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April 22, 2021

Darktrace Identifies APT35 in Pre-Infected State

Learn how Darktrace identified APT35 (Charming Kitten) in a pre-infected environment. Gain insights into the detection and mitigation of this threat.
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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22
Apr 2021

What is APT35?

APT35, sometimes referred to as Charming Kitten, Imperial Kitten, or Tortoiseshell, is a notorious cyber-espionage group which has been active for nearly 10 years. Famous for stealing scripts from HBO’s Game of Thrones in 2017 and suspected of interfering in the U.S. presidential election last year, it has launched extensive campaigns against organizations and officials across North America and the Middle East. Public attribution has associated APT35 with an Iran-based nation state threat actor.

Darktrace regularly detects attacks by many known threat actors including Evil Corp and APT41, alongside large amounts of malicious but uncategorized activity from sophisticated attack groups. As Cyber AI doesn’t rely on pre-defined rules, signatures, or threat intelligence to detect cyber-attacks, it often detects new and previously unknown threats.

This blog post examines a real-world instance of APT35 activity in an organization in the EMEA region. Darktrace observed this activity last June, but due to ongoing investigations, details are only now being released with the wider community. It represents an interesting case for the value of self-learning AI in two key ways:

  • Identifying ‘low and slow’ attacks: How do you spot an attacker that is lying low and conducts very little detectable activity?
  • Detecting pre-existing infections without signatures: What if a threat actor is already inside the system when Cyber AI is activated?

Advanced Persistent Threats (APTs) lying low

APT35 had already infected a single corporate device, likely via a spear phishing email, when Cyber AI was deployed in the company’s digital estate for the first time.

The infected device exhibited no other signs of malicious activity beyond continued command and control (C2) beaconing, awaiting instructions from the attackers for several days. This is what we call ‘lying low’ – where the hacker stays present within a system, but remains under the radar, avoiding detection either intentionally, or because they’re focusing on another victim while being content with backdoor access into the organization.

Either way, this is a nightmare scenario for a security team and any security vendor: an APT which has established a foothold and is lying in wait to continue their attack – undetected.

Finding the infected device

When Darktrace’s AI was first activated, it spent five business days learning the unique ‘patterns of life’ for the organization. After this initial, short learning period, Darktrace immediately flagged the infected device and the C2 activity.

Although the breach device had been beaconing since before Darktrace was implemented, Cyber AI automatically clusters devices into ‘peer groups’ based on similar behavioral patterns, enabling Darktrace to identify the continued C2 traffic coming from the device as highly unusual in comparison to the wider, automatically identified peer group. None of its behaviorally close neighbors were doing anything remotely similar, and Darktrace was therefore able to determine that the activity was malicious, and that it represented C2 beaconing.

Darktrace detected the APT35 C2 activity without the use of any signatures or threat intelligence on multiple levels. Responding to the alerts, the internal security team quickly isolated the device and verified with the Darktrace system that no further reconnaissance, lateral movement, or data exfiltration had taken place.

APT35 ‘Charming Kitten’ analysis

Once the C2 was detected, Cyber AI Analyst immediately began analyzing the infected device. The Cyber AI Analyst only highlights the most severe incidents in any given environment and automates many of the typical level one and level two SOC tasks. This includes reviewing all alerts, investigating the scope and nature of each event, and reducing time to triage by 92%.

Figure 1: Similar Cyber AI Analyst report observing C2 communications

Numerous factors made the C2 activity stand out strongly to Darktrace. Combining all those small anomalies, Darktrace was able to autonomously prioritize this behavior and classify it as the most significant security incident in the week.

Figure 2: Example list of C2 detections for an APT35 attack

Some of the command and control destinations were known to threat intelligence and open-source intelligence (OSINT) – for instance, the domain cortanaservice[.]com is a known C2 domain for APT35.

However, the presence of a known malicious domain does not guarantee detection. In fact, the organization had a very mature security stack, yet they failed to discover the existing APT35 infection until Darktrace was activated in their environment.

Assessing the impact of the intrusion

Once an intrusion has been identified, it is important to understand the extent of it – such as whether lateral movement is occurring and what connectivity the infected device has in general. Asset management is never perfect, so it can be very hard for organizations to determine what damage a compromised device is capable of inflicting.

Darktrace presents this information in real time, and from a bird’s-eye perspective, making the assessment very simple. It immediately highlights which subnet the device is located in and any further context.

