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November 25, 2024

Why Artificial Intelligence is the Future of Cybersecurity

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Nov 2024
This blog explores the impact of AI on the threat landscape, the benefits of AI in cybersecurity, and the role it plays in enhancing security practices and tools.

Introduction: AI & Cybersecurity

In the wake of artificial intelligence (AI) becoming more commonplace, it’s no surprise to see that threat actors are also adopting the use of AI in their attacks at an accelerated pace. AI enables augmentation of complex tasks such as spear-phishing, deep fakes, polymorphic malware generation, and advanced persistent threat (APT) campaigns, which significantly enhances the sophistication and scale of their operations. This has put security professionals in a reactive state, struggling to keep pace with the proliferation of threats.

As AI reshapes the future of cyber threats, defenders are also looking to integrate AI technologies into their security stack. Adopting AI-powered solutions in cybersecurity enables security teams to detect and respond to these advanced threats more quickly and accurately as well as automate traditionally manual and routine tasks. According to research done by Darktrace in the 2024 State of AI Cybersecurity Report improving threat detection, identifying exploitable vulnerabilities, and automating low level security tasks were the top three ways practitioners saw AI enhancing their security team’s capabilities [1], underscoring the wide-ranging capabilities of AI in cyber.  

In this blog, we will discuss how AI has impacted the threat landscape, the rise of generative AI and AI adoption in security tools, and the importance of using multiple types of AI in cybersecurity solutions for a holistic and proactive approach to keeping your organization safe.  

The impact of AI on the threat landscape

The integration of AI and cybersecurity has brought about significant advancements across industries. However, it also introduces new security risks that challenge traditional defenses.  Three major concerns with the misuse of AI being leveraged by adversaries are: (1) the increase of novel social engineering attacks that are harder to detect and able to bypass traditional security tools,  (2) the ease of access for less experienced threat actors to now deliver advanced attacks at speed and scale and (3) the attacking of AI itself, to include machine learning models, data corpuses and APIs or interfaces.

In the context of social engineering, AI can be used to create more convincing phishing emails, conduct advanced reconnaissance, and simulate human-like interactions to deceive victims more effectively. Generative AI tools, such as ChatGPT, are already being used by adversaries to craft these sophisticated phishing emails, which can more aptly mimic human semantics without spelling or grammatical error and include personal information pulled from internet sources such as social media profiles. And this can all be done at machine speed and scale. In fact, Darktrace researchers observed a 135% rise in ‘novel social engineering attacks’ across Darktrace / EMAIL customers in 2023, corresponding to the widespread adoption and use of ChatGPT [2].  

Furthermore, these sophisticated social engineering attacks are now able to circumvent traditional security tools. In between December 21, 2023, and July 5, 2024, Darktrace / EMAIL detected 17.8 million phishing emails across the fleet, with 62% of these phishing emails successfully bypassing Domain-based Message Authentication, Reporting, and Conformance (DMARC) verification checks [2].  

And while the proliferation of novel attacks fueled by AI is persisting, AI also lowers the barrier to entry for threat actors. Publicly available AI tools make it easy for adversaries to automate complex tasks that previously required advanced technical skills. Additionally, AI-driven platforms and phishing kits available on the dark web provide ready-made solutions, enabling even novice attackers to execute effective cyber campaigns with minimal effort.

The impact of adversarial use of AI on the ever-evolving threat landscape is important for organizations to understand as it fundamentally changes the way we must approach cybersecurity. However, while the intersection of cybersecurity and AI can have potentially negative implications, it is important to recognize that AI can also be used to help protect us.

A generation of generative AI in cybersecurity

When the topic of AI in cybersecurity comes up, it’s typically in reference to generative AI, which became popularized in 2023. While it does not solely encapsulate what AI cybersecurity is or what AI can do in this space, it’s important to understand what generative AI is and how it can be implemented to help organizations get ahead of today’s threats.  

