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March 12, 2023

How Compliance Breach Mitigation Can Prevent Compromise

Compliance breaches can significantly damage a company’s finances and reputation if not properly addressed by security teams. Learn how Darktrace can help!
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Rachel Resnekov
Cyber Analyst
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12
Mar 2023

Compliance is often an afterthought for security teams responding to cyber security incidents, with many organizations seeing compliance issues as “rule breaking employees” rather than legitimate threats to their network. However, even seemingly innocuous compliance breaches can significantly damage a company’s finances and reputation if not properly addressed.

Adhering to cyber security standards and regulatory requirements is essential, but can often result in “tick box compliance” wherein meeting standards does not result in a reduction of non-compliant activity, lacking tangible impact for many organizations. Protecting data is of paramount importance, especially given the implementation of numerous data protection laws concerned with protecting sensitive data, such as Personally Identifiable Information (PII), financial information, and Protected Health Information (PHI). However, many compliance breaches which do not result in data loss go unadressed, inevitably leading to vulnerabilities within the network that are advantageous to threat actors. Darktrace detects compliance issues in real time and escalates them accordingly, using a dedicated compliance model stack. It highlights incidents of concern, from insecure password storage to device updates, ensuring that users adhere to company standards.

Finding ways to prioritize and quickly triage through these compliance issues, rather than focusing on log auditing or more manually intensive processes, can result in immense gains for security teams.  

Darktrace Coverage of Compliance Breaches   

Incident: Outgoing Operational Technology Connection 

Compliance issues in Operational Technology (OT) are difficult to detect using traditional security measures. The OT space faces unique challenges, such as legacy systems, limited visibility, and convergence between OT and Information Technology (IT). Darktrace’s compliance stack includes an OT-specific subset, allowing users to quickly identify and remediate issues as they arise.

In early 2022, Darktrace observed a compliance incident on the network of a customer based in the energy sector when an individual inserted a mobile phone SIM card into the Human-Machine Interface (HMI) of an Industrial Control System (ICS). The HMI proceeded to access several non-compliant external endpoints, including Facebook. Typically IT and OT networks should be air-gapped to keep critical industrial infrastructure protected and operational.

In this case, Darktrace DETECT triggered a compliance model breach (ICS:: OT Compliance External Connection) and the customer was quickly able mitigate the issue before any meaningful harm could be done to the network.

Incident: Personal Email Use in Corporate Setting

The email space contains a litany of compliance standards and is one of the most common places where security standards are breached, with research demonstrating that “91% of all cyber attacks start with a phishing email.”[1]

In late October 2022, Darktrace/Email identified an email from the recipient’s personal address containing a suspicious link. As the user regularly sent emails between their corporate and personal addresses, this freemail address was a known correspondent. However, this personal email address had been compromised and sent a phishing email to the user’s corporate address. Darktrace/Email immediately identified the suspicious link and alerted the customer, recommending that their security team lock the link. Unfortunately, the customer did not have autonomous response actions for Email enabled, so the recipient was able to open the link and input their corporate credentials on the phishing page. 

Not only is Darktrace/Email able to assess and mitigate threats from personal email addresses, it can also identify suspicious links inside these emails that may have evaded traditional security measures by using a known correspondence. By enabling autonomous response actions, Darktrace/Email is able to follow this up by instantaneously locking such links, ensuring they cannot be opened and preventing the account from being compromised.

Incident: Multi-Factor Authentication for SaaS Accounts

A desire for increased efficiency and cost-effectiveness are two of the reasons underpinning the widespread adoption of cloud-based Software-as-a-Service (SaaS) solutions. However, third-party SaaS environments are not always held to the same compliance standards as traditional on-premisis network infrastructure.

Multi-factor Authentication (MFA) in SaaS environments requires users to prove their identity in at least two ways before granting them access to applications. This significantly reduces the risk of compromise,  but it is not a silver-bullet to prevent account compromise and is still not universally adopted as a baseline security practice.

