds: Darktrace / EMAIL in action
Let’s take a look at some real-world scenarios where Darktrace / EMAIL stopped tactics associated with APT campaigns in their tracks – from adversary-in-the-middle attacks to suspicious lateral movement.
1: How Darktrace disrupted an adversary-in-the-middle attack by identifying abnormal login redirects and blocking credential exfiltration
In October 2024, Darktrace detected an adversary-in-the-middle (AiTM) attack targeting a Darktrace customer. The attack began with a phishing email from a seemingly legitimate Dropbox address, which contained multiple link payloads inviting the recipient to access a file. Other solutions would have struggled to catch this attack, as the initial AitM attack was launched through delivering a malicious URL through a trusted vendor or service. Once compromised, the threat actor could have laid low on the target account, gathering reconnaissance, without detection from the email security solution.
Darktrace / EMAIL identified the abnormal login redirects and flagged the suspicious activity. Darktrace / IDENTITY then detected unusual login patterns and blocked credential exfiltration attempts, effectively disrupting the attack and preventing the adversary from gaining unauthorized access. Read more.
Figure 1: Overview of the malicious email in the Darktrace / EMAIL console, highlighting Dropbox associated content/link payloads
2: How Darktrace stopped lateral movement to block NTLM hash theft
In early 2024, Darktrace detected an attack by the TA577 threat group, which aimed to steal NTLM hashes to gain unauthorized access to systems. The attack began with phishing emails containing ZIP files that connected to malicious infrastructure.
A traditional email security solution would have likely missed this attack by focusing too heavily on analyzing the zip file payloads or relying on reputation analysis to understand whether the infrastructure was registered as bad before this activity was a recognized IoC.
Because it correlates activity across domains, Darktrace identified unusual lateral movement within the network and promptly blocked the attempts to steal NTLM hashes, effectively preventing the attackers from accessing sensitive credentials and securing the network. Read more.
Figure 2: A summary of anomaly indicators seen for a campaign email sent by TA577, as detected by Darktrace / EMAIL
3: How Darktrace prevented the WarmCookie backdoor deployment embedded in phishing emails
In mid-2024, Darktrace identified a phishing campaign targeting organizations with emails impersonating recruitment firms. These emails contained malicious links that, when clicked, deployed the WarmCookie backdoor.
These emails are difficult to detect, as they use social engineering tactics to manipulate users into engaging with emails and following the embedded malicious links – but if a security solution is not analysing content and context, these could be allowed through.
In several observed cases across customer environments, Darktrace detected and blocked the suspicious behavior associated with WarmCookie that had already managed to evade customers’ native email security. By using behavioral analysis to correlate anomalous activity across the digital estate, Darktrace was able to identify the backdoor malware strain and notify customers. Read more.
Conclusion
These threat examples highlight a key principle of the Darktrace approach – that a backwards-facing approach grounded in threat intelligence will always be one step behind.
Most threat actors operate in campaigns, carefully crafting attacks and testing them across multiple targets. Once a campaign is identified, good defenders and traditional security solutions quickly update their defenses with new threat intelligence, rules, and signatures. However, APTs have the resources to rapidly adapt – spinning up new infrastructure, modifying payloads and altering their attack footprint to evade detection.
This is where Darktrace / EMAIL excels. Only by analyzing each user, message and interaction can an email security solution hope to catch the types of highly-sophisticated attacks that have the potential to cause major reputational and financial damage. Darktrace / EMAIL ensures that even the most subtle threats are detected and blocked with autonomous response, before causing impact – helping organizations remain one step ahead of increasingly adaptive threat actors.
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Stop the 58% of threats bypassing traditional email security
NIS2 Compliance: Interpreting 'State-of-the-Art' for Organisations
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.
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.
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