Generative AI (e.g., ChatGPT or Microsoft Copilot) is a type of AI that creates new or original content. It has the capability to generate images, videos, or text based on information it learns from large datasets. These systems use advanced algorithms and deep learning techniques to understand patterns and structures within the data they are trained on, enabling them to generate outputs that are coherent, contextually relevant, and often indistinguishable from human-created content.

For security professionals, generative AI offers some valuable applications. Primarily, it’s used to transform complex security data into clear and concise summaries. By analyzing vast amounts of security logs, alerts, and technical data, it can contextualize critical information quickly and present findings in natural, comprehensible language. This makes it easier for security teams to understand critical information quickly and improves communication with non-technical stakeholders. Generative AI can also automate the creation of realistic simulations for training purposes, helping security teams prepare for various cyberattack scenarios and improve their response strategies.  

Despite its advantages, generative AI also has limitations that organizations must consider. One challenge is the potential for generating false positives, where benign activities are mistakenly flagged as threats, which can overwhelm security teams with unnecessary alerts. Moreover, implementing generative AI requires significant computational resources and expertise, which may be a barrier for some organizations. It can also be susceptible to prompt injection attacks and there are risks with intellectual property or sensitive data being leaked when using publicly available generative AI tools.  In fact, according to the MIT AI Risk Registry, there are potentially over 700 risks that need to be mitigated with the use of generative AI.

Generative AI impact on cyber attacks screenshot data sheet

For more information on generative AI's impact on the cyber threat landscape download the Darktrace Data Sheet

Beyond the Generative AI Glass Ceiling

Generative AI has a place in cybersecurity, but security professionals are starting to recognize that it’s not the only AI organizations should be using in their security tool kit. In fact, according to Darktrace’s State of AI Cybersecurity Report, “86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats.” As we look toward the future of AI in cybersecurity, it’s critical to understand that different types of AI have different strengths and use cases and choosing the technologies based on your organization’s specific needs is paramount.

There are a few types of AI used in cybersecurity that serve different functions. These include:

Supervised Machine Learning: Widely used in cybersecurity due to its ability to learn from labeled datasets. These datasets include historical threat intelligence and known attack patterns, allowing the model to recognize and predict similar threats in the future. For example, supervised machine learning can be applied to email filtering systems to identify and block phishing attempts by learning from past phishing emails. This is human-led training facilitating automation based on known information.  

Large Language Models (LLMs): Deep learning models trained on extensive datasets to understand and generate human-like text. LLMs can analyze vast amounts of text data, such as security logs, incident reports, and threat intelligence feeds, to identify patterns and anomalies that may indicate a cyber threat. They can also generate detailed and coherent reports on security incidents, summarizing complex data into understandable formats.

Natural Language Processing (NLP): Involves the application of computational techniques to process and understand human language. In cybersecurity, NLP can be used to analyze and interpret text-based data, such as emails, chat logs, and social media posts, to identify potential threats. For instance, NLP can help detect phishing attempts by analyzing the language used in emails for signs of deception.

Unsupervised Machine Learning: Continuously learns from raw, unstructured data without predefined labels. It is particularly useful in identifying new and unknown threats by detecting anomalies that deviate from normal behavior. In cybersecurity, unsupervised learning can be applied to network traffic analysis to identify unusual patterns that may indicate a cyberattack. It can also be used in endpoint detection and response (EDR) systems to uncover previously unknown malware by recognizing deviations from typical system behavior.

Types of AI in cybersecurity
Figure 1: Types of AI in cybersecurity

Employing multiple types of AI in cybersecurity is essential for creating a layered and adaptive defense strategy. Each type of AI, from supervised and unsupervised machine learning to large language models (LLMs) and natural language processing (NLP), brings distinct capabilities that address different aspects of cyber threats. Supervised learning excels at recognizing known threats, while unsupervised learning uncovers new anomalies. LLMs and NLP enhance the analysis of textual data for threat detection and response and aid in understanding and mitigating social engineering attacks. By integrating these diverse AI technologies, organizations can achieve a more holistic and resilient cybersecurity framework, capable of adapting to the ever-evolving threat landscape.

