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December 12, 2022

ML Integration for Third-Party EDR Alerts

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12
Dec 2022
The advantages and benefits of combining EDR technologies with Darktrace: how this integration can enhance your cybersecurity strategy.

This blog demonstrates how we use EDR integration in Darktrace for detection & investigation. We’ll look at four key features, which are summarized with an example below:  

1)    Contextualizing existing Darktrace information – E.g. ‘There was a Microsoft Defender for Endpoint (MDE) alert 5 minutes after Darktrace saw the device beacon to an unusual destination on the internet. Let me pivot back into the Defender UI’
2)    Cross-data detection engineering
‘Darktrace, create an alert or trigger a response if you see a specific MDE alert and a native Darktrace detection on the same entity over a period of time’
3)    Applying unsupervised machine learning to third-party EDR alerts
‘Darktrace, create an alert or trigger a response if there is a specific MDE alert that is unusual for the entity, given the context’
4)    Use third-party EDR alerts to trigger AI Analyst
‘AI Analyst, this low-fidelity MDE alert flagged something on the endpoint. Please take a deep look at that device at the time of the Defender alert, conduct an investigation on Darktrace data and share your conclusions about whether there is more to it or not’ 

MDE is used as an example above, but Darktrace’s EDR integration capabilities extend beyond MDE to other EDRs as well, for example to Sentinel One and CrowdStrike EDR.

Darktrace brings its Self-Learning AI to your data, no matter where it resides. The data can be anywhere – in email environments, cloud, SaaS, OT, endpoints, or the network, for example. Usually, we want to get as close to the raw data as possible to get the maximum context for our machine learning. 

We will explain how we leverage high-value integrations from our technology partners to bring further context to Darktrace, but also how we apply our Self-Learning AI to third-party data. While there are a broad range of integrations and capabilities available, we will primarily look at Microsoft Defender for Endpoint, CrowdStrike, and SentinelOne and focus on detection in this blog post. 

The Nuts and Bolts – Setting up the Integration

Darktrace is an open platform – almost everything it does is API-driven. Our system and machine learning are flexible enough to ingest new types of data & combine it with already existing information.  

The EDR integrations mentioned here are part of our 1-click integrations. All it requires is the right level of API access from the EDR solutions and the ability for Darktrace to communicate with the EDR’s API. This type of integration can be setup within minutes – it currently doesn’t require additional Darktrace licenses.

Figure 1: Set-up of Darktrace Graph Security API integration

As soon as the setup is complete, it enables various additional capabilities. 
Let’s look at some of the key detection & investigation-focussed capabilities step-by-step.

Contextualizing Existing Darktrace Information

The most basic, but still highly-useful integration is enriching existing Darktrace information with EDR alerts. Darktrace shows a chronological history of associated telemetry and machine learning for each entity observed in the entities event log. 

With an EDR integration enabled, we now start to see EDR alerts for the respective entities turn up in the entity’s event log at the correct point in time – with a ton of context and a 1-click pivot back to the native EDR console: 

Figure 2: A pivot from the Darktrace Threat Visualizer to Microsoft Defender

This context is extremely useful to have in a single screen during investigations. Context is king – it reduces time-to-meaning and skill required to understand alerts.

Cross-Data Detection Engineering

When an EDR integration is activated, Darktrace enables an additional set of detections that leverage the new EDR alerts. This comes out of the box and doesn’t require any further detection engineering. It is worth mentioning though that the new EDR information is being made available in the background for bespoke detection engineering, if advanced users want to leverage these as custom metrics.

The trick here is that the added context provided by the additional EDR alerts allows for more refined detections – primarily to detect malicious activity with higher confidence. A network detection showing us beaconing over an unusual protocol or port combination to a rare destination on the internet is great – but seeing within Darktrace that CrowdStrike detected a potentially hostile file or process three minutes prior to the beaconing detection on the same device will greatly help to prioritize the detections and aid a subsequent investigation.

Here is an example of what this looks like in Darktrace:

Figure 3: A combined model breach in the Threat Visualizer

Applying Unsupervised Machine Learning to Third-Party EDR Alerts


Once we start seeing EDR alerts in Darktrace, we can start treating it like any other data – by applying unsupervised machine learning to it. This means we can then understand how unusual a given EDR detection is for each device in question. This is extremely powerful – it allows to reduce noisy alerts without requiring ongoing EDR alert tuning and opens a whole world of new detection capabilities.

