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December 12, 2022

ML Integration for Third-Party EDR Alerts

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

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.
Written by
Max Heinemeyer
Global Field CISO

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July 23, 2025

Global Telecom Provider: Powering and Protecting the World's Data Giants

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This global leader plays a critical role in keeping the world connected. The company works with some of the largest and most influential public and private organizations in the world to enable ultra-fast data transmission.

Safeguarding the systems that keep the world connected

Standing at the forefront of global connectivity, this industry leader designs and manages large-scale communications systems that power the world’s most data-intensive enterprises – including social media giants, hyperscale cloud providers, and major data center operators. Given the scale, confidentiality, and sensitivity of the systems and data it helps transport, the company faces complex cybersecurity challenges.

Protecting sensitive customer data

Most of the organization’s projects are custom-designed and highly proprietary, making data privacy and Intellectual Property (IP) protection critical to maintaining trust and confidentiality with customers. In an industry where every competitor knows the landscape intimately, any loss of data could cause significant damage.

International security implications

The company faces a broad range of advanced cyber threats – from corporate espionage and supply chain risks to cyber-physical attacks on critical infrastructure. Its international footprint adds complexity, including cross-border regulatory compliance. A successful attack could disrupt business, compromise IP, or trigger wider consequences like disruptions to international data transfers and other critical services.

The global leader works closely with communities to anticipate threats that could impact the global communications network at large.

In this environment, cybersecurity is a foundation for international trust,” said the organization’s CISO.

Building a resilient cybersecurity strategy from the ground up

The CISO had the rare opportunity to build the IT and cybersecurity infrastructure from scratch. "Initially, we bought what everyone else buys,” referencing the traditional mix of firewalls, routers, and antivirus tools. “But I knew we needed to do more.”

Self-Learning AI – “the missing piece”

With solid perimeter defenses in place, the security team sought deeper protection inside the network. Darktrace’s Self-Learning AI stood out. “Unlike other solutions, Darktrace’s AI looks beyond known threat signatures, learning what’s normal for our environment and flagging what’s not. That was the missing piece – something that could help us even when everything else failed.”

A solution and partnership that delivered

The CISO said he appreciated the ability to observe Darktrace in action before full deployment, noting that the Darktrace team was there every step of the way, providing guidance and expertise to ensure he got the most out of his investment.

Partnership was especially valuable given the company’s explosive 400% growth over the last six years. As resources were stretched and priorities shifted, “Darktrace remained patient and responsive. We’re slow and methodical, but the Darktrace support team was phenomenal, never losing momentum and earning our trust.”

A unified cybersecurity ecosystem

Today, the global leader is using the Darktrace ActiveAI Security Platform™ as a core part of its layered defense strategy, including:

The CISO appreciates how, as a unified cybersecurity platform, Darktrace has an intuitive user interface, which makes it easier for his team to investigate alerts visually, even without deep technical expertise.

Advancing defenses while impacting the bottom line

A 24/7 “safety net”

The fact that this company has never been hacked is the clearest proof it made the right decision with Darktrace, said the CISO. Initially rolled out in Human Confirmation Mode, meaning it would not take autonomous action without explicit approval from the security team, Darktrace immediately uncovered threats and anomalies that other tools had missed.

Darktrace acts as a must-have safety net—ready to step in when other tools fall short,” said the CISO.

From monitoring internal behavior and identifying unusual attack patterns, to autonomously neutralizing threats after hours, the platform provides peace of mind in a high-stakes industry. “Darktrace is my dark horse – the thing I have in my back pocket if everything else fails. It’s here to save the day, save my company, and maybe even save my career.”

Autonomous capabilities free up time for skilled analysts

Darktrace’s AI-powered detection and response capabilities are deeply embedded in the team’s day-to-day operations, autonomously investigating and responding to the majority of potential threats. Cyber AI Analyst conducted a total of 2,776 total investigations within three months, averaging just 12 minutes to autonomously investigate an incident. Of those 2,776 investigations, Darktrace resolved 2,671 (96%) autonomously and escalated only 105 (4%) to analysts. Darktrace has dramatically reduced alert fatigue and freed up analysts to focus on what really matters, saving the security team 486 analyst hours on investigations within a 20-day period.