Figure 3: Darktrace’s Threat Visualizer displaying the connectivity of a device

Based on this information, the organization confirmed that it was a corporate device that had been infected by APT35. As Darktrace shows any credentials associated with the device, a quick assessment could be made of potentially compromised accounts.

Figure 4: Similar and associated credentials of a device

Luckily, only a single local user account was associated with the device.

The exact level of privileges and connectivity which the infected device had, as well as the extent to which the intrusion might have spread from the initially infected device, was still uncertain. By looking at the device’s event log, this became rapidly clear within minutes.

Filtering first for internal connections only (excluding any connections going to the Internet) gave a good idea of the level of connectivity of the device. A cursory glance showed that the device did indeed have some level of internal connectivity. It made DNS requests to the internal domain controller and was making successful NetBIOS connections over ports 135 and 139 internally.

By filtering further in the event log, it quickly became clear that in this time the device had not used any administrative channels, such as RDP, SSH, Telnet, or SMB. This is a strong indicator that no lateral movement over common channels had taken place.

It is more difficult to assess whether the device was performing any other suspicious activity, like stealthy reconnaissance or staging data from other internal devices. Darktrace provided another capability to assess this quickly – filtering the device’s network connections to show only unusual or new connections.

Figure 5: Event device log filtered to show unusual connections only

Darktrace assesses each individual connection for every entity observed in context, using its unsupervised machine learning to evaluate how unusual a given connection is. This could be a single new failed internal connection attempt, indicating stealthy reconnaissance, or a connection over SMB at an unusual time to a new internal destination, implying lateral movement or data staging.

By filtering for only unusual or new connections, Darktrace’s AI produces further leads that can be pursued extremely quickly, thanks to the context and added visibility.

No further suspicious internal connections were observed, strengthening the hypothesis that APT35 was lying low at that time.

Unprecedented but not unpreventable

Darktrace’s 24/7 monitoring service, Proactive Threat Notifications, would have alerted on and escalated the incident. Darktrace RESPOND would have responded autonomously and enforced normal activity for the device, preventing the C2 traffic without interrupting regular business workflows.

It is impossible to predefine where the next attack will come from. APT35 is just one of the many sophisticated threat actors on the scene, and with such a diverse and volatile threat landscape, unsupervised machine learning is crucial in spotting and defending against anomalies, no matter what form they take.

This case study helps illustrate how Darktrace detects pre-existing infections and ‘low and slow’ attacks, and further shows how Darktrace can be used to quickly understand the scope and extent of an intrusion.

Learn how Cyber AI Analyst detected APT41 two weeks before public attribution

Shortened list of C2 detections over four days on the infected device:

  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Beaconing Meta Model
  • Compromise / Beaconing Activity To External Rare
  • Compromise / SSL Beaconing To Rare Destination
  • Compromise / Slow Beaconing To External Rare
  • Compromise / High Volume of Connections with Beacon Score
  • Compromise / Unusual Connections to Rare Lets Encrypt
  • Compromise / Beacon for 4 Days
  • Compromise / Agent Beacon

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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September 25, 2025

Announcing Unified Real-Time CDR and Automated Investigations to Transform Cloud Security Operations

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Fragmented Tools are Failing SOC Teams in the Cloud Era

The cloud has transformed how businesses operate, reshaping everything from infrastructure to application delivery. But cloud security has not kept pace. Most tools still rely on traditional models of logging, policy enforcement, and posture management; approaches that provide surface-level visibility but lack the depth to detect or investigate active attacks.

Meanwhile, attackers are exploiting vulnerabilities, delivering cloud-native exploits, and moving laterally in ways that posture management alone cannot catch fast enough. Critical evidence is often missed, and alerts lack the forensic depth SOC analysts need to separate noise from true risk. As a result, organizations remain exposed: research shows that nearly nine in ten organizations have suffered a critical cloud breach despite investing in existing security tools [1].

SOC teams are left buried in alerts without actionable context, while ephemeral workloads like containers and serverless functions vanish before evidence can be preserved. Point tools for logging or forensics only add complexity, with 82% of organizations using multiple platforms to investigate cloud incidents [2].

The result is a broken security model: posture tools surface risks but don’t connect them to active attacker behaviors, while investigation tools are too slow and fragmented to provide timely clarity. Security teams are left reactive, juggling multiple point solutions and still missing critical signals. What’s needed is a unified approach that combines real-time detection and response for active threats with automated investigation and cloud posture management in a single workflow.