As an example – let’s imagine a low-level malware alert keeps appearing from the EDR on a specific device. This might be a false-positive in the EDR, or just not of interest for the security team, but they may not have the resources or knowledge to further tune their EDR and get rid of this noisy alert.

While Darktrace keeps adding this as contextual information in the device’s event log, it could, depending on the context of the device, the EDR alert, and the overall environment, stop alerting on this particular EDR malware alert on this specific device if it stops being unusual. Over time, noise is reduced across the environment – but if that particular EDR alert appears on another device, or on the same device in a different context, it might get flagged again, as it now is unusual in the given context.

Darktrace then goes a step further, taking those unusual EDR alerts and combining them with unusual activity seen in other Darktrace coverage areas, like the network for example. Combining an unusual EDR alert with an unusual lateral movement attempt, for example, allows it to find these combined, high-precision, cross-data set anomalous events that are highly indicative of an active cyber-attack – without having to pre-define the exact nature of what ‘unusual’ looks like.

Figure 4: Combined EDR & network detection using unsupervised machine learning in Darktrace

Use Third-Party EDR Alerts to Trigger AI Analyst

Everything we discussed so far is great for improving precision in initial detections, adding context, and cutting through alert-noise. We don’t stop there though – we can also now use the third-party EDR alerts to trigger our investigation engine, the AI Analyst.

Cyber AI Analyst replicates and automates typical level 1 and level 2 Security Operations Centre (SOC) workflows. It is usually triggered by every native Darktrace detection. This is not a SOAR where playbooks are statically defined – AI Analyst builds hypotheses, gathers data, evaluates the data & reports on its findings based on the context of each individual scenario & investigation. 

Darktrace can use EDR alerts as starting points for its investigation, with every EDR alert ingested now triggering AI Analyst. This is similar to giving a (low-level) EDR alert to a human analyst and telling them: ‘Go and take a look at information in Darktrace and try to conclude whether there is more to this EDR alert or not.’

The AI Analyst subsequently looks at the entity which had triggered the EDR alert and investigates all available Darktrace data on that entity, over a period of time, in light of that EDR alert. It does not pivot outside Darktrace itself for that investigation (e.g. back into the Microsoft console) but looks at all of the context natively available in Darktrace. If concludes that there is more to this EDR alert – e.g. a bigger incident – it will report on that and clearly flag it. The report can of course be directly downloaded as a PDF to be shared with other stakeholders.

This comes in handy for a variety of reasons – primarily to further automate security operations and alleviate pressure from human teams. AI Analyst’s investigative capabilities sit on top of everything we discussed so far (combining EDR detections with detections from other coverage areas, applying unsupervised machine learning to EDR detections, …).

However, it can also come in handy to follow up on low-severity EDR alerts for which you might not have the human resources to do so.

The below screenshot shows an example of a concluded AI Analyst investigation that was triggered by an EDR alert:

Figure 5: An AI Analyst incident trained on third-party data

The Impact of EDR Integrations

The purpose behind all of this is to augment human teams, save them time and drive further security automation.

By ingesting third-party endpoint alerts, combining it with our existing intelligence and applying unsupervised machine learning to it, we achieve that further security automation. 

Analysts don’t have to switch between consoles for investigations. They can leverage our high-fidelity detections that look for unusual endpoint alerts, in combination with our already powerful detections across cloud and email systems, zero trust architecture, IT and OT networks, and more. 

In our experience, this pinpoints the needle in the haystack – it cuts through noise and reduces the mean-time-to-detect and mean-time-to-investigate drastically.

All of this is done out of the box in Darktrace once the endpoint integrations are enabled. It does not need a data scientist to make the machine learning work. Nor does it need a detection engineer or threat hunter to create bespoke, meaningful detections. We want to reduce the barrier to entry for using detection and investigation solutions – in terms of skill and experience required. The system is still flexible, transparent, and open, meaning that advanced users can create their own combined detections, leveraging unsupervised machine learning across different data sets with a few clicks.

There are of course more endpoint integration capabilities available than what we covered here, and we will explore these in future blog posts.

