December 15, 2023

How Darktrace Halted A DarkGate in MS Teams

Discover how Darktrace thwarted DarkGate malware in Microsoft Teams. Stay informed on the latest cybersecurity measures and protect your business.

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

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

Natalia Sánchez Rocafort

Cyber Security Analyst

Dec 2023

Securing Microsoft Teams and SharePoint

Given the prevalence of the Microsoft Teams and Microsoft SharePoint platforms in the workplace in recent years, it is essential that organizations stay vigilant to the threat posed by applications vital to hybrid and remote work and prioritize the security and cyber hygiene of these services. For just as the use of these platforms has increased exponentially with the rise of remote and hybrid working, so too has the malicious use of them to deliver malware to unassuming users.

Researchers across the threat landscape have begun to observe these legitimate services being leveraged by malicious actors as an initial access method. Microsoft Teams can easily be exploited to send targeted phishing messages to individuals within an organization, while appearing legitimate and safe. Although the exact contents of these messages may vary, the messages frequently use social engineering techniques to lure users to click on a SharePoint link embedded into the message. Interacting with the malicious link will then download a payload [1].

Darktrace observed one such malicious attempt to use Microsoft Teams and SharePoint in September 2023, when a device was observed downloading DarkGate, a commercial trojan that is known to deploy other strains of malware, also referred to as a commodity loader [2], after clicking on SharePoint link. Fortunately for the customer, Darktrace’s suite of products was perfectly poised to identify the initial signs of suspicious activity and Darktrace RESPOND™ was able to immediately halt the advancement of the attack.

DarkGate Attack Overview

On September 8, 2023, Darktrace DETECT™ observed around 30 internal devices on a customer network making unusual SSL connections to an external SharePoint site which contained the name of a person, 'XXXXXXXX-my.sharepoint[.]com' (107.136[.]8, 13.107.138[.]8). The organization did not have any employees who went by this name and prior to this activity, no internal devices had been seen contacting the endpoint.

At first glance, this initial attack vector would have appeared subtle and seemingly trustworthy to users. Malicious actors likely sent various users a phishing message via Microsoft Teams that contained the spoofed SharePoint link to the personalized SharePoint link ''XXXXXXXX-my.sharepoint[.]com'.

Figure 1: Advanced Search query showing a sudden spike in connections to ''XXXXXXXX -my.sharepoint[.]com'.

Darktrace observed around 10 devices downloading approximately 1 MB of data during their connections to the Sharepoint endpoint. Darktrace DETECT observed some of the devices making subsequent HTTP GET requests to a range of anomalous URIs. The devices utilized multiple user-agents for these connections, including ‘curl’, a command line tool that allows individuals to request and transfer data from a specific URL. The connections were made to the IP 5.188.87[.]58, an endpoint that has been flagged as an indicator of compromise (IoC) for DarkGate malware by multiple open-source intelligence (OSINT) sources [3], commonly associated with HTTP GET requests:

GET request over port 2351 with the User-Agent header 'Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5)' and the target URI '/bfyxraav' to 5.188.87[.]58
GET request over port 2351 with the user-agent header 'curl' and the target URI '/' to 5.188.87[.]58
GET request over port 2351 with the user-agent header 'curl/8.0.1' and the target URI '/msibfyxraav' to 5.188.87[.]58

The HTTP GET requests made with the user-agent header 'curl' and the target URI '/' to 5.188.87[.]58 were responded to with a filename called 'Autoit3.exe'. The other requests received script files with names ending in '.au3, such as 'xkwtvq.au3', 'otxynh.au3', and 'dcthbq.au3'. DarkGate malware has been known to make use of legitimate AutoIt files, and typically runs multiple AutoIt scripts (‘.au3’) [4].

Following these unusual file downloads, the devices proceeded to make hundreds of HTTP POST requests to the target URI '/' using the user-agent header 'Mozilla/4.0 (compatible; Synapse)' to 5.188.87[.]58. The contents of these requests, along with the contents of the responses, appear to be heavily obfuscated.

Figure 2: Example of obfuscated response, as shown in a packet capture downloaded from Darktrace.

While Microsoft’s Safe Attachments and Safe Links settings were unable to detect this camouflaged malicious activity, Darktrace DETECT observed the unusual over-the-network connectivity that occurred. While Darktrace DETECT identified multiple internal devices engaging in this anomalous behavior throughout the course of the compromise, the activity observed on one device in particular best showcases the overall kill chain of this attack.

The device in question was observed using two different user agents (curl/8.0.1 and Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5)) when connecting to the endpoint 5.188.87[.]58 and target URI ‘/bfyxraav’. Additionally, Darktrace DETECT recognized that it was unusual for this device to be making these HTTP connections via destination port 2351.

