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February 1, 2021

Explore AI Email Security Approaches with Darktrace

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Feb 2021
Stay informed on the latest AI approaches to email security. Explore Darktrace's comparisons to find the best solution for your cybersecurity needs!

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.
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Dan Fein
VP, Product

Based in New York, Dan joined Darktrace’s technical team in 2015, helping customers quickly achieve a complete and granular understanding of Darktrace’s product suite. Dan has a particular focus on Darktrace/Email, ensuring that it is effectively deployed in complex digital environments, and works closely with the development, marketing, sales, and technical teams. Dan holds a Bachelor’s degree in Computer Science from New York University.

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Company Shuts Down Cyber-attacks with “Flawless” Detection and Response from Darktrace

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Growing pains: Balancing efficiency with risk  

This organization has recently scaled its operations, and numerous acquisitions have significantly boosted the organization’s capabilities and growth. However, this also creates work and high expectations for the organization’s IT and security teams. Within 12 months of an acquisition, the teams must fully integrate each new business onto the company’s platform. “A huge piece of that integration plan is rolling out our security controls,” said the CISO. “While our goal is to connect those facilities up as quickly as possible to drive efficiency, we also need to implement the proper security controls to protect the enterprise.”

Gap beyond the perimeter  

The organization had established strong security measures to safeguard its perimeter; however, the CISO identified a critical gap in real-time network monitoring. If the perimeter were breached, threats were only discovered after an endpoint was compromised and the issue was manually reported.

As digital transformation progresses, the need to adopt advanced technologies is becoming essential, particularly as organizations begin to open up operational environments to greater connectivity. Many processes still rely on traditional methods, and integrating innovative solutions could drive significant improvements in efficiency and productivity. “We’re committed to adopting cutting-edge technologies,” the CISO explained. “But we understood that without more robust network security controls, opening up our operational environments would expose us to heightened risks, including advanced threats like ransomware.”

Building a layered, proactive security strategy with Darktrace  

To close the gap beyond the perimeter, the company embarked on a free trial with Darktrace. The CISO recalls: “The trials were fantastic. It was obvious that Darktrace was exactly what we needed. The Darktrace team was also very knowledgeable and helpful throughout the process, which was impressive.”  

Today, the organization is using a combination of Darktrace solutions for its layered security approach, including:

Detecting unusual behavior with AI  

Darktrace’s use of machine learning and Self-Learning AI is one of the reasons the company chose Darktrace. Instead of teaching an AI system what an ‘attack’ looks like, training it on large data lakes of thousands of organizations’ data, Darktrace AI learns from the company’s own unique data and user activity to learn and create baseline models of what ‘normal’ looks like for their business.

Darktrace can then detect subtle deviations and unusual activity that signals a possible threat. “That fascinated us because what it really means is this technology doesn’t need to know about every single threat because the threat itself isn’t important, it’s the behavior of the activity that’s important. That capability is unique when it when it comes to threat detection,” said the CISO.

Identifying and mitigating high-impact attack paths

The security team appreciated that with Darktrace they could take a more proactive approach to security by exposing high-risk attack paths through modeling and AI risk assessments. Darktrace / Proactive Exposure Management gives them visibility into vulnerable entry points and assets, identifies active risks, and prioritizes the most important security issues to be addressed.

“Specific users and assets within our business have a higher risk of being targeted by a cyber-attack, for example our executives,” said the CISO. “With Darktrace, we get an adversarial view of our risk. We can see the attack path around those potential targets and proactively take measures to mitigate that vulnerability and prevent an attack.”

Driving up productivity while putting the brakes on cyber-attacks  

The security team collaborated with Darktrace to fine tune the models that really fit their business. With Darktrace now automating most of their threat detection and response efforts, productivity has soared, the security team is now focused on delivering greater value to the business and, most importantly, Darktrace proved it could quickly detect and shut down a major cyber-attack–and do so without impacting business operations.