A Multi-Layered AI Approach with Darktrace

AI-powered security solutions are emerging as a crucial line of defense against an AI-powered threat landscape. In fact, “Most security stakeholders (71%) are confident that AI-powered security solutions will be better able to block AI-powered threats than traditional tools.” And 96% agree that AI-powered solutions will level up their organization’s defenses.  As organizations look to adopt these tools for cybersecurity, it’s imperative to understand how to evaluate AI vendors to find the right products as well as build trust with these AI-powered solutions.  

Darktrace, a leader in AI cybersecurity since 2013, emphasizes interpretability, explainability, and user control, ensuring that our AI is understandable, customizable and transparent. Darktrace’s approach to cyber defense is rooted in the belief that the right type of AI must be applied to the right use cases. Central to this approach is Self-Learning AI, which is crucial for identifying novel cyber threats that most other tools miss. This is complemented by various AI methods, including LLMs, generative AI, and supervised machine learning, to support the Self-Learning AI.  

Darktrace focuses on where AI can best augment the people in a security team and where it can be used responsibly to have the most positive impact on their work. With a combination of these AI techniques, applied to the right use cases, Darktrace enables organizations to tailor their AI defenses to unique risks, providing extended visibility across their entire digital estates with the Darktrace ActiveAI Security Platform™.

Credit to: Ed Metcalf, Senior Director Product Marketing, AI & Innovations - Nicole Carignan VP of Strategic Cyber AI for their contribution to this blog.

CISOs guide to buying AI white paper cover

To learn more about Darktrace and AI in cybersecurity download the CISO’s Guide to Cyber AI here.

Download the white paper to learn how buyers should approach purchasing AI-based solutions. It includes:

  • Key steps for selecting AI cybersecurity tools
  • Questions to ask and responses to expect from vendors
  • Understand tools available and find the right fit
  • Ensure AI investments align with security goals and needs
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.
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Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
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February 19, 2025

Darktrace Releases Annual 2024 Threat Insights

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Introduction: Darktrace’s threat research

Defenders must understand the threat landscape in order to protect against it. They can do that with threat intelligence.

Darktrace approaches threat intelligence with a unique perspective. Unlike traditional security vendors that rely on established patterns from past incidents, it uses a strategy that is rooted in the belief that identifying behavioral anomalies is crucial for identifying both known and novel threats.

For Darktrace analysts and researchers, the incidents detected by the AI solution mark the beginning of a deeper investigation, aiming to connect mitigated threats to wider trends from across the threat landscape. Through hindsight analysis, the Darktrace Threat Research team has highlighted numerous threats, including zero-day, n-day, and other novel attacks, showcasing their evolving nature and Darktrace’s ability to identify them.

In 2024, the Threat Research team observed major trends around vulnerabilities in internet-facing systems, new and re-emerging ransomware strains, and sophisticated email attacks. Read on to discover some of our key insights into the current cybersecurity threat landscape.

Multiple campaigns target vulnerabilities in internet-facing systems

It is increasingly common for threat actors to identify and exploit newly discovered vulnerabilities in widely used services and applications, and in some cases, these vulnerability exploitations occur within hours of disclosure.

In 2024, the most significant campaigns observed involved the ongoing exploitation of zero-day and n-day vulnerabilities in edge and perimeter network technologies. In fact, in the first half of the year, 40% of all identified campaign activity came from the exploitation of internet-facing devices. Some of the most common exploitations involved Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS) appliances, Palo Alto Network (PAN-OS) firewall devices, and Fortinet appliances.

Darktrace helps security teams identify suspicious behavior quickly, as demonstrated with the critical vulnerability in PAN-OS firewall devices. The vulnerability was publicly disclosed on April 11, 2024, yet with anomaly-based detection, Darktrace’s Threat Research team was able to identify a range of suspicious behavior related to exploitation of this vulnerability, including command-and-control (C2) connectivity, data exfiltration, and brute-forcing activity, as early as March 26.

