How Empowering End Users can Improve Your Email Security and Decrease the Burden on the SOC
Most email security solutions either assume end-user reporting is of poor quality, so don’t prioritize it, or triage every user-reported email equally without any attempt to improve long-term efficiency. This blog explores how Darktrace aims to improve user reporting from the ground up, reducing the 90% falsely reported phishing and decreasing the load on security teams.
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
Carlos Gray
Senior Product Marketing Manager, Email
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08
May 2024
Why do we pay attention to the end user?
Every email security solution filters inbound mail, then typically hands over false positives and false negatives to the security team for manual triage. A crucial problem with this lifecycle is that it ignores the inevitability of end users being at the front line of any organization. Employees may receive point in time security awareness training, but it is rarely engaging or contextualized to their reality. While an employee may report a suspicious-looking email to the security team, they will rarely get to understand the outcome or impact of that decision. This means that the quality of reporting never improves, so the burden on the security team of triaging these emails – of which 90% are falsely reported – persists and grows with the business over time.
At Darktrace, we recognize that employees will always be on the front line of email security. That’s why we aim to improve end-user reporting from the ground up, reducing the overall number of emails needing triage and saving security team resource.
How does Darktrace improve the quality of end-user reporting?
Darktrace prioritizes improving users’ security awareness to increase the quality of end-user reporting from day one. We train users and optimize their experience, which in turn provides better detection.
That starts with training and security awareness. Traditionally, organizations oblige employees to attend point-in-time training sessions which interrupt their daily work schedules. With Darktrace/Email, if a message contains some potentially suspicious markers but is most likely safe, Darktrace takes a specific action to neutralize the risky components and presents it to the user with a simple narrative explaining why certain elements have been held back. The user can then decide whether to report this email to the security team.
Figure 1: AI shares its analysis in context and in real time at the moment a user is questioning an email
The AI narrative gives the user context for why their specific email may carry risk, putting their security awareness training into practice. This creates an element of trust with the security solution, rather than viewing it as outside of daily workflows. Users may also receive a daily or weekly digest of their held emails and make a decision on whether to release or report them.
Whatever the user’s existing workflow is for reporting emails, Darktrace/Email can integrate with it and improve its quality. Our add-in for Outlook gives users a fully optimized experience, allowing them to engage with the narratives for each email, as well as non-productive mail management. However, if teams want to integrate Darktrace into an existing workflow, it can analyze emails reported to an internal SOC mailbox, the native email provider’s 'Report Phish’ button, or the ‘Knowbe4’ button.
By empowering the user with contextual feedback on each unique email, we foster employee engagement and elevate both reporting quality and security awareness. In fact, 60% fewer benign emails are reported because of the extra context supplied by Darktrace to end users. The eventual report is then fed back to the detection algorithm, improving future decision-making.
Reducing the amount of emails that reach the SOC
Out of the higher-quality emails that do end up being reported by users, the next step is to reduce the amount of emails that reach the SOC.
Once a user reports an email, Darktrace will independently determine if the mail should be automatically remediated based on second level triage. Darktrace/Email’s Mailbox Security Assistant automates secondary triage by combining additional behavioral signals and the most advanced link analysis engine we have ever built. It detects 70% more sophisticated malicious phishing links by looking at an additional twenty times more context than at the primary analysis stage, revealing the hidden intent within interactive and dynamic webpages. This directly alleviates the burden of manual triage for security analysts.
Following this secondary triage the emails that are deemed worthy of security team attention are then passed over, resulting in a lower quantity and higher quality of emails for SOC manual triage.
Centralizing and speeding analysis for investigations
For those emails that are received by the SOC, Darktrace also helps to improve triage time for manual remediation.
AI-generated narratives and automated remediation actions empower teams to fast-track manual triage and remediation, while still providing security analysts with the necessary depth. With live inbox view, security teams gain access to a centralized platform that combines intuitive search capabilities, Cyber AI Analyst reports, and mobile application access. With all security workflows consolidated within a unified interface, users can analyze and take remediation actions without the need to navigate multiple tools, such as e-discovery platforms – eliminating console hopping and accelerating incident response.
Our customers tell us that our AI allows them to go in-depth quickly for investigations, versus other solutions that only provide a high-level view.
Figure 2: Cyber AI Analyst provides a simple language narrative for each reported email, allowing teams to quickly understand why it may be suspicious
Conclusion
Unlike our competitors, we believe that improving the quality of users’ experience is not only a nice-to-have, but a fundamental means for improving security. Any modern solution should consider end users as a key source of information as well as an opportunity for defense. Darktrace does both – optimizing the user experience as well as our AI learning from the user to augment detection.
The benefits of empowering users are ultimately felt by the security team, who benefit from improved detection, a reduction in manual triage of benign emails, and faster investigation workflows.
Augmented end user reporting is just one of a range of features new to Darktrace/Email. Check out the latest Innovations to Darktrace/Email in our recent blog.
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.
Why Data Classification Isn’t Enough to Prevent Data Loss
In a world of growing data volume and diversity, protecting and keeping track of your organization’s sensitive information is increasingly complex – particularly when 63% of breaches stem from malicious insiders or human error. This blog explores how security teams can achieve visibility beyond the limits of data classification, without adding to the burden of data management.
Email bombing exposed: Darktrace’s email defense in action
An email bomb attack floods inboxes with a large volume of emails to disrupt operations and conceal suspicious activity, often bypassing traditional security tools. Darktrace detected such an attack in early 2025, identifying unusual email patterns and subsequent network anomalies.
FedRAMP High-compliant email security protects federal agencies from nation-state attacks
Not only has Darktrace Federal achieved its FedRAMP High Authority to Operate, one of the few cybersecurity vendors to do this, but we have also released Darktrace Commercial Government Cloud High/Email, a FedRAMP High-compliant email security solution for customers using Microsoft Government Community Cloud High.
Marcus Fowler
CEO of Darktrace Federal and SVP of Strategic Engagements and Threats
Why Asset Visibility and Signature-Based Threat Detection Fall Short in ICS Security
In the realm of Industrial Control System (ICS) security, two concepts often dominate discussions:
Asset visibility
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:
73% of organizations now reporting intrusions affecting both IT and OT systems, up from 49% in 2023.[3]
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.
Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)
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.
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.
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.
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.
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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