As a result, Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the suspicious activity and was able to connect the unusual external connections together, viewing them as one beaconing incident as opposed to isolated series of connections.

Figure 3: Cyber AI Analyst investigation summarizing the unusual repeated connections made to 5.188.87[.]58 via destination port 2351.

Darktrace then observed the device downloading the ‘Autoit3.exe’ file. Darktrace RESPOND took swift mitigative action by blocking similar connections to this endpoint, preventing the device from downloading any additional suspicious files.

Figure 4: Suspicious ‘Autoit3.exe’ downloaded by the source device from the malicious external endpoint.

Just one millisecond later, Darktrace observed the device making suspicious HTTP GET requests to URIs including ‘/msibfyxraav’. Darktrace recognized that the device had carried out several suspicious actions within a relatively short period of time, breaching multiple DETECT models, indicating that it may have been compromised. As a result, RESPOND took action against the offending device by preventing it from communicating externally [blocking all outbound connections] for a period of one hour, allowing the customer’s security team precious time to address the issue.

It should be noted that, at this point, had the customer subscribed to Darktrace’s Proactive Threat Notification (PTN) service, the Darktrace Security Operations Center (SOC) would have investigated these incidents in greater detail, and likely would have sent a notification directly to the customer to inform them of the suspicious activity.

Additionally, AI Analyst collated various distinct events and suggested that these stages were linked as part of an attack. This type of augmented understanding of events calculated at machine speed is extremely valuable since it likely would have taken a human analyst hours to link all the facets of the incident together.

Figure 5: AI Analyst investigation showcasing the use of the ‘curl’ user agent to connect to the target URI ‘/msibfyxraav’.

Figure 6: Darktrace RESPOND moved to mitigate any following connections by blocking all outgoing traffic for 1 hour.

Following this, an automated investigation was launched by Microsoft Defender for Endpoint. Darktrace is designed to coordinate with multiple third-party security tools, allowing for information on ongoing incidents to be seamlessly exchanged between Darktrace and other security tools. In this instance, Microsoft Defender identified a ‘low severity’ incident on the device, this automatically triggered a corresponding alert within DETECT, presented on the Darktrace Threat Visuallizer.

The described activity occurred within milliseconds. At each step of the attack, Darktrace RESPOND took action either by enforcing expected patterns of life [normality] on the affected device, blocking connections to suspicious endpoints for a specified amount of time, and/or blocking all outgoing traffic from the device. All the relevant activity was detected and promptly stopped for this device, and other compromised devices, thus containing the compromise and providing the security team invaluable remediation time.

Figure 7: Overview of the compromise activity, all of which took place within a matter of miliseconds.

Darktrace identified similar activity on other devices in this customer’s network, as well as across Darktrace’s fleet around the same time in early September.

On a different customer environment, Darktrace DETECT observed more than 25 ‘.au3’ files being downloaded; this activity can be seen in Figure 9.

Figure 8: High volume of file downloads following GET request and 'curl' commands.

Figure 9 provides more details of this activity, including the source and destination IP addresses (5.188.87[.]58), the destination port, the HTTP method used and the MIME/content-type of the file

Figure 9: Additional information of the anomalous connections.

A compromised server in another customer deployment was seen establishing unusual connections to the external IP address 80.66.88[.]145 – an endpoint that has been associated with DarkGate by OSINT sources [5]. This activity was identified by Darktrace/DETECT as a new connection for the device via an unusual destination port, 2840. As the device in question was a critical server, Darktrace DETECT treated it with suspicion and generated an ‘Anomalous External Activity from Critical Network Device’ model breach.

Figure 10: Model breach and model breach event log for suspicious connections to additional endpoint.

Conclusion

While Microsoft Teams and SharePoint are extremely prominent tools that are essential to the business operations of many organizations, they can also be used to compromise via living off the land, even at initial intrusion. Any Microsoft Teams user within a corporate setting could be targeted by a malicious actor, as such SharePoint links from unknown senders should always be treated with caution and should not automatically be considered as secure or legitimate, even when operating within legitimate Microsoft infrastructure.

Malicious actors can leverage these commonly used platforms as a means to carry out their cyber-attacks, therefore organizations must take appropriate measures to protect and secure their digital environments. As demonstrated here, threat actors can attempt to deploy malware, like DarkGate, by targeting users with spoofed Microsoft Teams messages. By masking malicious links as legitimate SharePoint links, these attempts can easily convince targets and bypass traditional security tools and even Microsoft’s own Safe Links and Safe Attachments security capabilities.

When the chain of events of an attack escalates within milliseconds, organizations must rely on AI-driven tools that can quickly identify and automatically respond to suspicious events without latency. As such, the value of Darktrace DETECT and Darktrace RESPOND cannot be overstated. Given the efficacy and efficiency of Darktrace’s detection and autonomous response capabilities, a more severe network compromise in the form of the DarkGate commodity loader was ultimately averted.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Zoe Tilsiter.

