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February 1, 2021

Explore AI Email Security Approaches with Darktrace

Stay informed on the latest AI approaches to email security. Explore Darktrace's comparisons to find the best solution for your cybersecurity 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.
Written by
Dan Fein
VP, Product
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01
Feb 2021

Innovations in artificial intelligence (AI) have fundamentally changed the email security landscape in recent years, but it can often be hard to determine what makes one system different to the next. In reality, under that umbrella term there exists a significant distinction in approach which may determine whether the technology provides genuine protection or simply a perceived notion of defense.

One backward-looking approach involves feeding a machine thousands of emails that have already been deemed to be malicious, and training it to look for patterns in these emails in order to spot future attacks. The second approach uses an AI system to analyze the entirety of an organization’s real-world data, enabling it to establish a notion of what is ‘normal’ and then spot subtle deviations indicative of an attack.

In the below, we compare the relative merits of each approach, with special consideration to novel attacks that leverage the latest news headlines to bypass machine learning systems trained on data sets. Training a machine on previously identified ‘known bads’ is only advantageous in certain, specific contexts that don’t change over time: to recognize the intent behind an email, for example. However, an effective email security solution must also incorporate a self-learning approach that understands ‘normal’ in the context of an organization in order to identify unusual and anomalous emails and catch even the novel attacks.

Signatures – a backward-looking approach

Over the past few decades, cyber security technologies have looked to mitigate risk by preventing previously seen attacks from occurring again. In the early days, when the lifespan of a given strain of malware or the infrastructure of an attack was in the range of months and years, this method was satisfactory. But the approach inevitably results in playing catch-up with malicious actors: it always looks to the past to guide detection for the future. With decreasing lifetimes of attacks, where a domain could be used in a single email and never seen again, this historic-looking signature-based approach is now being widely replaced by more intelligent systems.

Training a machine on ‘bad’ emails

The first AI approach we often see in the wild involves harnessing an extremely large data set with thousands or millions of emails. Once these emails have come through, an AI is trained to look for common patterns in malicious emails. The system then updates its models, rules set, and blacklists based on that data.

This method certainly represents an improvement to traditional rules and signatures, but it does not escape the fact that it is still reactive, and unable to stop new attack infrastructure and new types of email attacks. It is simply automating that flawed, traditional approach – only instead of having a human update the rules and signatures, a machine is updating them instead.

Relying on this approach alone has one basic but critical flaw: it does not enable you to stop new types of attacks that it has never seen before. It accepts that there has to be a ‘patient zero’ – or first victim – in order to succeed.

The industry is beginning to acknowledge the challenges with this approach, and huge amounts of resources – both automated systems and security researchers – are being thrown into minimizing its limitations. This includes leveraging a technique called “data augmentation” that involves taking a malicious email that slipped through and generating many “training samples” using open-source text augmentation libraries to create “similar” emails – so that the machine learns not only the missed phish as ‘bad’, but several others like it – enabling it to detect future attacks that use similar wording, and fall into the same category.

But spending all this time and effort into trying to fix an unsolvable problem is like putting all your eggs in the wrong basket. Why try and fix a flawed system rather than change the game altogether? To spell out the limitations of this approach, let us look at a situation where the nature of the attack is entirely new.

The rise of ‘fearware’

When the global pandemic hit, and governments began enforcing travel bans and imposing stringent restrictions, there was undoubtedly a collective sense of fear and uncertainty. As explained previously in this blog, cyber-criminals were quick to capitalize on this, taking advantage of people’s desire for information to send out topical emails related to COVID-19 containing malware or credential-grabbing links.

These emails often spoofed the Centers for Disease Control and Prevention (CDC), or later on, as the economic impact of the pandemic began to take hold, the Small Business Administration (SBA). As the global situation shifted, so did attackers’ tactics. And in the process, over 130,000 new domains related to COVID-19 were purchased.

Let’s now consider how the above approach to email security might fare when faced with these new email attacks. The question becomes: how can you train a model to look out for emails containing ‘COVID-19’, when the term hasn’t even been invented yet?