Fueling team productivity with automation and AI

Prior to using Darktrace, the security team had little visibility into potential risks beyond the perimeter. Today, the team has full control and visibility over the network. “My team is now spending 80-90% of their time doing proactive work because Darktrace is managing the vast majority of our detect and response needs. The team really has faith in the Darktrace system,” said the CISO.  

With less time spent on low-level manual tasks, the security team can now focus on higher priority initiatives. For example, they have expanded their internal vulnerability assessments across the entire group. The team couldn’t focus on this additional audit and vulnerability management work if Darktrace wasn’t taking care of most of their security monitoring. “Darktrace has allowed us to move on to these additional kinds of governance projects that we otherwise would have to hire an army of staff to get through”.

Stopping email threats in their tracks

Using Darktrace / EMAIL, the company has identified and blocked a significant percentage of emails that were making it past their native email filters. “Darktrace is especially good at detecting impersonation emails, and we really appreciate its ability to automatically remove suspicious emails directly from a user’s inbox. It adds an extra level of confidence,” said the CISO.

Self-Learning AI understands anomalies within unique communication patterns to stop known and unknown threats. For example, when an employee sent an email to a brand new domain, Darktrace identified the behavior as unusual and inconsistent with baseline models and blocked the email.

Darktrace passes the biggest test of all

In 2024, the company experienced the value of the security system firsthand when attackers exploited a vulnerability in a third-party remote support solution that they was using. This solution provided remote access and tech support capabilities. If successful, the attackers could have infiltrated high-value end points and created their own administrative user, giving them full control over the server.

“We first became aware of the attack when Darktrace notified us of unusual behavior coming from the remote support server,” said the CISO. The attackers were attempting to put backdoors onto the service with the intent of selling access to the highest bidder who would then install ransomware on their servers. It all happened very quickly, as the attackers tried to connect to the internal network and other servers, while also firing off a host of other actions, like PowerShell commands, to escalate their privileges.  

“Darktrace worked flawlessly. There was no chance that ransomware was ever going to come in,” the CISO said. “Even though there was no signature to really look at, Darktrace realized this was not normal behavior for this server, shutting down connections and doing everything it could do to stop the attack.” Within eight hours, the security team identified and stopped the attack, severed its connection to the third-party solution, and completed additional analysis and clean-up. “In addition to our own investigation, third parties like our external SOC and legal department also confirmed that Darktrace performed as expected. We were able to report back to the executive team that there was zero risk that any data or systems were compromised.”

Post-attack, there was no need to make any changes to Darktrace. The team consistently reviews its models and baselines, often collaborating with Darktrace to make adjustments when needed to continuously improve performance. “Because of this relationship and constant engagement with Darktrace’s technical teams, we didn't have to go back and ask: ‘why wasn’t this updated’ or ‘why didn’t this model work.’ The models worked.”

His advice to other organizations facing similar challenges? First, focus on updating, patching, and vulnerability management, and act quickly when vulnerabilities are identified. His second piece of advice: “have an automated detection system like Darktrace in place so you can respond at the speed that these attacks evolve. Humans can no longer keep up with a scripted attack as it moves around and tries to compromise items on your network. You need the right technology to fight these types of attacks.”

Dynamic capabilities for a dynamic future

Real-time playbooks

With a proactive, enterprise-wide security strategy in place, the CISO now has the time to think about future projects and innovations. He’s particularly interested in the idea of generating playbooks on the fly in response to real-time events. He believes cyber-attacks are far too varied for a static playbook to be useful; when an attack strikes, teams need to quickly understand exactly what’s in front of them and how to shut it down. “This fits into our future cybersecurity strategy, and Darktrace is the only company I’ve seen talking about building playbooks dynamically. This kind of technology would really help bring our cybersecurity strategy full circle.”

“Darktrace ’s technology, experience and expertise is helping us staying ahead of cyber-attacks, minimizing our risk and driving greater productivity for our team,” said the CISO. In collaboration with Darktrace, the team have created a security foundation that is both powerful and agile. “While Darktrace is detecting and responding to attacks targeting our business today, we know that it’s always learning, adapting and scaling to ensure we’re protected tomorrow. That gives me peace of mind and the freedom to focus on our future.”