Just as security teams once had to evolve beyond basic firewalls and antivirus into network and endpoint detection, response, and forensics, cloud security now requires its own next era: one that unifies detection, response, and investigation at the speed and scale of the cloud.

A Powerful Combination: Real-Time CDR + Automated Cloud Forensics

Darktrace / CLOUD now uniquely unites detection, investigation, and response into one workflow, powered by Self-Learning AI. This means every alert, from any tool in your stack, can instantly become actionable evidence and a complete investigation in minutes.

With this release, Darktrace / CLOUD delivers a more holistic approach to cloud defense, uniting real-time detection, response, and investigation with proactive risk reduction. The result is a single solution that helps security teams stay ahead of attackers while reducing complexity and blind spots.

  • Automated Cloud Forensic Investigations: Instantly capture and analyze volatile evidence from cloud assets, reducing investigation times from days to minutes and eliminating blind spots
  • Enhanced Cloud-Native Threat Detection: Detect advanced attacker behaviors such as lateral movement, privilege escalation, and command-and-control in real time
  • Enhanced Live Cloud Topology Mapping: Gain continuous insight into cloud environments, including ephemeral workloads, with live topology views that simplify investigations and expose anomalous activity
  • Agentless Scanning for Proactive Risk Reduction: Continuously monitor for misconfigurations, vulnerabilities, and risky exposures to reduce attack surface and stop threats before they escalate.

Automated Cloud Forensic Investigations

Darktrace / CLOUD now includes capabilities introduced with Darktrace / Forensic Acquisition & Investigation, triggering automated forensic acquisition the moment a threat is detected. This ensures ephemeral evidence, from disks and memory to containers and serverless workloads can be preserved instantly and analyzed in minutes, not days. The integration unites detection, response, and forensic investigation in a way that eliminates blind spots and reduces manual effort.

Figure 1: Easily view Forensic Investigation of a cloud resource within the Darktrace / CLOUD architecture map

Enhanced Cloud-Native Threat Detection

Darktrace / CLOUD strengthens its real-time behavioral detection to expose early attacker behaviors that logs alone cannot reveal. Enhanced cloud-native detection capabilities include:

• Reconnaissance & Discovery – Detects enumeration and probing activity post-compromise.

• Privilege Escalation via Role Assumption – Identifies suspicious attempts to gain elevated access.

• Malicious Compute Resource Usage – Flags threats such as crypto mining or spam operations.

These enhancements ensure active attacks are detected earlier, before adversaries can escalate or move laterally through cloud environments.

Figure 2: Cyber AI Analyst summary of anomalous behavior for privilege escalation and establishing persistence.

Enhanced Live Cloud Topology Mapping

New enhancements to live topology provide real-time mapping of cloud environments, attacker movement, and anomalous behavior. This dynamic visibility helps SOC teams quickly understand complex environments, trace attack paths, and prioritize response. By integrating with Darktrace / Proactive Exposure Management (PEM), these insights extend beyond the cloud, offering a unified view of risks across networks, endpoints, SaaS, and identity — giving teams the context needed to act with confidence.

Figure 3: Enhanced live topology maps unify visibility across architectures, identities, network connections and more.

Agentless Scanning for Proactive Risk Reduction

Darktrace / CLOUD now introduces agentless scanning to uncover malware and vulnerabilities in cloud assets without impacting performance. This lightweight, non-disruptive approach provides deep visibility into cloud workloads and surfaces risks before attackers can exploit them. By continuously monitoring for misconfigurations and exposures, the solution strengthens posture management and reduces attack surface across hybrid and multi-cloud environments.

Figure 4: Agentless scanning of cloud assets reveals vulnerabilities, which are prioritized by severity.

Together, these capabilities move cloud security operations from reactive to proactive, empowering security teams to detect novel threats in real time, reduce exposures before they are exploited, and accelerate investigations with forensic depth. The result is faster triage, shorter MTTR, and reduced business risk — all delivered in a single, AI-native solution built for hybrid and multi-cloud environments.

Accelerating the Evolution of Cloud Security

Cloud security has long been fragmented, forcing teams to stitch together posture tools, log-based monitoring, and external forensics to get even partial coverage. With this release, Darktrace / CLOUD delivers a holistic, unified approach that covers every stage of the cloud lifecycle, from proactive posture management and risk identification to real-time detection, to automated investigation and response.

By bringing these capabilities together in a single AI-native solution, Darktrace is advancing cloud security beyond incremental change and setting a new standard for how organizations protect their hybrid and multi-cloud environments.