And while COVID-19 is the most salient example of this, the same reasoning follows for every single novel and unexpected news cycle that attackers are leveraging in their phishing emails to evade tools using this approach – and attracting the recipient’s attention as a bonus. Moreover, if an email attack is truly targeted to your organization, it might contain bespoke and tailored news referring to a very specific thing that supervised machine learning systems could never be trained on.

This isn’t to say there’s not a time and a place in email security for looking at past attacks to set yourself up for the future. It just isn’t here.

Spotting intention

Darktrace uses this approach for one specific use which is future-proof and not prone to change over time, to analyze grammar and tone in an email in order to identify intention: asking questions like ‘does this look like an attempt at inducement? Is the sender trying to solicit some sensitive information? Is this extortion?’ By training a system on an extremely large data set collected over a period of time, you can start to understand what, for instance, inducement looks like. This then enables you to easily spot future scenarios of inducement based on a common set of characteristics.

Training a system in this way works because, unlike news cycles and the topics of phishing emails, fundamental patterns in tone and language don’t change over time. An attempt at solicitation is always an attempt at solicitation, and will always bear common characteristics.

For this reason, this approach only plays one small part of a very large engine. It gives an additional indication about the nature of the threat, but is not in itself used to determine anomalous emails.

Detecting the unknown unknowns

In addition to using the above approach to identify intention, Darktrace uses unsupervised machine learning, which starts with extracting and extrapolating thousands of data points from every email. Some of these are taken directly from the email itself, while others are only ascertainable by the above intention-type analysis. Additional insights are also gained from observing emails in the wider context of all available data across email, network and the cloud environment of the organization.

Only after having a now-significantly larger and more comprehensive set of indicators, with a more complete description of that email, can the data be fed into a topic-indifferent machine learning engine to start questioning the data in millions of ways in order to understand if it belongs, given the wider context of the typical ‘pattern of life’ for the organization. Monitoring all emails in conjunction allows the machine to establish things like:

  • Does this person usually receive ZIP files?
  • Does this supplier usually send links to Dropbox?
  • Has this sender ever logged in from China?
  • Do these recipients usually get the same emails together?

The technology identifies patterns across an entire organization and gains a continuously evolving sense of ‘self’ as the organization grows and changes. It is this innate understanding of what is and isn’t ‘normal’ that allows AI to spot the truly ‘unknown unknowns’ instead of just ‘new variations of known bads.’

This type of analysis brings an additional advantage in that it is language and topic agnostic: because it focusses on anomaly detection rather than finding specific patterns that indicate threat, it is effective regardless of whether an organization typically communicates in English, Spanish, Japanese, or any other language.

By layering both of these approaches, you can understand the intention behind an email and understand whether that email belongs given the context of normal communication. And all of this is done without ever making an assumption or having the expectation that you’ve seen this threat before.

Years in the making

It’s well established now that the legacy approach to email security has failed – and this makes it easy to see why existing recommendation engines are being applied to the cyber security space. On first glance, these solutions may be appealing to a security team, but highly targeted, truly unique spear phishing emails easily skirt these systems. They can’t be relied on to stop email threats on the first encounter, as they have a dependency on known attacks with previously seen topics, domains, and payloads.

An effective, layered AI approach takes years of research and development. There is no single mathematical model to solve the problem of determining malicious emails from benign communication. A layered approach accepts that competing mathematical models each have their own strengths and weaknesses. It autonomously determines the relative weight these models should have and weighs them against one another to produce an overall ‘anomaly score’ given as a percentage, indicating exactly how unusual a particular email is in comparison to the organization’s wider email traffic flow.

It is time for email security to well and truly drop the assumption that you can look at threats of the past to predict tomorrow’s attacks. An effective AI cyber security system can identify abnormalities with no reliance on historical attacks, enabling it to catch truly unique novel emails on the first encounter – before they land in the inbox.

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
Dan Fein
VP, Product

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May 21, 2025

Evaluating Email Security: How to Select the Best Solution for Your Organization

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When evaluating email security solutions, it’s crucial to move beyond marketing claims and focus on real-world performance. One of the most effective ways to achieve this is through an A/B comparison approach – a side-by-side evaluation of vendors based on consistent, predefined criteria.