Download the Darktrace / NETWORK Solution Brief

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Protect in real time: Defend against known and emerging threats without relying on historical data or external intelligence.

Full visibility: Gain comprehensive insights across all network environments, including on-premises, cloud, and remote devices.

AI-powered efficiency: Streamline incident response with AI automation, saving time and resources while ensuring minimal disruption to operations.

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Darktrace is Positioned as a Leader in the IDC MarketScape: Worldwide Network Detection and Response 2024 Vendor Assessment

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Darktrace is pleased to announce that we have been positioned as a Leader in the IDC MarketScape: Worldwide Network Detection and Response 2024 Vendor Assessment. We believe this further highlights Darktrace’s position as a pioneer in the NDR market and follows similar recognition from KuppingerCole, who recently named Darktrace as an Overall Leader, Product Leader, Market Leader and Innovation Leader in the KuppingerCole Leadership Compass: Network Detection and Response (2024).

Network Detection and Response (NDR) solutions are uniquely positioned to provide visibility over the core hub of a business and employee activity, analyzing North-South and East-West traffic to identify threats across the modern network. NDR provides a rich and true source of anomalies and goes beyond process level data that is relied on by Endpoint Detection and Response (EDR) agents that do not provide network level visibility and can be misconfigured at any time.1

Metadata from network traffic can be used to detect a variety of different threats based on events such as anomalous port usage, unusual upload/download activity, impossible travel and many other activities. This has been accelerated by the increased usage of user behavioral analytics (UBA) in network security, which establishes statistical baselines about network entities and highlights deviations from expected activity.1

Darktrace is recognized as a Leader in the IDC MarketScape due to our leadership in the market and our pioneering leadership in AI over the past decade, alongside a variety of other unique differentiators and innovations in the NDR industry.

Darktrace / NETWORK™ delivers full visibility, real time threat detection and Autonomous Response capabilities across an organization’s on-premises, cloud, hybrid and virtual environments, including remote worker endpoints.

Unique Approach to AI

Most NDR vendors and network security tools such as IDS/IPS rely on detecting known attacks with historical data and supervised machine learning, leaving organizations blind and vulnerable to novel threats such as zero-days, variants of known attacks, supply chain attacks and insider threats.

These vendors also tend to apply AI models that are trained globally, and are not unique to each organization’s environment, which creates a high number of false positives and alerts that ultimately lack business context.

The IDC MarketScape recognizes that Darktrace takes a differentiated approach in the market with regards to delivering network detection and response capabilities, noting; “Darktrace is unique in that it does not rely on rules and signatures but rather learns what constitutes as normal for an organization and generates alerts when there is a deviation.”1

Darktrace / NETWORK achieves this through the use of Self-Learning AI and unsupervised machine learning to understand what is normal network behavior, continuously analyzing, mapping and modeling every connection to create a full picture of devices, identities, connections and potential attack paths. Darktrace Self-Learning AI autonomously optimizes itself to cut through the noise and quickly surface genuine, prioritized network security incidents – significantly reducing false positives and removing the hassle of needing to continually tuning alerts manually.

Darktrace’s unique approach to AI also extends to the investigation and triage of network alerts with Cyber AI Analyst. Unlike a chat or prompt based LLM, Cyber AI Analyst investigates all relevant alerts in an environment, including third party alerts, autonomously forming hypotheses and reaching conclusions just like a human analyst would, accelerating SOC Level 2 analyses of incidents by 10x. Cyber AI Analyst also typically providing SOC teams with up to 50,000 additional hours annually of Level 2 analysis producing high level alerts and written reporting, transforming security operations.2

Darktrace also uses its deep understanding of what is normal for a network to identify suspicious behavior, leveraging Autonomous Response capabilities to shut down both known and novel threats in real time, taking targeted actions without disrupting business operations. Darktrace / NETWORK is the only NDR solution that can autonomously enforce a pattern of life based on what is normal for a standalone device or group of peers, rapidly containing and disarming threats based on the overall context of the environment and a granular understanding of what is normal for a device or user – instead of relying on historical attack data.

