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January 6, 2021

Darktrace Insights On SolarWinds Hack

Learn how Darktrace analyzed the SolarWinds hack without signatures. Understand the techniques used to identify and mitigate this major cyber 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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06
Jan 2021

For a high-level explanation of the SolarWinds hack, watch our video below.

The SUNBURST malware attacks against SolarWinds have heightened companies’ concerns about the risk to their digital environments. Malware installed during software updates in March 2020 has allowed advanced attackers to gain unauthorized access to files that may include customer data and intellectual property.

Darktrace does not use SolarWinds, and its operations remain unaffected by this breach. However, SolarWinds is an IT discovery tool that is used by a significant number of Darktrace customers. In what follows, we explore a set of Darktrace detections that highlight and alert security teams to the types of behaviors related to this breach.

This is not an example of a SolarWinds compromise, but examples of anomalous behaviors we can expect to see from this type of breach. These examples stress the value of self-learning Cyber AI capable of understanding the evolving normal ‘patterns of life’ within an enterprise – as opposed to a signature-based approach that looks at historical data to predict today’s threat.

As Darktrace detects device activity patterns rather than known malicious signatures, detecting use of these techniques will fall into the scope of Darktrace’s capabilities without further need for configuration. The technology automatically clusters devices into ‘peer groups’, allowing it to detect cases of an individual device behaving unusually. Using a self-learning approach is the best possible mechanism to catch an attacker who gains access into your systems using a degree of stealth so as to not trigger signature-based detection.

A number of these models may fire in combination with other models in order to make a strong detection over a time-series – and this is exactly where Darktrace’s autonomous incident triage capability, Cyber AI Analyst, plays a crucial role in investigating the alerts on behalf of security teams. Cyber AI Analyst saves critical time for security teams, and its results should be treated with a high priority during this period of vigilance.

How SolarWinds was detected with AI

We want to focus on the most sophisticated details of the hands-on intrusion that in many cases followed the initial automated attack. This post-exploitation part of the attack is much more varied and stealthy. These stages are also near-impossible to predict, as they are driven by the attacker’s intentions and goals for each individual victim at this stage – making the use of signatures, threat intelligence or static use cases virtually useless.

While the automated, initial malware execution is a critical initial step to understand, the behavior was pre-configured for the malware and included the download of further payloads and the connection to domain-generation-algorithm (DGA) based subdomains of avsvmcloud[.]com. These automated first stages of the attack have been sufficiently researched in depth by the community. This post is not aiming to add anything to these findings, but instead takes a look at the potential post-infection activities.

Malware / C2 domains

The threat-actor set the hostnames on their later-stage command and control (C2) infrastructure to match a legitimate hostname found within the victim’s environment. This allowed the adversary to blend into the environment, avoid suspicion, and evade detection. They further used C2 servers in geopolitical proximity to their victims, further circumventing static geo-based trusts lists. Darktrace is unaffected by this type of tradecraft as it does not have implicit, pre-defined trust of any geo-locations.

This would be very likely to trigger the following Darktrace Cyber AI models. The models were not specifically designed to detect SolarWinds modifications but have been in place for years – they are designed to detect the subtle but significant attacker activities occurring within an organization’s network.

  • Compromise / Agent Beacon to New Endpoint
  • Compromise / SSL Beaconing to New Endpoint
  • Compromise / HTTP Beaconing to New Endpoint*

*The implant uses SSL, but may be identified as HTTP if using a proxy.

Lateral movement using different credentials

Once the attacker gained access to the network with compromised credentials, they moved laterally using multiple different credentials. The credentials used for lateral movement were always different from those used for remote access.

This very likely would trigger the following Cyber AI models:

  • User / Multiple Uncommon New Credentials on Device
Figure 1: Example breach event log showing anomalous (new) logins from a single device, with multiple user credentials
  • User / New Admin Credentials on Client
Figure 2: Example breach event log showing anomalous admin login

Temporary file replacement and temporary task modification

The attacker used a temporary file replacement technique to remotely execute utilities: they replaced a legitimate utility with theirs, executed their payload, and then restored the legitimate original file. They similarly manipulated scheduled tasks by updating an existing legitimate task to execute their tools and then returned the scheduled task to its original configuration. They routinely removed their tools – including the removal of backdoors once legitimate remote access was achieved.

This would be very likely to trigger the following Cyber AI models:

  • Anomalous Connection / New or Uncommon Service Control
Figure 3: Example breach showing uncommon service control
  • Anomalous Connection / High Volume of New or Uncommon Service Control
Figure 4: Example breach showing 10 uncommon service controls
  • Device / AT Service Scheduled Task
Figure 5: Breach event log shows new AT service scheduled task activity
  • Device / Multiple RPC Requests for Unknown Services
Figure 6: Breach shows multiple binds to unknown RPC services
  • Device / Anomalous SMB Followed By Multiple Model Breaches
Figure 7: Breach shows unusual SMB activity, combined with slow beaconing
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
Figure 8: Breach shows device writing .bat file to temp folder on another device
  • Unusual Activity / Anomalous SMB to New or Unusual Locations
Figure 9: Breach shows new access to SAMR, combined with SMB Reads and Kerberos login failures
  • Unusual Activity / Sustained Anomalous SMB Activity
Figure 10: Breach shows significant deviation in SMB activity from device

SolarWinds breach remembered

By understanding where credentials are used and which devices talk to each other, Cyber AI has an unprecedented and dynamic understanding of business systems. This empowers it to alert security teams to enterprise changes that could indicate cyber risk in real time.

These alerts demonstrate how AI learns ‘normal’ for the unique digital environment surrounding it, and then alerts operators to deviations, including those that are directly relevant to the SUNBURST compromise. It further provides insights into how the attacker exploited those networks that did not have the appropriate visibility and detection capabilities.

On top of these alerts, Cyber AI Analyst will also be automatically correlating these detections over time to identify patterns, generating comprehensive and intuitive incident summaries and significantly reducing triage time. Reviewing Cyber AI Analyst alerts should be given high priority over the next several weeks.


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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