That means that Darktrace and our Threat Research team detected this Common Vulnerabilities and Exposure (CVE) exploitation 16 days before the vulnerability was disclosed. Addressing critical vulnerabilities quickly massively benefits security, as teams can reduce their effectiveness by slowing malicious operations and forcing attackers to pursue more costly and time-consuming methods.

Persistent ransomware threats continue to evolve

The continued adoption of the Ransomware-as-a-Service (RaaS) model provides even less experienced threat actors with the tools needed to carry out disruptive attacks, significantly lowering the barrier to entry.

The Threat Research team tracked both novel and re-emerging strains of ransomware across the customer fleet, including Akira, LockBit, and Lynx. Within these ransomware attempts and incidents, there were notable trends in attackers’ techniques: using phishing emails as an attack vector, exploiting legitimate tools to mask C2 communication, and exfiltrating data to cloud storage services.

Read the Annual 2024 Threat Report for the complete list of prominent ransomware actors and their commonly used techniques.

Onslaught of email threats continues

With a majority of attacks originating from email, it is crucial that organizations secure the inboxes and beyond.

Between December 21, 2023, and December 18, 2024, Darktrace / EMAIL detected over 30.4 million phishing emails across the fleet. Of these, 70% successfully bypassed Domain-based Message Authentication, Reporting, and Conformance (DMARC) verification checks and 55% passed through all other existing layers of customer email security.

The abuse of legitimate services and senders continued to be a significant method for threat actors throughout 2024. By leveraging trusted platforms and domains, malicious actors can bypass traditional security measures and increase the likelihood of their phishing attempts being successful.

This past year, there was a substantial use of legitimately authenticated senders and previously established domains, with 96% of phishing emails detected by Darktrace / EMAIL utilizing existing domains rather than registering new ones.

These are not the only types of email attacks we observed. Darktrace detected over 2.7 million emails with multistage payloads.

While most traditional cybersecurity solutions struggle to cover multiple vectors and recognize each stage of complex attacks as part of wider malicious activity, Darktrace can detect and respond across email, identities, network, and cloud.

Conclusion

The Darktrace Threat Research team continues to monitor the ever-evolving threat landscape. Major patterns over the last year have revealed the importance of fast-acting, anomaly-based detection like Darktrace provides.

For example, response speed is essential when campaigns target vulnerabilities in internet-facing systems, and these vulnerabilities can be exploited by attackers within hours of their disclosure if not even before that.

Similarly, anomaly-based detection can identify hard to find threats like ransomware attacks that increasingly use living-off-the-land techniques and legitimate tools to hide malicious activity. A similar pattern can be found in the realm of email security, where attacks are also getting harder to spot, especially as they frequently exploit trusted senders, use redirects via legitimate services, and craft attacks that bypass DMARC and other layers of email security.

As attacks appear with greater complexity, speed, and camouflage, defenders must have timely detection and containment capabilities to handle all emerging threats. These hard-to-spot attacks can be identified and stopped by Darktrace.

Download the full report

Discover the latest threat landscape trends and recommendations from the Darktrace Threat Research team.

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February 18, 2025

Unifying IT & OT With AI-Led Investigations for Industrial Security

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As industrial environments modernize, IT and OT networks are converging to improve efficiency, but this connectivity also creates new attack paths. Previously isolated OT systems are now linked to IT and cloud assets, making them more accessible to attackers.

While organizations have traditionally relied on air gaps, firewalls, data diodes, and access controls to separate IT and OT, these measures alone aren’t enough. Threat actors often infiltrate IT/Enterprise networks first then exploit segmentation, compromising credentials, or shared IT/OT systems to move laterally, escalate privileges, and ultimately enter the OT network.

To defend against these threats, organizations must first ensure they have complete visibility across IT and OT environments.

Visibility: The first piece of the puzzle

Visibility is the foundation of effective industrial cybersecurity, but it’s only the first step. Without visibility across both IT and OT, security teams risk missing key alerts that indicate a threat targeting OT at their earliest stages.