Appendices

Darktrace DETECT Model Detections

[Model Breach: Device / Initial Breach Chain Compromise 100% –– Breach URI: /#modelbreach/114039 ] (Enhanced Monitoring)· [Model Breach: Device / Initial Breach Chain Compromise 100% –– Breach URI: /#modelbreach/114124 ] (Enhanced Monitoring)
[Model Breach: Device / New User Agent and New IP 62% –– Breach URI: /#modelbreach/114030 ]
[Model Breach: Anomalous Connection / Application Protocol on Uncommon Port 46% –– Breach URI: /#modelbreach/114031 ]
[Model Breach: Anomalous Connection / New User Agent to IP Without Hostname 62% –– Breach URI: /#modelbreach/114032 ]
[Model Breach: Device / New User Agent 32% –– Breach URI: /#modelbreach/114035 ]
[Model Breach: Device / Three Or More New User Agents 31% –– Breach URI: /#modelbreach/114036 ]
[Model Breach: Anomalous Server Activity / Anomalous External Activity from Critical Network Device 62% –– Breach URI: /#modelbreach/612173 ]
[Model Breach: Anomalous File / EXE from Rare External Location 61% –– Breach URI: /#modelbreach/114037 ]
[Model Breach: Anomalous Connection / Multiple Connections to New External TCP Port 61% –– Breach URI: /#modelbreach/114042 ]
[Model Breach: Security Integration / Integration Ransomware Detected 100% –– Breach URI: /#modelbreach/114049 ]
[Model Breach: Compromise / Beaconing Activity To External Rare 62% –– Breach URI: /#modelbreach/114059 ]
[Model Breach: Compromise / HTTP Beaconing to New Endpoint 30% –– Breach URI: /#modelbreach/114067 ]
[Model Breach: Security Integration / C2 Activity and Integration Detection 100% –– Breach URI: /#modelbreach/114069 ]
[Model Breach: Anomalous File / EXE from Rare External Location 55% –– Breach URI: /#modelbreach/114077 ]
[Model Breach: Compromise / High Volume of Connections with Beacon Score 66% –– Breach URI: /#modelbreach/114260 ]
[Model Breach: Security Integration / Low Severity Integration Detection 59% –– Breach URI: /#modelbreach/114293 ]
[Model Breach: Security Integration / Low Severity Integration Detection 33% –– Breach URI: /#modelbreach/114462 ]
[Model Breach: Security Integration / Integration Ransomware Detected 100% –– Breach URI: /#modelbreach/114109 ]· [Model Breach: Device / Three Or More New User Agents 31% –– Breach URI: /#modelbreach/114118 ]· [Model Breach: Anomalous Connection / Application Protocol on Uncommon Port 46% –– Breach URI: /#modelbreach/114113 ] · [Model Breach: Anomalous Connection / New User Agent to IP Without Hostname 62% –– Breach URI: /#modelbreach/114114 ]· [Model Breach: Device / New User Agent 32% –– Breach URI: /#modelbreach/114117 ]· [Model Breach: Anomalous File / EXE from Rare External Location 61% –– Breach URI: /#modelbreach/114122 ]· [Model Breach: Security Integration / Low Severity Integration Detection 54% –– Breach URI: /#modelbreach/114310 ]
[Model Breach: Security Integration / Integration Ransomware Detected 65% –– Breach URI: /#modelbreach/114662 ]Darktrace/Respond Model Breaches
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 61% –– Breach URI: /#modelbreach/114033 ]
[Model Breach: Antigena / Network::External Threat::Antigena File then New Outbound Block 100% –– Breach URI: /#modelbreach/114038 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block 100% –– Breach URI: /#modelbreach/114040 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block 87% –– Breach URI: /#modelbreach/114041 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach 87% –– Breach URI: /#modelbreach/114043 ]
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 100% –– Breach URI: /#modelbreach/114052 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Security Integration and Network Activity Block 87% –– Breach URI: /#modelbreach/114070 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 87% –– Breach URI: /#modelbreach/114071 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious Activity Block 87% –– Breach URI: /#modelbreach/114072 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 53% –– Breach URI: /#modelbreach/114079 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 64% –– Breach URI: /#modelbreach/114539 ]
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 66% –– Breach URI: /#modelbreach/114667 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious Activity Block 79% –– Breach URI: /#modelbreach/114684 ]·
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 100% –– Breach URI: /#modelbreach/114110 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block 87% –– Breach URI: /#modelbreach/114111 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach 87% –– Breach URI: /#modelbreach/114115 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 87% –– Breach URI: /#modelbreach/114116 ]·
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 61% –– Breach URI: /#modelbreach/114121 ]·
[Model Breach: Antigena / Network::External Threat::Antigena File then New Outbound Block 100% –– Breach URI: /#modelbreach/114123 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block 100% –– Breach URI: /#modelbreach/114125 ]

