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July 24, 2024

The State of AI in Cybersecurity: Understanding AI Technologies

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Jul 2024
Part 4: This blog explores the findings from Darktrace’s State of AI Cybersecurity Report on security professionals' understanding of the different types of AI used in security programs. Get the latest insights into the evolving challenges, growing demand for skilled professionals, and the need for integrated security solutions by downloading the full report.

About the State of AI Cybersecurity Report

Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners”. This blog will focus on security professionals’ understanding of AI technologies in cybersecurity tools.

To access download the full report, click here.

How familiar are security professionals with supervised machine learning

Just 31% of security professionals report that they are “very familiar” with supervised machine learning.

Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).

Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.

Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.  

If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.  

Different types of AI

Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.  

Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.  

Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.  

Natural language processing (NLP): Applies computational techniques to process and understand human language.  

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.  

What impact will generative AI have on the cybersecurity field?

More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.

Chart showing the types of AI expected to impact security the most
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report

Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.

AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.

Do security professionals fully understand the different types of AI in security products?

Only 26% of security professionals report a full understanding of the different types of AI in use within security products.

Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.

Key findings include:

  • Executives and managers report higher understanding than practitioners.
  • Larger organizations have better understanding due to greater specialization.

As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.

To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.  

Do security professionals believe generative AI alone is enough to stop zero-day threats?

No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats

This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.

Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.

Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.

To access the full report, click here.

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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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November 7, 2024

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Inside the SOC

Onomastics Gymnastics: How Darktrace Detects Spoofing and Business Email Compromise in Multi-Name Users

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Note: For privacy reasons, actual surnames and email addresses observed in these incidents below have been replaced with fictitious placeholder names, using the common Spanish names “Fulano” and “Mengano”.

Naming conventions

Modeling names and their variants of members of an organization is a critical component to properly detect if those same names and variants are being spoofed by malicious actors. For many predominantly English-speaking organizations, these variants can largely be captured by variants of a person’s given name (e.g. James-Jimmy-Jim) and a consistent, singular surname or family name (e.g. Smith). Naming conventions, however, are far from universal. This piece will review how Darktrace / EMAIL manages the common naming conventions of much of the Spanish-speaking world, and can use its modeling to create high-fidelity detections of multiple types of spoofing attempts.

A brief summary of the common convention across Spain and much of Spanish-speaking America: most people are given one or two given names (e.g. Roberto, Juan, María, Natalia), and their surnames are the first surname of their father, followed by the first surname of their mother. While there are various exceptions to this norm, the below graphic Wikipedia [1][2] highlights the general rule.

Example Spanish naming convention for father “José García Torres” and mother “María Acosta Gómez” for child “Pablo García Acosta”. If shortened to one surname, the convention holds the child would be referred to as “Pablo García”
Figure 1: Example Spanish naming convention for father “José García Torres” and mother “María Acosta Gómez” for child “Pablo García Acosta”. If shortened to one surname, the convention holds the child would be referred to as “Pablo García” [1].

Detection of improper name usage

Implicit in the above comment that shortening to one surname follows the convention of using the first surname, shortening to the second surname is often a tell-tale sign of someone unfamiliar with the person or their broader culture. This can be a useful corroborating feature in detecting a spoof attempt – analogous to a spelling error.

In the case of a Spanish customer, this misuse of name shortening contributed to the detection of a spoof attempt trying to solicit a response by impersonating an internal user forwarding information about ‘Data Protection’.

Figure 2: The Cyber AI Analyst summary of the Darktrace / EMAIL detections shows the use of the Gmail sender impersonating Isabel Maria Fulano Mengano, but incorrectly uses the second surname Mengano.

While the limited communication history from the sender and the nature of the text content already marks the mail as suspicious, Darktrace / EMAIL notes the personal name used in the email is similar to a high-value user (‘whale’ to use the terminology of spearphishing). The additional context provided by the detection of the attempted spoof prompted more severe actioning of this email, leading to a ‘Hold’ action instead of a less-severe ‘Unspoof’ action via a banner on the email.

The content summary of the sender showing the ‘Personal’ field of the email being ‘Isabel Mengano’, breaking from the standard name-shortening convention. The additional metrics identify features that might be anomalous about the sender.
Figure 3: The content summary of the sender showing the ‘Personal’ field of the email being ‘Isabel Mengano’, breaking from the standard name-shortening convention. The additional metrics identify features that might be anomalous about the sender.