From noise to actionable insight

Darktrace delivers meaningful data and meaningful alerts. “If Darktrace escalates an incident, we drop everything and work on that. We trust in Darktrace.” When analysts do need to investigate an incident, Darktrace’s forensic logs and guided remediation suggestions have slashed the time analysts spend on investigations by four to five times.

Stronger security. Lower cost.

The CISO says, “Darktrace is a money-saver for our organization, making continued investments an easy sell to the CEO and the board.”  When he found himself down a resource after a member of the security team left the organization, the CISO turned to Darktrace Managed Threat Detection and Response services for 24/7 expert support. “It was a no brainer. We got better coverage, higher skill levels, and around-the-clock support – all for less than what we would pay to employ a single analyst.”

Scaling securely into the future

Securing networks in motion  

The organization is preparing to scale both its operations and security posture across existing distributed, mobile and deployable communications networks that historically have been disconnected. Some of these networks are in constant motion and operating in some of the world’s most volatile regions. “Darktrace will act as an autonomous defender, monitoring for anomalous behavior and intervening, when necessary, especially during those dangerous times when an asset ‘goes dark’ and becomes disconnected from the broader network,” said the CISO.

Applying AI strategically

As the organization continues to evaluate where and how to apply AI, its emphasis will be on technologies that can act independently to contain threats – especially in environments where human response may be delayed. “It’s about using the right kind of AI for the right challenge. That’s why we’re investing in Darktrace, with tools that can adapt and learn even in isolation and provide real-time protection wherever we operate.”

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July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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AI adoption in cybersecurity: Beyond the hype

Security operations today face a paradox. On one hand, artificial intelligence (AI) promises sweeping transformation from automating routine tasks to augmenting threat detection and response. On the other hand, security leaders are under immense pressure to separate meaningful innovation from vendor hype.

To help CISOs and security teams navigate this landscape, we’ve developed the most in-depth and actionable AI Maturity Model in the industry. Built in collaboration with AI and cybersecurity experts, this framework provides a structured path to understanding, measuring, and advancing AI adoption across the security lifecycle.

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

In our conversations and research with security leaders, a recurring theme has emerged:

There’s no shortage of AI solutions, but there is a shortage of clarity and understanding of AI uses cases.

In fact, Gartner estimates that “by 2027, over 40% of Agentic AI projects will be canceled due to escalating costs, unclear business value, or inadequate risk controls. Teams are experimenting, but many aren’t seeing meaningful outcomes. The need for a standardized way to evaluate progress and make informed investments has never been greater.

That’s why we created the AI Security Maturity Model, a strategic framework that:

  • Defines five clear levels of AI maturity, from manual processes (L0) to full AI Delegation (L4)
  • Delineating the outcomes derived between Agentic GenAI and Specialized AI Agent Systems
  • Applies across core functions such as risk management, threat detection, alert triage, and incident response
  • Links AI maturity to real-world outcomes like reduced risk, improved efficiency, and scalable operations

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How is maturity assessed in this model?

The AI Maturity Model for Cybersecurity is grounded in operational insights from nearly 10,000 global deployments of Darktrace's Self-Learning AI and Cyber AI Analyst. Rather than relying on abstract theory or vendor benchmarks, the model reflects what security teams are actually doing, where AI is being adopted, how it's being used, and what outcomes it’s delivering.

This real-world foundation allows the model to offer a practical, experience-based view of AI maturity. It helps teams assess their current state and identify realistic next steps based on how organizations like theirs are evolving.

Why Darktrace?

AI has been central to Darktrace’s mission since its inception in 2013, not just as a feature, but the foundation. With over a decade of experience building and deploying AI in real-world security environments, we’ve learned where it works, where it doesn’t, and how to get the most value from it. This model reflects that insight, helping security leaders find the right path forward for their people, processes, and tools

Security teams today are asking big, important questions:

  • What should we actually use AI for?
  • How are other teams using it — and what’s working?
  • What are vendors offering, and what’s just hype?
  • Will AI ever replace people in the SOC?