List of IoCs

IoC - Type - Description + Confidence

5.188.87[.]58 - IP address - C2 endpoint

80.66.88[.]145 - IP address - C2 endpoint

/bfyxraav - URI - Possible C2 endpoint URI

/msibfyxraav - URI - Possible C2 endpoint URI

Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5) - User agent - Probable user agent leveraged

curl - User agent - Probable user agent leveraged

curl/8.0.1 - User agent - Probable user agent leveraged

Mozilla/4.0 (compatible; Synapse) - User agent - Probable user agent leveraged

Autoit3.exe - Filename - Exe file

CvUYLoTv.au3

eDVeqcCe.au3

FeLlcFRS.au3

FTEZlGhe.au3

HOrzcEWV.au3

rKlArXHH.au3

SjadeWUz.au3

ZgOLxJQy.au3

zSrxhagw.au3

ALOXitYE.au3

DKRcfZfV.au3

gQZVKzek.au3

JZrvmJXK.au3

kLECCtMw.au3

LEXCjXKl.au3

luqWdAzF.au3

mUBNrGpv.au3

OoCdHeJT.au3

PcEJXfIl.au3

ssElzrDV.au3

TcBwRRnp.au3

TFvAUIgu.au3

xkwtvq.au3

otxynh.au3

dcthbq.au3 - Filenames - Possible exe files delivered in response to curl/8.0.1 GET requests with Target URI '/msibfyxraav

f3a0a85fe2ea4a00b3710ef4833b07a5d766702b263fda88101e0cb804d8c699 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

afa3feea5964846cd436b978faa7d31938e666288ffaa75d6ba75bfe6c12bf61 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

63aeac3b007436fa8b7ea25298362330423b80a4cb9269fd2c3e6ab1b1289208 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

ab6704e836a51555ec32d1ff009a79692fa2d11205f9b4962121bda88ba55486 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

References

1. https://www.truesec.com/hub/blog/darkgate-loader-delivered-via-teams

2. https://feedit.cz/wp-content/uploads/2023/03/YiR2022_onepager_ransomware_loaders.pdf

‍3. https://www.virustotal.com/gui/ip-address/5.188.87[.]58

‍4. https://www.forescout.com/resources/darkgate-loader-malspam-campaign/

‍5. https://otx.alienvault.com/indicator/ip/80.66.88[.]145

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Inside the SOC

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Senior Director, Product Marketing

Watch the NIS2 Webinar

Blog

AI

July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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

[related-resource]

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.

About the author

Ashanka Iddya

Senior Director, Product Marketing

Blog

Cloud

July 17, 2025

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

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.

[related-resource]

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.

[related-resource]

About the author

Calum Hall

Technical Content Researcher

Your data. Our AI.

Elevate your network security with Darktrace AI

Get a demo

Check out this article by Darktrace: How Darktrace Halted A DarkGate in MS Teams

How Darktrace Halted A DarkGate in MS Teams

Securing Microsoft Teams and SharePoint

DarkGate Attack Overview

Conclusion

Appendices

Darktrace DETECT Model Detections

List of IoCs

References

Crypto Wallets Continue to be Drained in Elaborate Social Media Scam

Introducing the AI Maturity Model for Cybersecurity

Enjoying the blog?

More in this series

Defending the Cloud: Stopping Cyber Threats in Azure and AWS with Darktrace

Top Eight Threats to SaaS Security and How to Combat Them

From Rockstar2FA to FlowerStorm: Investigating a Blooming Phishing-as-a-Service Platform

Blog

AI

July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

AI adoption in cybersecurity: Beyond the hype

Why a maturity model? And why now?

How is maturity assessed in this model?

Why Darktrace?

The structure: From experimentation to autonomy

Strategic benefits for security leaders

SOC fatigue is real, and AI can help

GenAI is error prone, requiring strong human oversight

AI’s real value lies in progression

Trust and oversight are key initially but evolves in later levels

People’s roles shift meaningfully

Outcome, not hype, defines maturity

Outcomes across the AI Security Maturity Model

L0 – Manual Operations

L1 – Automation Rules

L2 – AI Assistance

L3 – AI Collaboration

L4 – AI Delegation

Join the webinar

Blog

Cloud

July 17, 2025

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

1. Depth of data

2. Chain of custody

3. Automated collection and isolation

4. Ease of use

5. Incident preparedness

Cloud forensics with Darktrace