Continued NDR Market Leadership

Darktrace has been recognized as a Leader in the NDR market, and the IDC MarketScape listed a variety of strengths:

  • Darktrace achieves roughly one-fifth of all global NDR revenue. This is important because other IT and cybersecurity solutions providers necessarily want to have integration with Darktrace.
  • The AI algorithms that Darktrace uses for NDR have had 10 years of deployments, tuning, and learning to draw from.
  • Darktrace is available as a SaaS, as an enterprise license, and as physical, hybrid, or virtual appliances. Darktrace also offers an endpoint agent and visibility into VPN and ZTNA.
  • Darktrace integrates with 30+ different interfaces including SIEM, SOAR, XDR platforms, IT ticketing solutions, and their own dashboards. The Darktrace Threat Visualizer highlights events and incidents from the entire deployment including cloud, apps, email, endpoint, zero trust, network, and OT.
  • Darktrace / NETWORK charts the progress that the SOC is making over time with key metrics such as MTTD/MTTR, alerts generated and processed, and other criteria.
  • Darktrace reported coverage of 14 MITRE ATT&CK categories, 158 techniques, and 184 subtechniques

Proactive Network Resilience

The IDC MarketScape notes, “Ultimately, NDR shines as a standalone detection and response technology but is especially powerful when combined with other platforms. NDR in combination with other control points such as endpoint, data, identity, and application provides the proper context when winnowing alerts and trying to uncover a single source of truth.” . Darktrace comprehensively addresses this as part of the ActiveAI Security Platform, by combining network alerts with data from / EMAIL, / IDENTITY, / ENDPOINT, / CLOUD and / OT, providing deeper contextual analysis for each network alert and automatically enriching investigations.

Darktrace also goes beyond NDR solutions with capabilities that are closely linked to our NDR offering, helping clients to achieve and maintain a state of proactive network resilience:

  • Darktrace / Proactive Exposure Management – look beyond just CVE risks to discover, prioritize and validate risks by business impact and how to address them early, reducing the number of real threats that security teams need to handle.
  • Darktrace / Incident Readiness & Recovery – lets teams respond in the best way to each incident and proactively test their familiarity and effectiveness of IR workflows with sophisticated incident simulations based on their own analysts and assets.

Together, these solutions allow Darktrace / NETWORK to go beyond the traditional approach to NDR and shift teams to a more hardened and proactive stance.

Protecting Clients with Continued Innovation

Darktrace invests heavily in Research and Development to continue providing customers with market-leading NDR capabilities and innovations, which was reflected in our position in the Leader category of the MarketScape report for both capabilities and strategy. We are led by the needs and challenges of our customers, which serve as the driving force behind our continued innovation and leadership in the NDR market. The IDC MarketScape report underlines this approach with the following feedback presented by Darktrace customers:

“A customer intimated that 99% of their detections were OOTB with little need to tune or define parameters.”
“A customer reported that it had early warnings for adversarial tactics such as suspicious SMB scanning, suspicious remote execution, remote desktop protocol (RDP) scanning, data exfiltration, C2C, LDAP query, and suspicious Kerberos activity.”
“The client could use Regex to determine if suspicious behavior was found elsewhere on the network.”

Thousands of customers around the world across all industries and sectors rely on Darktrace / NETWORK to protect against known and novel threats. From the latest vulnerabilities in network hardware to sophisticated new strains of ransomware and everything in-between, Darktrace helps clients detect and respond to all types of threats affecting their networks and avoid business disruption, even from the latest attacks.

Find out more about the unique capabilities of Darktrace / NETWORK and our application of AI in network security in the IDC MarketScape excerpt.

References

  1. IDC MarketScape: Worldwide Network Detection and Response 2024 Vendor Assessment (Doc #US51752324, November 2024)
  2. Darktrace Cyber AI Analyst Customer Fleet Data
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About the author
Mikey Anderson
Product Manager, Network Detection & Response
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