This method cuts through biases, reveals true capability differences, and ensures that all solutions are assessed on a level playing field. It’s not just about finding an objectively good solution – it’s about finding the best solution for your organization’s specific needs.

An A/B comparison approach is particularly effective for three main reasons:

  1. Eliminates bias: By comparing solutions under identical conditions, it’s easier to spot differences in performance without the fog of marketing jargon.
  2. Highlights real capabilities: Direct side-by-side testing exposes genuine strengths and weaknesses, making it easier to judge which features are impactful versus merely decorative.
  3. Encourages objective decision-making: This structured method reduces emotional or brand-driven decisions, focusing purely on metrics and performance.

Let’s look at the key factors to consider when setting up your evaluation to ensure a fair, accurate, and actionable comparison.

Deployment: Setting the stage for fair evaluation

To achieve a genuine comparison, deployment must be consistent across all evaluated solutions:

  • Establish the same scope: All solutions should be granted identical visibility across relevant tenants and domains to ensure parity.
  • Set a concrete timeline: Deploy and test each solution with the same dataset, at the same points in time. This allows you to observe differences in learning periods and adaptive capabilities.

Equal visibility and synchronized timelines prevent discrepancies that could skew your understanding of each vendor’s true capabilities. But remember – quicker results might not equal better learning or understanding!

Tuning and configurations: Optimizing for real-world conditions

Properly tuning and configuring each solution is critical for fair evaluation:

  • Compare on optimal performance: Consult with each vendor to understand what optimal deployment looks like for their solution, particularly if machine learning is involved.
  • Consider the long term: Configuration adjustments should be made with long-term usage in mind. Short-term fixes can mask long-term challenges.
  • Data visibility: Ensure each solution can retain and provide search capabilities on all data collected throughout the evaluation period.

These steps guarantee that you are comparing fully optimized versions of each platform, not underperforming or misconfigured ones.

Evaluation: Applying consistent metrics

Once deployment and configurations are aligned, the evaluation itself must be consistent, to prevent unfair scoring and help to identify true differences in threat detection and response capabilities.

  • Coordinate your decision criteria: Ensure all vendors are measured against the same set of criteria, established before testing begins.
  • Understand vendor threat classification: Each vendor may have different ways of classifying threats, so be sure to understand these nuances.
  • Maintain communication: If results seem inaccurate, engage with the vendors. Their response and remediation capabilities are part of the evaluation.

Making a decision: Look beyond the metrics

When it comes to reviewing the performance of each solution, it’s important to both consider and look beyond the raw data. This is about choosing the solution that best aligns with your specific business needs, which may include factors and features not captured in the results.

  • Evaluate based on results: Consider accuracy, threats detected, precision, and response effectiveness.
  • Evaluate beyond results: Assess the overall experience, including support, integrations, training, and long-term alignment with your security strategy.
  • Review and communicate: Internally review the findings and communicate them back to the vendors.

Choosing the right email security solution isn’t just about ticking boxes, it’s about strategic alignment with your organization’s goals and the evolving threat landscape. A structured, A/B comparison approach will help ensure that the solution you select is truly the best fit.

For a full checklist of the features and capabilities to compare, as well as how to perform a commercial and technical evaluation, check out the full Buyer’s Checklist for Evaluating Email Security.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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May 21, 2025

Adapting to new USCG cybersecurity mandates: Darktrace for ports and maritime systems

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What is the Marine Transportation System (MTS)?

Marine Transportation Systems (MTS) play a substantial roll in U.S. commerce, military readiness, and economic security. Defined as a critical national infrastructure, the MTS encompasses all aspects of maritime transportation from ships and ports to the inland waterways and the rail and roadways that connect them.

MTS interconnected systems include:

  • Waterways: Coastal and inland rivers, shipping channels, and harbors
  • Ports: Terminals, piers, and facilities where cargo and passengers are transferred
  • Vessels: Commercial ships, barges, ferries, and support craft
  • Intermodal Connections: Railroads, highways, and logistics hubs that tie maritime transport into national and global supply chains

The Coast Guard plays a central role in ensuring the safety, security, and efficiency of the MTS, handling over $5.4 trillion in annual economic activity. As digital systems increasingly support operations across the MTS, from crane control to cargo tracking, cybersecurity has become essential to protecting this lifeline of U.S. trade and infrastructure.