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.
Author
Max Heinemeyer
Global Field CISO

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

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March 28, 2025

Darktrace Recognized as the Only Visionary in the 2025 Gartner® Magic Quadrant™ for CPS Protection Platforms

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We are thrilled to announce that Darktrace has been named the only Visionary in the inaugural Gartner® Magic Quadrant™ for Cyber-Physical Systems (CPS) Protection Platforms. We feel This recognition highlights Darktrace’s AI-driven approach to securing industrial environments, where conventional security solutions struggle to keep pace with increasing cyber threats.

A milestone for CPS security

It's our opinion that the first-ever Gartner Magic Quadrant for CPS Protection Platforms reflects a growing industry shift toward purpose-built security solutions for critical infrastructure. As organizations integrate IT, OT, and cloud-connected systems, the cyber risk landscape continues to expand. Gartner evaluated 17 vendors based on their Ability to Execute and Completeness of Vision, establishing a benchmark for security leaders looking to enhance cyber resilience in industrial environments.

We believe the Gartner recognition of Darktrace as the only Visionary reaffirms the platform’s ability to proactively defend against cyber risks through AI-driven anomaly detection, autonomous response, and risk-based security strategies. With increasingly sophisticated attacks targeting industrial control systems, organizations need a solution that continuously evolves to defend against both known and unknown threats.

AI-driven security for CPS environments

Securing CPS environments requires an approach that adapts to the dynamic nature of industrial operations. Traditional security tools rely on static signatures and predefined rules, leaving gaps in protection against novel and sophisticated threats. Darktrace / OT takes a different approach, leveraging Self-Learning AI to detect and neutralize threats in real time, even in air-gapped or highly regulated environments.

Darktrace / OT continuously analyzes network behaviors to establish a deep understanding of what is “normal” for each industrial environment. This enables it to autonomously identify deviations that signal potential cyber threats, providing early warning and proactive defense before attacks can disrupt operations. Unlike rule-based security models that require constant manual updates, Darktrace / OT improves with the environment, ensuring long-term resilience against emerging cyber risks.

Bridging the IT-OT security gap

A major challenge for organizations protecting CPS environments is the disconnect between IT and OT security. While IT security has traditionally focused on data

protection and compliance, OT security is driven by operational uptime and safety, leading to siloed security programs that leave critical gaps in visibility and response.

Darktrace / OT eliminates these silos by providing unified visibility across IT, OT, and IoT assets, ensuring that security teams have a complete picture of their attack surface. Its AI-driven approach enables cross-domain threat detection, recognizing risks that move laterally between IT and OT environments. By seamlessly integrating with existing security architectures, Darktrace / OT helps organizations close security gaps without disrupting industrial processes.

Proactive OT risk management and resilience

Beyond detection and response, Darktrace / OT strengthens organizations’ ability to manage cyber risk proactively. By mapping vulnerabilities to real-world attack paths, it prioritizes remediation actions based on actual exploitability and business impact, rather than relying on isolated CVE scores. This risk-based approach enables security teams to focus resources where they matter most, reducing overall exposure to cyber threats.

With autonomous threat response capabilities, Darktrace / OT not only identifies risks but also contains them in real time, preventing attackers from escalating intrusions. Whether mitigating ransomware, insider threats, or sophisticated nation-state attacks, Darktrace / OT ensures that industrial environments remain secure, operational, and resilient, no matter how threats evolve.

AI-powered incident response and SOC automation

Security teams are facing an overwhelming volume of alerts, making it difficult to prioritize threats and respond effectively. Darktrace / OT’s Cyber AI Analyst acts as a force multiplier for security teams by automating threat investigation, alert triage, and response actions. By mimicking the workflow of a human SOC analyst, Cyber AI Analyst provides contextual insights that accelerate incident response and reduce the manual workload on security teams.

With 24/7 autonomous monitoring, Darktrace / OT ensures that threats are continuously detected and investigated in real time. Whether facing ransomware, insider threats, or sophisticated nation-state attacks, organizations can rely on AI-driven security to contain threats before they disrupt operations.

Trusted by customers: Darktrace / OT recognized in Gartner Peer Insights

Source: Gartner Peer Insights (Oct 28th)

Beyond our recognition in the Gartner Magic Quadrant, we feel Darktrace / OT is one of the highest-rated CPS security solutions on Gartner Peer Insights, reflecting strong customer trust and validation. With a 4.9/5 overall rating and the highest "Willingness to Recommend" score among CPS vendors, organizations across critical infrastructure and industrial sectors recognize the impact of our AI-driven security approach. Source: Gartner Peer Insights (Oct 28th)

This strong customer endorsement underscores why leading enterprises trust Darktrace / OT to secure their CPS environments today and in the future.