For Attacks targeting OT, early stage exploits often originate in IT environments, adversaries perform internal reconnaissance among other tactics and procedures but then laterally move into OT first affecting IT devices, servers and workstations within the OT network. If visibility is limited, these threats go undetected. To stay ahead of attackers, organizations need full-spectrum visibility that connects IT and OT security, ensuring no early warning signs are missed.

However, visibility alone isn’t enough. More visibility also means more alerts, this doesn’t just make it harder to separate real threats from routine activity, but bogs down analysts who have to investigate all these alerts to determine their criticality.

Investigations: The real bottleneck

While visibility is essential, it also introduces a new challenge: Alert fatigue. Without the right tools, analysts are often occupied investigating alerts with little to no context, forcing them to manually piece together information and determine if an attack is unfolding. This slows response times and increases the risk of missing critical threats.

Figure 1: Example ICS attack scenario

With siloed visibility across IT and OT each of these events shown above would be individually alerted by a detection engine with little to no context nor correlation. Thus, an analyst would have to try to piece together these events manually. Traditional security tools struggle to keep pace with the sophistication of these threats, resulting in an alarming statistic: less than 10% of alerts are thoroughly vetted, leaving organizations vulnerable to undetected breaches. As a result, incidents inevitably follow.

Darktrace’s Cyber AI Analyst uses AI-led investigations to improve workflows for analysts by automatically correlating alerts wherever they occur across both IT and OT. The multi-layered AI engine identifies high-priority incidents, and provides analysts with clear, actionable insights, reducing noise and highlighting meaningful threats. The AI significantly alleviates workloads, enabling teams to respond faster and more effectively before an attack escalates.

Overcoming organizational challenges across IT and OT

Beyond technical challenges like visibility and alert management, organizational dynamics further complicate IT-OT security efforts. Fundamental differences in priorities, workflows, and risk perspectives create challenges that can lead to misalignment between teams:

Non-transferable practices: IT professionals might assume that cybersecurity practices from IT environments can be directly applied to OT environments. This can lead to issues, as OT systems and workflows may not handle IT security processes as expected. It's crucial to recognize and respect the unique requirements and constraints of OT environments.

Segmented responsibilities: IT and OT teams often operate under separate organizational structures, each with distinct priorities, goals, and workflows. While IT focuses on data security, network integrity, and enterprise applications, OT prioritizes uptime, reliability, and physical processes.

Different risk perspectives: While IT teams focus on preventing cyber threats and regulatory violations, OT teams prioritize uptime and operational reliability making them drawn towards asset inventory tools that provide no threat detection capability.

Result: A combination of disparate and ineffective tools and misaligned teams can make any progress toward risk reduction at an organization seem impossible. The right tools should be able to both free up time for collaboration and prompt better communication between IT and OT teams where it is needed. However, different size operations structure their IT and OT teams differently which impacts the priorities for each team.

In real-world scenarios, small IT teams struggle to manage security across both IT and OT, while larger organizations with OT security teams face alert fatigue and numerous false positives slowing down investigations and hindering effective communication with the IT security teams.

By unifying visibility and investigations, Darktrace / OT helps organizations of all sizes detect threats earlier, streamline workflows, and enhance security across both IT and OT environments. The following examples illustrate how AI-driven investigations can transform security operations, improving detection, investigation, and response.

Before and after AI-led investigation

Before: Small manufacturing company

At a small manufacturing company, a 1-3 person IT team juggles everything from email security to network troubleshooting. An analyst might see unusual traffic through the firewall:

  • Unusual repeated outbound traffic from an IP within their OT network destined to an unidentifiable external IP.

With no dedicated OT security tools and limited visibility into the industrial network, they don’t know what the internal device in question is, if it is beaconing to a malicious external IP, and what it may be doing to other devices within the OT network. Without a centralized dashboard, they must manually check logs, ask operators about changes, and hunt for anomalies across different systems.