Maritime Transportation Systems also enable international trade, making them prime targets for cyber threats from ransomware gangs to nation-state actors.

To defend against growing threats, the United States Coast Guard (USCG) has moved from encouraging cybersecurity best practices to enforcing them, culminating in a new mandate that goes into effect on July 16, 2025. These regulations aim to secure the digital backbone of the maritime industry.

Why maritime ports are at risk

Modern ports are a blend of legacy and modern OT, IoT, and IT digitally connected technologies that enable crane operations, container tracking, terminal storage, logistics, and remote maintenance.

Many of these systems were never designed with cybersecurity in mind, making them vulnerable to lateral movement and disruptive ransomware attack spillover.

The convergence of business IT networks and operational infrastructure further expands the attack surface, especially with the rise of cloud adoption and unmanaged IoT and IIoT devices.

Cyber incidents in recent years have demonstrated how ransomware or malicious activity can halt crane operations, disrupt logistics, and compromise safety at scale threatening not only port operations, but national security and economic stability.

Relevant cyber-attacks on maritime ports

Maersk & Port of Los Angeles (2017 – NotPetya):
A ransomware attack crippled A.P. Moller-Maersk, the world’s largest shipping company. Operations at 17 ports, including the Port of Los Angeles, were halted due to system outages, causing weeks of logistical chaos.

Port of San Diego (2018 – Ransomware Attack):
A ransomware attack targeted the Port of San Diego, disrupting internal IT systems including public records, business services, and dockside cargo operations. While marine traffic was unaffected, commercial activity slowed significantly during recovery.

Port of Houston (2021 – Nation-State Intrusion):
A suspected nation-state actor exploited a known vulnerability in a Port of Houston web application to gain access to its network. While the attack was reportedly thwarted, it triggered a federal investigation and highlighted the vulnerability of maritime systems.

Jawaharlal Nehru Port Trust, India (2022 – Ransomware Incident):
India’s largest container port experienced disruptions due to a ransomware attack affecting operations and logistics systems. Container handling and cargo movement slowed as IT systems were taken offline during recovery efforts.

A regulatory shift: From guidance to enforcement

Since the Maritime Transportation Security Act (MTSA) of 2002, ports have been required to develop and maintain security plans. Cybersecurity formally entered the regulatory fold in 2020 with revisions to 33 CFR Part 105 and 106, requiring port authorities to assess and address computer system vulnerabilities.

In January 2025, the USCG finalized new rules to enforce cybersecurity practices across the MTS. Key elements include (but are not limited to):

  • A dedicated cyber incident response plan (PR.IP-9)
  • Routine cybersecurity risk assessments and exercises (ID.RA)
  • Designation of a cybersecurity officer and regular workforce training (section 3.1)
  • Controls for access management, segmentation, logging, and encryption (PR.AC-1:7)
  • Supply chain risk management (ID.SC)
  • Incident reporting to the National Response Center

Port operators are encouraged to align their programs with the NIST Cybersecurity Framework (CSF 2.0) and NIST SP 800-82r3, which provide comprehensive guidance for IT and OT security in industrial environments.

How Darktrace can support maritime & ports

Unified IT + OT + Cloud coverage

Maritime ports operate in hybrid environments spanning business IT systems (finance, HR, ERP), industrial OT (cranes, gates, pumps, sensors), and an increasing array of cloud and SaaS platforms.

Darktrace is the only vendor that provides native visibility and threat detection across OT/IoT, IT, cloud, and SaaS environments — all in a single platform. This means:

  • Cranes and other physical process control networks are monitored in the same dashboard as Active Directory and Office 365.
  • Threats that start in the cloud (e.g., phishing, SaaS token theft) and pivot or attempt to pivot into OT are caught early — eliminating blind spots that siloed tools miss.

This unification is critical to meeting USCG requirements for network-wide monitoring, risk identification, and incident response.

