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November 9, 2023

Using Darktrace for Threat Hunting

Read about effective threat hunting techniques with Darktrace, focusing on identifying vulnerabilities and improving your security measures.
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
Brianna Leddy
Director of Analyst Operations
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09
Nov 2023

What is Threat Hunting?

Threat Hunting is a technique to identify adversaries within an organization that go undetected by traditional security tools.

While a traditional, reactive approach to cyber security often involves automated alerts received and investigated by a security team, threat hunting takes a proactive approach to seek out potential threats and vulnerabilities before they escalate into full-blown security incidents. The benefits of hunting include identifying hidden threats, reducing the dwell time of attackers, and enhancing overall detection and response capabilities.

Threat Hunting Methodology

There are many different methodologies and frameworks for threat hunting, including the Pyramid of Pain, the Sqrrl Hunting Loop, and the MITRE ATT&CK Framework.  While there is not one gold standard on how to conduct threat hunts, the typical process can be broken down into several key steps:

Planning and Hypothesis Creation: Define the scope and objective of the threat hunt. Identify potential targets and predict activity that might be taking place.

Data Collection: Refining data collection methods and gathering data from various sources, including logs, network traffic, and endpoint data.

Data Processing: Data that has been collected needs to be processed to generate information.

Data Analysis: Processed data can then be analyzed for anomalies, indicators of compromise (IoCs), or patterns of suspicious behavior.

Threat Identification: Based on the analysis, threat hunters may identify potential threats or security incidents.

Response: Taking action to mitigate or eradicate identified threats if any.

Documentation and Dissemination: It is important to record any findings or actions taken during the threat hunting process to serve as lessons learned for future reference. Additionally, any new threats or tactics, techniques, and procedures (TTPs) discovered may be shared with the cyber threat intelligence team or the wider community.

Building a Threat Hunting Program

For organizations looking to implement threat hunting as part of their cyber security program, they will need both a data collection source and human analysts as threat hunters.

Data collection and analysis may often be performed through existing security tools including SIEM systems, Network Traffic Analysis tools, endpoint agents, and system logs. On the human side, experienced threat hunters may be hired into an organization, or existing SOC analysts may be upskilled to perform threat hunts.

Leveraging AI security tools such as Darktrace can help to lower the bar in building a threat hunting program, both in analysis of the data and in assisting humans in their investigations.

Threat Hunting in Darktrace

To illustrate the benefits of leveraging Darktrace in threat hunting, we can walk through an example hunt following the key steps outlined above.

Planning and Hypothesis Creation

The initial hypothesis used in defining the scope of a threat hunt can come from several sources: threat intelligence feeds, the threat hunter’s own experience, or an anomaly detection that has been highlighted by Darktrace.

In this case, let’s imagine that this hunt is focused on a recent campaign by an Advanced Persistent Threat (APT). Threat intel has provided known file hashes, Command and Control (C2) IP addresses and domains, and MITRE techniques used by the attacker. The goal is to determine whether any indicators of this threat are present in the organization’s environment.

Data Collection and Data Processing

Darktrace can be deployed to cover an organization’s entire digital estate, including passive network traffic monitoring, cloud environments, and SaaS applications. Self-Learning AI is applied to the raw data to learn normal patterns of life for a specific environment and to highlight deviations from normal that might represent a threat. This data gives threat hunters a starting point in analyzing logs, meta-data, and anomaly detections.

Data Analysis

In the data analysis phase, threat hunters can use the Darktrace platform to search for the IoCs and TTPs identified during planning.

When searching for IoCs such as IP addresses or domain names, hunters can query the environment through the Omnisearch bar in the Darktrace Threat Visualizer. This search can provide a summary of all devices or users contacting a suspicious endpoint. From here the hunters can quickly pivot to identify surrounding activity from the source device.

Figure 1: Search for twitter[.]com (now known as X) as a potential indicator of compromise

Alternately, Darktrace Advanced Search can be used to search for these IoCs, but it also supports queries for file hashes or more advanced searches based on ports, protocols, data volumes, etc.

Figure 2: Advanced Search query for connections on port 3389 lasting longer than 60 seconds

While searching for known suspicious domains and IP addresses is straightforward, the real strength of Darktrace lies in the ability to highlight deviations from a device’s ‘normal’ pattern of life. Darktrace has many built-in behavioral models designed to detect common adversary TTPs, all mapped to the MITRE ATT&CK Framework.

In the context of our threat hunt, we know that our target APT uses the Remote Desktop Protocol (RDP) to move laterally within a compromised network, specifically leveraging MITRE technique T1021.001. As each Darktrace model is mapped to MITRE, the threat hunter can search and find specific detection models that may be of interest, in this case the model ‘Anomalous Connection / Unusual Internal Remote Desktop’. From here they can view any devices that may have triggered this model, indicating possible attacker activity.