In October 2022, Darktrace observed an unusual login from a rare IP address on the SaaS account of a customer that did not have MFA employed. Following this initial access, the actor created a new rule and sent emails containing suspicious links to several internal recipients. Further investigation revealed that the link directed to a fake Office365 login portal intended to harvest user credentials. Darktrace/Email and RESPOND for Apps worked in tandem to instantaneously detect this suspicious activity and force the user to log out, while alerting the customer’s security team to the incident.  As a security practice, MFA provides an additional but not guaranteed means of protecting companies from internal theft, data loss, and external access from malicious actors, but its effectiveness is contingent on its roll out across a company. Darktrace DETECT and RESPOND provide an autonomous early warning system and additional layer of security to quickly isolate and contain compromised accounts even in the absence of MFA.

Conclusion

Compliance standards are the building blocks for the cyber hygiene of any organization, but in the current cyber security landscape simply adhering to standards is not enough to close gaps from non-compliant behavior. Following up compliance standard obedience supported by additional measures and technology to tackle compliance breaches significantly reduces the risk of compromise and data breaches, in addition to financial and reputational damage. Ensuring compliance issues are not disregarded as background noise by security teams will help to ensure that minor breaches do not escalate and become legitimate threats.

Darktrace’s suite of products provides an additional layer of detection and autonomous response, alerting customers to ongoing compliance issues and preventing them from causing genuine harm or compromise to the network.

Credit to: Rachel Resznekov, Cyber Security Analyst, Roberto Romeu, Senior SOC Analyst 

Appendices

External Sources: 

hxxps[:]//www[.]comptia[.]org/content/articles/what-is-cybersecurity-compliance#\

hxxps[:]//darkcubed[.]com/compliance

hxxps[:]//www[.]zeguro[.]com/blog/cybersecurity-compliance-101

hxxps[:]//www[.]itgovernanceusa[.]com/cybersecurity-standards

hxxps[:]//www[.]linkedin[.]com/pulse/dangers-using-personal-email-work-partners-plus

hxxps[:]//www[.]metacompliance[.]com/lp/ultimate-guide-phishing

[1] hxxps[:]//www[.]metacompliance[.]com/lp/ultimate-guide-phishing

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Rachel Resnekov
Cyber Analyst

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April 21, 2025

Why Asset Visibility and Signature-Based Threat Detection Fall Short in ICS Security

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In the realm of Industrial Control System (ICS) security, two concepts often dominate discussions:

  1. Asset visibility
  2. Signature-based threat detection

While these are undoubtedly important components of a cybersecurity strategy, many organizations focus on them as the primary means to enhance ICS security. However, this is more of a short-term approach and these organizations often realize too late that these efforts do not translate into actually securing their environment.

To truly secure your environment, organizations should focus their efforts on anomaly detection across core network segments. This shift enables enhanced threat detection, while also providing a more meaningful and dynamic view of asset communication.

By prioritizing anomaly detection, organizations can build a more resilient security posture, detecting and mitigating threats before they escalate into serious incidents.

The shortcomings of asset visibility and signature-based threat detection

Asset visibility is frequently touted as the foundation of ICS security. The idea is that you cannot protect what you cannot see.

However, organizations that invest heavily in asset discovery tools often end up with extensive inventories of connected devices but little actionable insight into their security posture or risk level, let alone any indication as to whether these assets have been compromised.

Simply knowing what assets exist does not equate to securing them.

Worse, asset discovery is often a time-consuming static process. By the time practitioners complete their inventory, not only is there likely to have been changes to their assets, but the threat landscape may have already evolved, introducing new vulnerabilities and attack vectors  that were not previously accounted for.

Signature-based detection is reactive, not proactive

Traditional signature-based threat detection relies on known attack patterns and predefined signatures to identify malicious activity. This approach is fundamentally reactive because it can only detect threats that have already been identified elsewhere.

In an ICS environment where cyber-attacks on OT systems have become more frequent, sophisticated, and destructive, signature-based detection provides a false sense of security while failing to detect sophisticated, previously unseen threats:

Additionally, adversaries often dwell within OT networks for extended periods, studying their specific conditions to identify the most effective way to cause disruption. This means that the likelihood of any attack within OT network looking the same as a previous attack is unlikely.