Malicious email properly using both surnames

Misusing the name-shortening convention is not the only way that Darktrace / EMAIL can detect spoofing attempts. In the case of another Spanish customer,  Darktrace observed a whale impersonation being sent to 230 users with solicitation content, but no links or attachments. Although the name was modeled internally in the “Surname, Given-name” format, Darktrace identified the spoofing attempt targeting a high-value user and took action, blocking the series of emails from reaching end-user inboxes to prevent unsuspecting users from responding.

Cyber AI Analyst summary of a suspicious email
Figure 4: Cyber AI Analyst summary of a suspicious email. The personal field is visible as ‘juan fulano mengano’, which is consistent with the reverse-order modelled user ‘fulano mengano, juan’. The subject line ‘Urgent Request’ sent to 230 users gives an intuitive indicator of the emails potentially being part of a malicious solicitation campaign.

In Summary: A case of onomastics gymnastics

The variety in valid usage of human language can be a barrier to evaluating when a given text is benign or malicious. Despite this, Darktrace / EMAIL is designed to manage this variety, as exemplified by the detections of two spoofing attempts seen against organizations using the distinct Spanish-speaking world’s common naming convention. The scope of this design as seen in this onomastic context, extends to a wide range of detections surrounding emails and their behavioral anomalies.

Credit to Roberto Romeu (Principal Cyber Analyst), Justin Torres (Senior Cyber Analyst) and Natalia Sánchez Rocafort (Senior Analyst Consultant).

Darktrace / Email solution brief screenshot

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  • Gain up to 13 days of earlier threat detection and maximize ROI on your current email security
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  • Stop the 58% of threats bypassing traditional email security

References

[1] https://en.wikipedia.org/wiki/Naming_customs_of_Hispanic_America

[2] https://en.wikipedia.org/wiki/Spanish_naming_customs

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About the author
Roberto Romeu
Senior SOC Analyst

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October 31, 2024

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OT

Understanding the NERC-CIP015 Internal Network Security Monitoring (INSM) Requirements

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Background: NERC CIP-015

In January of 2023 the Federal Energy Regulatory Commission (FERC) released FERC Order 887 which addresses a critical security gap in Critical Infrastructure Protection (CIP) standards, the lack of internal network security monitoring (INSM).

The current NERC CIP standards only require solutions that use traditional detection systems that identify malicious code based on known rules and signatures. The new legislation will now require electric cooperatives to implement INSMs to detect malicious activity in east-west network traffic. INSMs establish a baseline of network activity and detect anomalies that would bypass traditional detection systems, improving an organization’s ability to detect novel threats. Without INSM, organizations have limited visibility into malicious activities inside their networks, leaving them vulnerable if attackers breach initial defenses like firewalls and anti-virus software.

Implementation of NERC CIP-015

Once approved, Bulk Electronic Systems (BESs) will have 36 months to implement INSM, and medium-impact BESs with external routable connectivity (ERC) will have 60 months to do so.

While the approval of the NERC CIP-015 requirements have not been finalized, preparation on the part of electric cooperatives should start as soon as possible. Darktrace is committed to helping electric cooperatives meet the requirements for INSM and help reach compliance standards.

Why is internal network security monitoring important?

NERC CIP-015 aims to enhance the detection of anomalies or unauthorized network activity within CIP environments, underscoring the importance of monitoring East-West traffic within trust zones. This approach enables faster response and recovery times.

INSMs are essential to detecting threats that bypass traditional defenses. For example, insider threats, sophisticated new attack techniques, and threats that exploit compromised credentials—such as those obtained through phishing or other malicious activities—can easily bypass traditional firewalls and antivirus software. These threats either introduce novel methods or leverage legitimate access, making them difficult to detect.

INSMs don’t rely on rules and signatures to detect anomalous activity, they spot abnormalities in network traffic and create alerts based on this activity making them vital to detecting sophisticated threats. Additionally, INSM sits behind the firewall and provides detections utilizing the passive monitoring of east west and north south traffic within the enforcement boundary.

Buyers should be aware of the discrepancies between different INSMs. Some systems require constant tuning and updating, external connectivity forcing holes in segmentation or have intrusive deployments that put sensitive OT assets at risk.

What are the NERC CIP-015 requirements?

The goal of this directive is to ensure that cyber threats are identified early in the attack lifecycle by mandating implementation of security systems that detect and speed up mitigation of malicious activity.

The requirements are divided into three sections:

  • Network security monitoring
  • Data retention for anomalous activity
  • Data protection

NERC CIP-015 emphasizes the importance of having documented processes and evidence of implementation, with a focus on risk-based monitoring, anomaly detection, evaluation, retention of data, and protection against unauthorized access. Below is a breakdown of each requirement.