Figure 3: MITRE Mapping details in the Darktrace Model Editor

Threat hunters can also search more widely for any detections within a specific MITRE tactic through filters found on the Darktrace Threat Tray.

Figure 4: Search for the Lateral Movement MITRE Tactic on the model breach threat tray

Threat Identification

Once a threat hunter has identified connections, model breaches, or anomalies during the analysis phase, they can begin to conduct further investigation to determine if this may represent a security incident.

Threat hunters can use Darktrace to perform deeper analysis through generating packet captures, visualizing surrounding network traffic, and utilizing features like the VirusTotal lookup to consult open-source intelligence (OSINT).

Another powerful tool to augment the hunter’s investigation is the Darktrace Cyber AI Analyst, which assists human teams in the investigation and correlation of behaviors to identify threats. Cyber AI Analyst automatically launches an initial triage of every model breach in the Darktrace platform, but threat hunters can also leverage manual investigations to gain additional context on their findings.

For example, say that an unusual RDP connection of interest was identified through Advanced Search. The hunter can pivot back to the Threat Visualizer and launch an AI Analyst investigation for the source device at the time of the connection. The resulting investigation may provide the hunter with additional suspicious behavior observed around that time, without the need for manual log analysis.

Figure 5: Manual Cyber AI Analyst investigations

Response

If a threat is detected within Darktrace and confirmed by the threat hunter, Darktrace's Autonomous Response can be leveraged to take either autonomous or manual action to contain the threat. This provides the security team with additional time to conduct further investigation, pull forensics, and remediate the threat. This process can be further supported through the bespoke, AI-generated playbooks offered by Darktrace / Incident Readiness & Recovery, allowing an efficient recovery back to normal.

Figure 6: Example of a manual RESPOND action used to block suspicious connectivity on port 3389 to contain possible lateral movement

Documentation and Dissemination

An important final step is to document the threat hunting process and use the results to better improve automated security alerting and response. In Darktrace, reporting can be generated through the Cyber AI Analyst, Advanced Search exports, and model breach details to support documentation.

To improve existing alerting through Darktrace, this may mean creating a new detection model or increasing the priority of existing detections to ensure that these are escalated to the security team in the future. The Darktrace model editor provides users with full visibility into models and allows the creation of custom detections based on use cases or business requirements.

Figure 7: The Darktrace Model Editor showing the Breach Logic configuration

Conclusions

Proactive threat hunting is an important part of a cyber security approach to identify hidden threats, reduce dwell time, and improve incident response. Darktrace’s Self-Learning AI provides a powerful tool for identifying attacker TTPs and augmenting human threat hunters in their process. Utilizing the Darktrace platform, threat hunters can significantly reduce the time required to complete their hunts and mitigate identified threats.

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  • Identity-based attacks: How attackers are bypassing traditional defenses
  • Zero-day exploitation: The rise of previously unknown vulnerabilities
  • AI-driven threats: How adversaries are leveraging AI to outmaneuver security controls

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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
Brianna Leddy
Director of Analyst Operations

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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.

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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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About the author
Ashanka Iddya
Senior Director, Product Marketing

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July 17, 2025

Forensics or Fauxrensics: Five Core Capabilities for Cloud Forensics and Incident Response

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The speed and scale at which new cloud resources can be spun up has resulted in uncontrolled deployments, misconfigurations, and security risks. It has had security teams racing to secure their business’ rapid migration from traditional on-premises environments to the cloud.

While many organizations have successfully extended their prevention and detection capabilities to the cloud, they are now experiencing another major gap: forensics and incident response.

Once something bad has been identified, understanding its true scope and impact is nearly impossible at times. The proliferation of cloud resources across a multitude of cloud providers, and the addition of container and serverless capabilities all add to the complexities. It’s clear that organizations need a better way to manage cloud incident response.

Security teams are looking to move past their homegrown solutions and open-source tools to incorporate real cloud forensics capabilities. However, with the increased buzz around cloud forensics, it can be challenging to decipher what is real cloud forensics, and what is “fauxrensics.”

This blog covers the five core capabilities that security teams should consider when evaluating a cloud forensics and incident response solution.

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1. Depth of data

There have been many conversations among the security community about whether cloud forensics is just log analysis. The reality, however, is that cloud forensics necessitates access to a robust dataset that extends far beyond traditional log data sources.

While logs provide valuable insights, a forensics investigation demands a deeper understanding derived from multiple data sources, including disk, network, and memory, within the cloud infrastructure. Full disk analysis complements log analysis, offering crucial context for identifying the root cause and scope of an incident.

For instance, when investigating an incident involving a Kubernetes cluster running on an EC2 instance, access to bash history can provide insights into the commands executed by attackers on the affected instance, which would not be available through cloud logs alone.