After a day of investigation, they concluded the traffic was not to be expected activity. They stop production within their smaller OT network, update their firewall rules and factory reset all OT devices and systems within the blast radius of the IP device in question.

After: Faster, automated response with Cyber AI Analyst

With Darktrace / OT and Cyber AI Analyst, the IT team moves from reactive, manual investigations to proactive, automated threat detection:

  • Cyber AI Analyst connects alerts across their IT and OT infrastructure temporally mapping them to attack frameworks and provides contextual analysis of how alerts are linked, revealing in real time attackers attempting lateral movement from IT to OT.
  • A human-readable incident report explains the full scope of the incident, eliminating hours of manual investigation.
  • The team is faster to triage as they are led directly to prioritized high criticality alerts, now capable of responding immediately instead of wasting valuable time hunting for answers.

By reducing noise, providing context, and automating investigations, Cyber AI Analyst transforms OT security, enabling small IT teams to detect, understand, and respond to threats—without deep OT cybersecurity expertise.

Before: Large critical infrastructure organization

In large critical infrastructure operations, OT and IT teams work in separate silos. The OT security team needs to quickly assess and prioritize alerts, but their system floods them with notifications:

  • Multiple new device connected to the ICS network alerts
  • Multiple failed logins to HMI detected
  • Multiple Unusual Modbus/TCP commands detected
  • Repeated outbound OT traffic to IT destinations

At first glance, these alerts seem important, but without context, it’s unclear whether they indicate a routine error, a misconfiguration, or an active cyber-attack. They might ask:

  • Are the failed logins just a mistake, or a brute-force attempt?
  • Is the outbound traffic part of a scheduled update, or data exfiltration?

Without correlation across events, the engineer must manually investigate each one—checking logs, cross-referencing network activity, and contacting operators—wasting valuable time. Meanwhile, if it’s a coordinated attack, the adversary may already be disrupting operations.

After: A new workflow with Cyber AI Analyst

With Cyber AI Analyst, the OT security team gets clear, automated correlation of security events, making investigations faster and more efficient:

  • Automated correlation of OT threats: Instead of isolated alerts, Cyber AI Analyst stitches together related events, providing a single, high-confidence incident report that highlights key details.
  • Faster time to meaning: The system connects anomalous behaviors (e.g., failed logins, unusual traffic from an HMI, and unauthorized PLC modifications) into a cohesive narrative, eliminating hours of manual log analysis.
  • Prioritized and actionable alerts: OT security receives clear, ranked incidents, immediately highlighting what matters most.
  • Rapid threat understanding: Security teams know within minutes whether an event is a misconfiguration or a cyber-attack, allowing for faster containment.

With Cyber AI Analyst, large organizations cut through alert noise, accelerate investigations, and detect threats faster—without disrupting OT operations.

An AI-led approach to industrial cybersecurity

Security vendors with a primary focus on IT may lack insight into OT threats. Even OT-focused vendors have limited visibility into IT device exploitation within OT networks, leading to failed ability to detect early indicators of compromise. A comprehensive solution must account for the unique characteristics of various OT environments.

In a world where industrial security is no longer just about protecting OT but securing the entire digital-physical ecosystem as it interacts with the OT network, Darktrace / OT is an AI-driven solution that unifies visibility across IT, IoT and OT, Cloud into one cohesive defense strategy.

Whether an attack originates from an external breach, an insider threat, a supply chain compromise, in the Cloud, OT, or IT domains Cyber AI Analyst ensures that security teams see the full picture - before disruption occurs.

Learn more about Darktrace / OT 

  • Unify IT and OT security under a single platform, ensuring seamless communication and protection for all interconnected devices.
  • Maintain uptime with AI-driven threat containment, stopping attacks without disrupting production.
  • Mitigate risks with or without patches, leveraging MITRE mitigations to reduce attack opportunities.

Download the solution brief to see how Darktrace secures critical infrastructure.

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About the author
Daniel Simonds
Director of Operational Technology
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