Redefining the future of CPS security

It's our view that Darktrace’s recognition as the only Visionary in the Gartner Magic Quadrant for CPS Protection Platforms validates its leadership in next-generation industrial security. As cyber threats targeting critical infrastructure continue to rise, organizations must adopt AI-driven security solutions that can adapt, respond, and mitigate risks in real time.

We believe this recognition reinforces our commitment to innovation and our mission to secure the world’s most essential systems. This recognition reinforces our commitment to innovation and our mission to secure the world’s most essential systems.

® Download the full Gartner Magic Quadrant for CPS Protection Platforms

® Request a demo to see Darktrace OT in action.

Gartner, Magic Quadrant for CPS Protection Platforms , Katell Thielemann, Wam Voster, Ruggero Contu 12 February 2025

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant and Peer Insights are a registered trademark, of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

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Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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March 28, 2025

Survey Findings: AI Cybersecurity Priorities and Objectives in 2025

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AI is changing the cybersecurity field, both on the offensive and defensive sides. We surveyed over 1,500 cybersecurity professionals from around the world to uncover their attitudes, understanding, and priorities when it comes to AI cybersecurity in 2025. Our full report, unearthing some telling trends, is available now.  

Download the full report to explore these findings in depth

It is clear that security professionals know their field is changing fast, and that AI will continue to influence those changes. Our survey results show that they are aware that the rise of AI will require them to adopt new tools and learn to use them effectively. Still, they aren’t always certain about how to plan for the future, or what to invest in.

The top priorities of security stakeholders for improving their defenses against AI-powered threats include augmenting their existing tool stacks with AI-powered solutions and improving integration among their security tools.

Figure 1: Year-over-year changes to the priorities of securitystakeholders.

Increasing cybersecurity staff

As was also the case last year, security stakeholders are less interested in hiring additional staff than in adding new AI-powered tools onto their existing security stacks, with only with 11% (and only 8% of executives) planning to increase cybersecurity staff in 2025.

This suggests that leaders are looking for new methods to overcome talent resource shortages.

Adding AI-powered security tools to supplement existing solutions

Executives are particularly enthusiastic about adopting AI-driven tools. Within that goal, there is consensus about the qualities cyber professionals are looking for when purchasing new security capabilities or replacing existing products.

  • 87% of survey respondents prefer solutions that are part of a broader platform over individual point products

These results are similar to last year’s, where again, almost nine out of ten agreed that a platform-oriented security solution was more effective at stopping cyber threats than a collection of individual products.

  • 88% of survey respondents agree that the use of AI within the security stack is critical to freeing up time for security teams to become more proactive, compared to reactive

AI itself can contribute to this shift from reactive to proactive security, improving risk prioritization and automating preventative strategies like Attack Surface Management (ASM) and proactive exposure management.

  • 84% of survey respondents prefer defensive AI solutions that do not require the organization’s data to be shared externally

This preference may reflect increasing attention to the data privacy and security risks posed by generative AI (gen AI) adoption. It may also reflect growing awareness of data residency requirements and other restrictions that regulators are imposing.

Improving cybersecurity awareness training for end users

Based on the survey results, practitioners in SecOps are more interested in improving security awareness training.

This goal is not necessarily mutually exclusive from the addition of AI tools. For example, teams can leverage AI to build more effective security awareness training programs, and as gen AI tools are adopted, users will need to be taught about data privacy and associated security risks.

Looking towards the future

One conclusion we can draw from the attitudinal shifts from last year’s survey to this year’s: while hiring more security staff might be a nice-to-have, implementing AI-powered tools so that existing employees can work smarter is increasingly viewed as a must-have.

However, trending goals are not just about managing resources, whether headcount or AI investments, to keep up with workloads. Existing end users must also be trained to follow safe practices while using established and newly adopted tools.

Security professionals, including executives, SecOps, and every role in between, continue to shift their identified challenges and priorities as they gear up for the coming year in the Era of AI.

State of AI report

Download the full report to explore these findings in depth

The full report for Darktrace’s State of AI Cybersecurity is out now. Download the paper to dig deeper into these trends, and see how results differ by industry, region, organization size, and job title.  

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