Having all of the evidence in one place is also a capability that can significantly streamline investigations, unifying your evidence be it disk images, memory captures or cloud logs, into a single timeline allowing security teams to reconstruct an attacks origin, path and impact far more easily. Multi–cloud environments also require platforms that can support aggregating data from many providers and services into one place. Doing this enables more holistic investigations and reduces security blind spots.

There is also the importance of collecting data from ephemeral resources in modern cloud and containerized environments. Critical evidence can be lost in seconds as resources are constantly spinning up and down, so having the ability to capture this data before its gone can be a huge advantage to security teams, rather than having to figure out what happened after the affected service is long gone.

darktrace / cloud, cado, cloud logs, ost, and memory information. value of cloud combined analysis

2. Chain of custody

Chain of custody is extremely critical in the context of legal proceedings and is an essential component of forensics and incident response. However, chain of custody in the cloud can be extremely complex with the number of people who have access and the rise of multi-cloud environments.

In the cloud, maintaining a reliable chain of custody becomes even more complex than it already is, due to having to account for multiple access points, service providers and third parties. Having automated evidence tracking is a must. It means that all actions are logged, from collection to storage to access. Automation also minimizes the chance of human error, reducing the risk of mistakes or gaps in evidence handling, especially in high pressure fast moving investigations.

The ability to preserve unaltered copies of forensic evidence in a secure manner is required to ensure integrity throughout an investigation. It is not just a technical concern, its a legal one, ensuring that your evidence handling is documented and time stamped allows it to stand up to court or regulatory review.

Real cloud forensics platforms should autonomously handle chain of custody in the background, recording and safeguarding evidence without human intervention.

3. Automated collection and isolation

When malicious activity is detected, the speed at which security teams can determine root cause and scope is essential to reducing Mean Time to Response (MTTR).

Automated forensic data collection and system isolation ensures that evidence is collected and compromised resources are isolated at the first sign of malicious activity. This can often be before an attacker has had the change to move latterly or cover their tracks. This enables security teams to prevent potential damage and spread while a deeper-dive forensics investigation takes place. This method also ensures critical incident evidence residing in ephemeral environments is preserved in the event it is needed for an investigation. This evidence may only exist for minutes, leaving no time for a human analyst to capture it.

Cloud forensics and incident response platforms should offer the ability to natively integrate with incident detection and alerting systems and/or built-in product automation rules to trigger evidence capture and resource isolation.

4. Ease of use

Security teams shouldn’t require deep cloud or incident response knowledge to perform forensic investigations of cloud resources. They already have enough on their plates.

While traditional forensics tools and approaches have made investigation and response extremely tedious and complex, modern forensics platforms prioritize usability at their core, and leverage automation to drastically simplify the end-to-end incident response process, even when an incident spans multiple Cloud Service Providers (CSPs).

Useability is a core requirement for any modern forensics platform. Security teams should not need to have indepth knowledge of every system and resource in a given estate. Workflows, automation and guidance should make it possible for an analyst to investigate whatever resource they need to.

Unifying the workflow across multiple clouds can also save security teams a huge amount of time and resources. Investigations can often span multiple CSP’s. A good security platform should provide a single place to search, correlate and analyze evidence across all environments.

Offering features such as cross cloud support, data enrichment, a single timeline view, saved search, and faceted search can help advanced analysts achieve greater efficiency, and novice analysts are able to participate in more complex investigations.

5. Incident preparedness

Incident response shouldn't just be reactive. Modern security teams need to regularly test their ability to acquire new evidence, triage assets and respond to threats across both new and existing resources, ensuring readiness even in the rapidly changing environments of the cloud.  Having the ability to continuously assess your incident response and forensics workflows enables you to rapidly improve your processes and identify and mitigate any gaps identified that could prevent the organization from being able to effectively respond to potential threats.

Real forensics platforms deliver features that enable security teams to prepare extensively and understand their shortcomings before they are in the heat of an incident. For example, cloud forensics platforms can provide the ability to:

  • Run readiness checks and see readiness trends over time
  • Identify and mitigate issues that could prevent rapid investigation and response
  • Ensure the correct logging, management agents, and other cloud-native tools are appropriately configured and operational
  • Ensure that data gathered during an investigation can be decrypted
  • Verify that permissions are aligned with best practices and are capable of supporting incident response efforts

Cloud forensics with Darktrace

Darktrace delivers a proactive approach to cyber resilience in a single cybersecurity platform, including cloud coverage. Darktrace / CLOUD is a real time Cloud Detection and Response (CDR) solution built with advanced AI to make cloud security accessible to all security teams and SOCs. By using multiple machine learning techniques, Darktrace brings unprecedented visibility, threat detection, investigation, and incident response to hybrid and multi-cloud environments.

Darktrace’s cloud offerings have been bolstered with the acquisition of Cado Security Ltd., which enables security teams to gain immediate access to forensic-level data in multi-cloud, container, serverless, SaaS, and on-premises environments.

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About the author
Calum Hall
Technical Content Researcher
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