Implementation effort vs. actual security gains

Many organizations spend considerable time and resources implementing asset visibility solutions and signature-based detection systems only to be required to constantly tune and adjust the sensitivity of the solution.

Despite these efforts, these tools often fail to deliver the level of protection expected, leaving gaps in detection, an overwhelming amount of asset data, and a constant stream of false positives and false negatives from signature-based systems.

A more effective approach: Anomaly detection at core network segments

While it's important to understand the type of device involved during alert triage, organizations should shift their focus from static asset visibility and threat signatures to anomaly detection across critical network segments. This method provides a superior approach to ICS security for several reasons:

Proactive threat detection

Anomaly detection monitors network behavior in real time and identifies deviations . This means that even novel or previously unseen threats can be detected based on unusual network activity, rather than relying on predefined signatures.

Granular security insights

By analyzing traffic patterns across key network segments, organizations can gain deeper insights into how assets interact. This not only improves threat detection but also organically enhances asset visibility. Instead of simply cataloging devices, organizations gain meaningful visibility into how they behave within the network, understanding their unique pattern of life, and making it easier to detect malicious activity.

Efficiency and scalability

Implementing anomaly detection allows security teams to focus on real threats rather than sifting through massive inventories of assets or managing signature updates. It scales better with evolving threats and provides continuous monitoring without requiring constant manual intervention.

Enhanced threat detection for critical infrastructure

Unlike traditional security approaches that rely on static baselines or threat intelligence that doesn't reflect the unique behaviors of your OT environment, Darktrace / OT uses multiple AI techniques to continuously learn and adapt to your organization’s real-world activity across IT, OT, and IoT.

By building a dynamic understanding of each device’s pattern of life, it detects threats at every stage of the kill chain — from known malware to zero-days and insider attacks — without overwhelming your team with false positives or unnecessary alerts. This ensures scalable protection as your environment evolves, without a significant increase in operational overhead.

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About the author
Jeffrey Macre
Industrial Security Solutions Architect

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April 16, 2025

Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)

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DEMIST-2 is Darktrace’s latest embedding model, built to interpret and classify security data with precision. It performs highly specialized tasks and can be deployed in any environment. Unlike generative language models, DEMIST-2 focuses on providing reliable, high-accuracy detections for critical security use cases.

DEMIST-2 Core Capabilities:  

  • Enhances Cyber AI Analyst’s ability to triage and reason about security incidents by providing expert representation and classification of security data, and as a part of our broader multi-layered AI system
  • Classifies and interprets security data, in contrast to language models that generate unpredictable open-ended text responses  
  • Incorporates new innovations in language model development and architecture, optimized specifically for cybersecurity applications
  • Deployable across cloud, on-prem, and edge environments, DEMIST-2 delivers low-latency, high-accuracy results wherever it runs. It enables inference anywhere.

Cybersecurity is constantly evolving, but the need to build precise and reliable detections remains constant in the face of new and emerging threats. Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2) addresses these critical needs and is designed to create stable, high-fidelity representations of security data while also serving as a powerful classifier. For security teams, this means faster, more accurate threat detection with reduced manual investigation. DEMIST-2's efficiency also reduces the need to invest in massive computational resources, enabling effective protection at scale without added complexity.  

As an embedding language model, DEMIST-2 classifies and creates meaning out of complex security data. This equips our Self-Learning AI with the insights to compare, correlate, and reason with consistency and precision. Classifications and embeddings power core capabilities across our products where accuracy is not optional, as a part of our multi-layered approach to AI architecture.

Perhaps most importantly, DEMIST-2 features a compact architecture that delivers analyst-level insights while meeting diverse deployment needs across cloud, on-prem, and edge environments. Trained on a mixture of general and domain-specific data and designed to support task specialization, DEMIST-2 provides privacy-preserving inference anywhere, while outperforming larger general-purpose models in key cybersecurity tasks.