With Darktrace / CLOUD, security teams finally gain end-to-end visibility, response, and investigation at the speed of the cloud, transforming cloud defense from fragmented and reactive to unified and proactive.

[related-resource]

Sources: [1], [2] Darktrace Report: Organizations Require a New Approach to Handle Investigations in the Cloud

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About the author
Adam Stevens
Senior Director of Product, Cloud | Darktrace

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September 25, 2025

Introducing the Industry’s First Truly Automated Cloud Forensics Solution

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Why Cloud Investigations Fail Today

Cloud investigations have become one of the hardest problems in modern cybersecurity. Traditional DFIR tools were built for static, on-prem environments, rather than dynamic and highly scalable cloud environments, containing ephemeral workloads that disappear in minutes. SOC analysts are flooded with cloud security alerts with one-third lacking actionable data to confirm or dismiss a threat[1], while DFIR teams waste 3-5 days requesting access and performing manual collection, or relying on external responders.

These delays leave organizations vulnerable. Research shows that nearly 90% of organizations suffer some level of damage before they can fully investigate and contain a cloud incident [2]. The result is a broken model: alerts are closed without a complete understanding of the threat due to a lack of visibility and control, investigations drag on, and attackers retain the upper hand.

For SOC teams, the challenge is scale and clarity. Analysts are inundated with alerts but lack the forensic depth to quickly distinguish real threats from noise. Manual triage wastes valuable time, creates alert fatigue, and often forces teams to escalate or dismiss incidents without confidence — leaving adversaries with room to maneuver.

For DFIR teams, the challenge is depth and speed. Traditional forensics tools were built for static, on-premises environments and cannot keep pace with ephemeral workloads that vanish in minutes. Investigators are left chasing snapshots, requesting access from cloud teams, or depending on external responders, leading to blind spots and delayed response.

That’s why we built Darktrace / Forensic Acquisition & Investigation, the first automated forensic solution designed specifically for the speed, scale, and complexities of the cloud. It addresses both sets of challenges by combining automated forensic evidence capture, attacker timeline reconstruction, and cross-cloud scale. The solution empowers SOC analysts with instant clarity and DFIR teams with forensic depth, all in minutes, not days. By leveraging the very nature of the cloud, Darktrace makes these advanced capabilities accessible to security teams of all sizes, regardless of expertise or resources.

Introducing Automated Forensics at the Speed and Scale of Cloud

Darktrace / Forensic Acquisition & Investigation transforms cloud investigations by capturing, processing, and analyzing forensic evidence of cloud workloads, instantly, even from time-restricted ephemeral resources. Triggered by a detection from any cloud security tool, the entire process is automated, providing accurate root cause analysis and deep insights into attacker behavior in minutes rather than days or weeks. SOC and DFIR teams no longer have to rely on manual processes, snapshots, or external responders, they can now leverage the scale and elasticity of the cloud to accelerate triage and investigations.

Seamless Integration with Existing Detection Tools

Darktrace / Forensic Acquisition & Investigation does not require customers to replace their detection stack. Instead, it integrates with cloud-native providers, XDR platforms, and SIEM/SOAR tools, automatically initiating forensic capture whenever an alert is raised. This means teams can continue leveraging their existing investments while gaining the forensic depth required to validate alerts, confirm root cause, and accelerate response.

Most importantly, the solution is natively integrated with Darktrace / CLOUD, turning real-time detections of novel attacker behaviors into full forensic investigations instantly. When Darktrace / CLOUD identifies suspicious activity such as lateral movement, privilege escalation, or abnormal usage of compute resources, Darktrace / Forensic Acquisition & Investigation automatically preserves the underlying forensic evidence before it disappears. This seamless workflow unites detection, response, and investigation in a way that eliminates gaps, accelerates triage, and gives teams confidence that every critical cloud alert can be investigated to completion.

Figure 1: Integration with Darktrace / CLOUD – this example is showing the ability to pivot into the forensic investigation associated with a compromised cloud asset

Automated Evidence Collection Across Hybrid and Multi-Cloud

The solution provides automated forensic acquisition across AWS, Microsoft Azure, GCP, and on-prem environments. It supports both full volume capture, creating a bit-by-bit copy of an entire storage device for the most comprehensive preservation of evidence, and triage collection, which prioritizes speed by gathering only the most essential forensic artifacts such as process data, logs, network connections, and open file contents. This flexibility allows teams to strike the right balance between speed and depth depending on the investigation at hand.