These questions are valid, and they’re not always easy to answer. That’s why we created this model: to help security leaders move past buzzwords and build a clear, realistic plan for applying AI across the SOC.

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

L0 – Manual Operations: Processes are mostly manual with limited automation of some tasks.

L1 – Automation Rules: Manually maintained or externally-sourced automation rules and logic are used wherever possible.

L2 – AI Assistance: AI assists research but is not trusted to make good decisions. This includes GenAI agents requiring manual oversight for errors.

L3 – AI Collaboration: Specialized cybersecurity AI agent systems  with business technology context are trusted with specific tasks and decisions. GenAI has limited uses where errors are acceptable.

L4 – AI Delegation: Specialized AI agent systems with far wider business operations and impact context perform most cybersecurity tasks and decisions independently, with only high-level oversight needed.

Each level reflects a shift, not only in technology, but in people and processes. As AI matures, analysts evolve from executors to strategic overseers.

Strategic benefits for security leaders

The maturity model isn’t just about technology adoption it’s about aligning AI investments with measurable operational outcomes. Here’s what it enables:

SOC fatigue is real, and AI can help

Most teams still struggle with alert volume, investigation delays, and reactive processes. AI adoption is inconsistent and often siloed. When integrated well, AI can make a meaningful difference in making security teams more effective

GenAI is error prone, requiring strong human oversight

While there is a lot of hype around GenAI agentic systems, teams will need to account for inaccuracy and hallucination in Agentic GenAI systems.

AI’s real value lies in progression

The biggest gains don’t come from isolated use cases, but from integrating AI across the lifecycle, from preparation through detection to containment and recovery.

Trust and oversight are key initially but evolves in later levels

Early-stage adoption keeps humans fully in control. By L3 and L4, AI systems act independently within defined bounds, freeing humans for strategic oversight.

People’s roles shift meaningfully

As AI matures, analyst roles consolidate and elevate from labor intensive task execution to high-value decision-making, focusing on critical, high business impact activities, improving processes and AI governance.

Outcome, not hype, defines maturity

AI maturity isn’t about tech presence, it’s about measurable impact on risk reduction, response time, and operational resilience.

[related-resource]

Outcomes across the AI Security Maturity Model

The Security Organization experiences an evolution of cybersecurity outcomes as teams progress from manual operations to AI delegation. Each level represents a step-change in efficiency, accuracy, and strategic value.

L0 – Manual Operations

At this stage, analysts manually handle triage, investigation, patching, and reporting manually using basic, non-automated tools. The result is reactive, labor-intensive operations where most alerts go uninvestigated and risk management remains inconsistent.

L1 – Automation Rules

At this stage, analysts manage rule-based automation tools like SOAR and XDR, which offer some efficiency gains but still require constant tuning. Operations remain constrained by human bandwidth and predefined workflows.

L2 – AI Assistance

At this stage, AI assists with research, summarization, and triage, reducing analyst workload but requiring close oversight due to potential errors. Detection improves, but trust in autonomous decision-making remains limited.

L3 – AI Collaboration

At this stage, AI performs full investigations and recommends actions, while analysts focus on high-risk decisions and refining detection strategies. Purpose-built agentic AI systems with business context are trusted with specific tasks, improving precision and prioritization.

L4 – AI Delegation

At this stage, Specialized AI Agent Systems performs most security tasks independently at machine speed, while human teams provide high-level strategic oversight. This means the highest time and effort commitment activities by the human security team is focused on proactive activities while AI handles routine cybersecurity tasks

Specialized AI Agent Systems operate with deep business context including impact context to drive fast, effective decisions.

Join the webinar

Get a look at the minds shaping this model by joining our upcoming webinar using this link. We’ll walk through real use cases, share lessons learned from the field, and show how security teams are navigating the path to operational AI safely, strategically, and successfully.

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