This proprietary language model reflects Darktrace's ongoing commitment to continually innovate our AI solutions to meet the unique challenges of the security industry. We approach AI differently, integrating diverse insights to solve complex cybersecurity problems. DEMIST-2 shows that a refined, optimized, domain-specific language model can deliver outsized results in an efficient package. We are redefining possibilities for cybersecurity, but our methods transfer readily to other domains. We are eager to share our findings to accelerate innovation in the field.  

The evolution of DEMIST-2

Key concepts:  

  • Tokens: The smallest units processed by language models. Text is split into fragments based on frequency patterns allowing models to handle unfamiliar words efficiently
  • Low-Rank Adaptors (LoRA): Small, trainable components added to a model that allow it to specialize in new tasks without retraining the full system. These components learn task-specific behavior while the original foundation model remains unchanged. This approach enables multiple specializations to coexist, and work simultaneously, without drastically increasing processing and memory requirements.

Darktrace began using large language models in our products in 2022. DEMIST-2 reflects significant advancements in our continuous experimentation and adoption of innovations in the field to address the unique needs of the security industry.  

It is important to note that Darktrace uses a range of language models throughout its products, but each one is chosen for the task at hand. Many others in the artificial intelligence (AI) industry are focused on broad application of large language models (LLMs) for open-ended text generation tasks. Our research shows that using LLMs for classification and embedding offers better, more reliable, results for core security use cases. We’ve found that using LLMs for open-ended outputs can introduce uncertainty through inaccurate and unreliable responses, which is detrimental for environments where precision matters. Generative AI should not be applied to use cases, such as investigation and threat detection, where the results can deeply matter. Thoughtful application of generative AI capabilities, such as drafting decoy phishing emails or crafting non-consequential summaries are helpful but still require careful oversight.

Data is perhaps the most important factor for building language models. The data used to train DEMIST-2 balanced the need for general language understanding with security expertise. We used both publicly available and proprietary datasets.  Our proprietary dataset included privacy-preserving data such as URIs observed in customer alerts, anonymized at source to remove PII and gathered via the Call Home and aianalyst.darktrace.com services. For additional details, read our Technical Paper.  

DEMIST-2 is our way of addressing the unique challenges posed by security data. It recognizes that security data follows its own patterns that are distinct from natural language. For example, hostnames, HTTP headers, and certificate fields often appear in predictable ways, but not necessarily in a way that mirrors natural language. General-purpose LLMs tend to break down when used in these types of highly specialized domains. They struggle to interpret structure and context, fragmenting important patterns during tokenization in ways that can have a negative impact on performance.  

DEMIST-2 was built to understand the language and structure of security data using a custom tokenizer built around a security-specific vocabulary of over 16,000 words. This tokenizer allows the model to process inputs more accurately like encoded payloads, file paths, subdomain chains, and command-line arguments. These types of data are often misinterpreted by general-purpose models.  

When the tokenizer encounters unfamiliar or irregular input, it breaks the data into smaller pieces so it can still be processed. The ability to fall back to individual bytes is critical in cybersecurity contexts where novel or obfuscated content is common. This approach combines precision with flexibility, supporting specialized understanding with resilience in the face of unpredictable data.  

Along with our custom tokenizer, we made changes to support task specialization without increasing model size. To do this, DEMIST-2 uses LoRA . LoRA is a technique that integrates lightweight components with the base model to allow it to perform specific tasks while keeping memory requirements low. By using LoRA, our proprietary representation of security knowledge can be shared and reused as a starting point for more highly specialized models, for example, it takes a different type of specialization to understand hostnames versus to understand sensitive filenames. DEMIST-2 dynamically adapts to these needs and performs them with purpose.  

The result is that DEMIST-2 is like having a room of specialists working on difficult problems together, while sharing a basic core set of knowledge that does not need to be repeated or reintroduced to every situation. Sharing a consistent base model also improves its maintainability and allows efficient deployment across diverse environments without compromising speed or accuracy.  