Figure 2: Ability to acquire forensic data from Cloud, SaaS and on-prem environments

Automated Investigations, Root Cause Analysis and Attacker Timelines

Once evidence is collected, Darktrace applies automation to reconstruct attacker activity into a unified timeline. This includes correlating commands, files, lateral movement, and network activity into a single investigative view enriched with custom threat intelligence such as IOCs. Detailed investigation reporting including an investigation summary, an overview of the attacker timeline, and key events. Analysts can pivot into detailed views such as the filesystem view, traversing directories or inspecting file content, or filter and search using faceted options to quickly narrow the scope of an investigation.

Figure 3: Automated Investigation view surfacing the most significant attacker activity, which is contextualized with Alarm information

Forensics for Containers and Ephemeral Assets

Investigating containers and serverless workloads has historically been one of the hardest challenges for DFIR teams, as these assets often disappear before evidence can be preserved. Darktrace / Forensic Acquisition & Investigation captures forensic evidence across managed Kubernetes cloud services, even from distroless or no-shell containers, AWS ECS and other environments, ensuring that ephemeral activity is no longer a blind spot. For hybrid organizations, this extends to on-premises Kubernetes and OpenShift deployments, bringing consistency across environments.

Figure 4: Container investigations – this example is showing the ability to capture containers from managed Kubernetes cloud services

SaaS Log Collection for Modern Investigations

Beyond infrastructure-level data, the solution collects logs from SaaS providers such as Microsoft 365, Entra ID, and Google Workspace. This enables investigations into common attack types like business email compromise (BEC), account takeover (ATO), and insider threats — giving teams visibility into both infrastructure-level and SaaS-driven compromise from a single platform.

Figure 5: Ability to import logs from SaaS providers including Microsoft 365, Entra ID, and Google Workspace

Proactive Vulnerability and Malware Discovery

Finally, the solution surfaces risk proactively with vulnerability and malware discovery for Linux-based cloud resources. Vulnerabilities are presented in a searchable table and correlated with the attacker timeline, enabling teams to quickly understand not just which packages are exposed, but whether they have been targeted or exploited in the context of an incident.

Figure 6: Vulnerability data with pivot points into the attacker timeline

Cloud-Native Scale and Performance

Darktrace / Forensic Acquisition & Investigation uses a cloud-native parallel processing architecture that spins up compute resources on demand, ensuring that investigations run at scale without bottlenecks. Detailed reporting and summaries are automatically generated, giving teams a clear record of the investigation process and supporting compliance, litigation readiness, and executive reporting needs.

Scalable and Flexible Deployment Options

Every organization has different requirements for speed, control, and integration. Darktrace / Forensic Acquisition & Investigation is designed to meet those needs with two flexible deployment models.

  • Self-Hosted Virtual Appliance delivers deep integration and control across hybrid environments, preserving forensic data for compliance and litigation while scaling to the largest enterprise investigations.
  • SaaS-Delivered Deployment provides fast time-to-value out of the box, enabling automated forensic response without requiring deep cloud expertise or heavy setup.

Both models are built to scale across regions and accounts, ensuring organizations of any size can achieve rapid value and adapt the solution to their unique operational and compliance needs. This flexibility makes advanced cloud forensics accessible to every security team — whether they are optimizing for speed, integration depth, or regulatory alignment

Delivering Advanced Cloud Forensics for Every Team

Until now, forensic investigations were slow, manual, and reserved for only the largest organizations with specialized DFIR expertise. Darktrace / Forensic Acquisition & Investigation changes that by leveraging the scale and elasticity of the cloud itself to automate the entire investigation process. From capturing full disk and memory at detection to reconstructing attacker timelines in minutes, the solution turns fragmented workflows into streamlined investigations available to every team.

Whether deployed as a SaaS-delivered service for fast time-to-value or as a self-hosted appliance for deep integration, Darktrace / Forensic Acquisition & Investigation provides the features that matter most: automated evidence capture, cross-cloud investigations, forensic depth for ephemeral assets, and root cause clarity without manual effort.

With Darktrace / Forensic Acquisition & Investigation, what once took days now takes minutes. Now, forensic investigations in the cloud are faster, more scalable, and finally accessible to every security team, no matter their size or expertise.

[related-resource]

Sources: [1], [2] Darktrace Report: Organizations Require a New Approach to Handle Investigations in the Cloud

Additional Resources

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About the author
Paul Bottomley
Director of Product Management | Darktrace
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