AI that understands your environment. Not just known threats

Darktrace’s AI doesn’t rely on rules or signatures. Instead, it uses Self-Learning AI TM that builds a unique “pattern of life” for every device, protocol, user, and network segment, whether it’s a crane router or PLC, SCADA server, Workstation, or Linux file server.

  • No predefined baselines or manual training
  • Real-time anomaly detection for zero-days, ransomware, and supply chain compromise
  • Continuous adaptation to new devices, configurations, and operations

This approach is critical in diverse distributed OT environments where change and anomalous activity on the network are more frequent. It also dramatically reduces the time and expertise needed to classify and inventory assets, even for unknown or custom-built systems.

Supporting incident response requirements

A key USCG requirement is that cybersecurity plans must support effective incident response.

Key expectations include:

  • Defined response roles and procedures: Personnel must know what to do and when (RS.CO-1).
  • Timely reporting: Incidents must be reported and categorized according to established criteria (RS.CO-2, RS.AN-4).
  • Effective communication: Information must be shared internally and externally, including voluntary collaboration with law enforcement and industry peers (RS.CO-3 through RS.CO-5).
  • Thorough analysis: Alerts must be investigated, impacts understood, and forensic evidence gathered to support decision-making and recovery (RS.AN-1 through RS.AN-5).
  • Swift mitigation: Incidents must be contained and resolved efficiently, with newly discovered vulnerabilities addressed or documented (RS.MI-1 through RS.MI-3).
  • Ongoing improvement: Organizations must refine their response plans using lessons learned from past incidents (RS.IM-1 and RS.IM-2).

That means detections need to be clear, accurate, and actionable.

Darktrace cuts through the noise using AI that prioritizes only high-confidence incidents and provides natural-language narratives and investigative reports that explain:

  • What’s happening, where it’s happening, when it’s happening
  • Why it’s unusual
  • How to respond

Result: Port security teams often lean and multi-tasked can meet USCG response-time expectations and reporting needs without needing to scale headcount or triage hundreds of alerts.

Built-for-edge deployment

Maritime environments are constrained. Many traditional SaaS deployment types often are unsuitable for tugboats, cranes, or air-gapped terminal systems.

Darktrace builds and maintains its own ruggedized, purpose-built appliances and unique virtual deployment options that:

  • Deploy directly into crane networks or terminal enclosures
  • Require no configuration or tuning, drop-in ready
  • Support secure over-the-air updates and fleet management
  • Operate without cloud dependency, supporting isolated and air-gapped systems

Use case: Multiple ports have been able to deploy Darktrace directly into the crane’s switch enclosure, securing lateral movement paths without interfering with the crane control software itself.

Segmentation enforcement & real-time threat containment

Darktrace visualizes real-time connectivity and attack pathways across IT, OT, and IoT it and integrates with firewalls (e.g., Fortinet, Cisco, Palo Alto) to enforce segmentation using AI insights alongside Darktrace’s own native autonomous and human confirmed response capabilities.

Benefits of autonomous and human confirmed response:

  • Auto-isolate rogue devices before the threat can escalate
  • Quarantine a suspicious connectivity with confidence operations won’t be halted
  • Autonomously buy time for human responders during off-hours or holidays
  • This ensures segmentation isn't just documented but that in the case of its failure or exploitation responses are performed as a compensating control

No reliance on 3rd parties or external connectivity

Darktrace’s supply chain integrity is a core part of its value to critical infrastructure customers. Unlike solutions that rely on indirect data collection or third-party appliances, Darktrace:

  • Uses in-house engineered sensors and appliances
  • Does not require transmission of data to or from the cloud

This ensures confidence in both your cyber visibility and the security of the tools you deploy.

See examples here of how Darktrace stopped supply chain attacks:

Readiness for USCG and Beyond

With a self-learning system that adapts to each unique port environment, Darktrace helps maritime operators not just comply but build lasting cyber resilience in a high-threat landscape.

Cybersecurity is no longer optional for U.S. ports its operationally and nationally critical. Darktrace delivers the intelligence, automation, and precision needed to meet USCG requirements and protect the digital lifeblood of the modern port.

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About the author
Daniel Simonds
Director of Operational Technology
Your data. Our AI.
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