Tokenization and task specialization represent only a portion of the updates we have made to our embedding model. In conjunction with the changes described above, DEMIST-2 integrates several updated modeling techniques that reduce latency and improve detections. To learn more about these details, our training data and methods, and a full write-up of our results, please read our scientific whitepaper.

DEMIST-2 in action

In this section, we highlight DEMIST-2's embeddings and performance. First, we show a visualization of how DEMIST-2 classifies and interprets hostnames, and second, we present its performance in a hostname classification task in comparison to other language models.  

Embeddings can often feel abstract, so let’s make them real. Figure 1 below is a 2D visualization of how DEMIST-2 classifies and understands hostnames. In reality, these hostnames exist across many more dimensions, capturing details like their relationships with other hostnames, usage patterns, and contextual data. The colors and positions in the diagram represent a simplified view of how DEMIST-2 organizes and interprets these hostnames, providing insights into their meaning and connections. Just like an experienced human analyst can quickly identify and group hostnames based on patterns and context, DEMIST-2 does the same at scale.  

DEMIST-2 visualization of hostname relationships from a large web dataset.
Figure 1: DEMIST-2 visualization of hostname relationships from a large web dataset.

Next, let’s zoom in on two distinct clusters that DEMIST-2 recognizes. One cluster represents small businesses (Figure 2) and the other, Russian and Polish sites with similar numerical formats (Figure 3). These clusters demonstrate how DEMIST-2 can identify specific groupings based on real-world attributes such as regional patterns in website structures, common formats used by small businesses, and other properties such as its understanding of how websites relate to each other on the internet.

Cluster of small businesses
Figure 2: Cluster of small businesses
Figure 3: Cluster of Russian and Polish sites with a similar numerical format

The previous figures provided a view of how DEMIST-2 works. Figure 4 highlights DEMIST-2’s performance in a security-related classification task. The chart shows how DEMIST-2, with just 95 million parameters, achieves nearly 94% accuracy—making it the highest-performing model in the chart, despite being the smallest. In comparison, the larger model with 2.78 billion parameters achieves only about 89% accuracy, showing that size doesn’t always mean better performance. Small models don’t mean poor performance. For many security-related tasks, DEMIST-2 outperforms much larger models.

Hostname classification task performance comparison against comparable open source foundation models
Figure 4: Hostname classification task performance comparison against comparable open source foundation models

With these examples of DEMIST-2 in action, we’ve shown how it excels in embedding and classifying security data while delivering high performance on specialized security tasks.  

The DEMIST-2 advantage

DEMIST-2 was built for precision and reliability. Our primary goal was to create a high-performance model capable of tackling complex cybersecurity tasks. Optimizing for efficiency and scalability came second, but it is a natural outcome of our commitment to building a strong, effective solution that is available to security teams working across diverse environments. It is an enormous benefit that DEMIST-2 is orders of magnitude smaller than many general-purpose models. However, and much more importantly, it significantly outperforms models in its capabilities and accuracy on security tasks.  

Finding a product that fits into an environment’s unique constraints used to mean that some teams had to settle for less powerful or less performant products. With DEMIST-2, data can remain local to the environment, is entirely separate from the data of other customers, and can even operate in environments without network connectivity. The size of our model allows for flexible deployment options while at the same time providing measurable performance advantages for security-related tasks.  

As security threats continue to evolve, we believe that purpose-built AI systems like DEMIST-2 will be essential tools for defenders, combining the power of modern language modeling with the specificity and reliability that builds trust and partnership between security practitioners and AI systems.

Conclusion

DEMIST-2 has additional architectural and deployment updates that improve performance and stability. These innovations contribute to our ability to minimize model size and memory constraints and reflect our dedication to meeting the data handling and privacy needs of security environments. In addition, these choices reflect our dedication to responsible AI practices.

DEMIST-2 is available in Darktrace 6.3, along with a new DIGEST model that uses GNNs and RNNs to score and prioritize threats with expert-level precision.

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About the author
Margaret Cunningham, PhD
Director, Security & AI Strategy, Field CISO
Your data. Our AI.
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