R1: Network Security Monitoring

The NERC CIP-015 requires the implementation of and a documented process for monitoring networks within Electronic Security Perimeters (ESPs) that contain high and medium impact BES Cyber Systems.

Key parts:

Part 1.1: Use a risk-based rationale to implement network data feeds that monitor connections, devices, and communications.

Part 1.2: Detect anomalous network activity using the data feeds.

Part 1.3: Evaluate the anomalous activity to determine necessary actions.

M1: Evidence for R1 Implementation: Documentation of processes, including risk-based rationale for data collection, detection events, configuration settings, and network baselines.

Incorporating automated solutions for network baselining is essential for effective internal monitoring, especially in diverse environments like substations and control centers. Each environment requires unique baselines—what’s typical for a substation may differ significantly from a control center, making manual monitoring impractical.

A continuous internal monitoring solution powered by artificial intelligence (AI) simplifies this challenge by instantly detecting all connected assets, dynamically learning the environment’s baseline behavior, and identifying anomalies in real-time. Unlike traditional methods, Darktrace’s AI-driven approach requires no external connectivity or repeated tuning, offering a seamless, adaptive solution for maintaining secure operations across all environments.

R2: Data Retention for Anomalous Activity

Documented processes must be in place to retain network security data related to detected anomalies until the required actions are completed.

Note: Data that does not relate to detected anomalies (Part 1.2) is not required to be retained.

M2: Evidence for Data Retention (R2): Documentation of data retention processes, system configurations, or reports showing compliance with R2.

R3: Data Protection: Implement documented processes to protect the collected security monitoring data from unauthorized deletion or modification.

M3: Evidence for Data Protection (R3): Documentation demonstrating how network security monitoring data is protected from unauthorized access or changes.

How to choose the right INSM for your organization?

Several vendors will offer INSM, but how do you choose the right solution for your organization?

Here are seven questions to help you get started evaluating potential INSM vendors:

  1. How does the solution help with ongoing compliance and reporting including CIP-015? Or any other regulations we comply with?
  2. Does the solution provide real-time monitoring of east-west traffic across critical systems? And what kind of threats has it proven capable of finding?
  3. How deep is the traffic visibility—does it offer Layer 7 (application) insights, or is it limited to Layers 3-4?
  4. Is the solution compatible with our existing infrastructure (firewalls, IDS/IPS, SIEM, OT networks)?
  5. Is this solution inline, passive, or hybrid? What impact will it have on network latency?
  6. Does the vendor have experience with electric utilities or critical infrastructure environments?
  7. Where and how are logs and monitoring data stored?

How Darktrace helps electric utilities with INSM requirements

Darktrace's ActiveAI Security Platform is uniquely designed to continuously monitor network activity and detect anomalous activity across both IT and OT environments successfully detecting insider threats and novel ransomware, while accelerating time to detection and incident reporting.

Most INSM solutions require repeated baselining, which creates more work and increases the likelihood of false positives, as even minor deviations trigger alerts. Since networks are constantly changing, baselines need to adjust in real time. Unlike these solutions, Darktrace does not depend on external connectivity or cloud access over the public internet. Our passive network analysis requires no agents or intrusive scanning, minimizing disruptions and reducing risks to OT systems.

Darktrace's AI-driven threat detection, asset management, and incident response capabilities can help organizations comply with the requirements of NERC CIP-015 for internal network security monitoring and data protection. Built specifically to deploy in OT environments, Darktrace / OT comprehensively manages, detects, evaluates, and protects network activity and anomalous events across IT and OT environments, facilitating adherence to regulatory requirements like data retention and anomaly management.

See how INSM with Darktrace can enhance your security operations, schedule a personalized demo today.

Disclaimer

The information provided in this blog is intended for informational purposes only and reflects Darktrace’s understanding of the NERC CIP-015 INSM requirements as of the publication date. While every effort has been made to ensure the accuracy and reliability of the content, Darktrace makes no warranties or representations regarding its accuracy, completeness, or applicability to specific situations. This blog does not constitute legal or compliance advice and readers are encouraged to consult with qualified professionals for guidance specific to their circumstances. Darktrace disclaims any liability for actions taken or not taken based on the information contained herein.

References

1.     https://www.nerc.com/pa/Stand/Reliability%20Standards/CIP-015-1.pdf

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About the author
Daniel Simonds
Director of Operational Technology
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