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March 22, 2024

What are Botnets and How Darktrace Uncovers Them

Learn how Darktrace detected and implemented defense protocols against Socks5Systemz botnet before any threat to intelligence had been published.
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
Adam Potter
Senior Cyber Analyst
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22
Mar 2024

What are botnets?

Although not a recent addition to the threat landscape, botnets persist as a significant concern for organizations, with many threat actors utilizing them for political, strategic, or financial gain. Botnets pose a particularly persistent threat to security teams; even if one compromised device is detected, attackers will likely have infected multiple devices and can continue to operate. Moreover, threat actors are able to easily replace the malware communication channels between infected devices and their command-and-control (C2) servers, making it incredibly difficult to remove the infection.

Botnet example: Socks5Systemz

One example of a botnet recently investigated by the Darktrace Threat Research team is Socks5Systemz. Socks5Systemz is a proxy-for-rent botnet, whereby actors can rent blocks of infected devices to perform proxying services.  Between August and November 2023, Darktrace detected indicators of Socks5Systemz botnet compromise within a cross-industry section of the customer base. Although open-source intelligence (OSINT) research of the botnet only appeared in November 2023, the anomaly-based approach of Darktrace DETECT™ allowed it to identify multiple stages of the network-based activity on affected customer systems well before traditional rules and signatures would have been implemented.

Darktrace’s Cyber AI Analyst™ complemented DETECT’s successful identification of Socks5Systemz activity on customer networks, playing a pivotal role in piecing together the seemingly separate events that comprised the wider compromise. This allowed Darktrace to build a clearer picture of the attack, empowering its customers with full visibility over emerging incidents.

In the customer environments highlighted in this blog, Darktrace RESPOND™ was not configured to operate autonomously. As a result, Socks5Systemz attacks were able to advance through their kill chains until customer security teams acted upon Darktrace’s detections and began their remediation procedures.

What is Socks5Systemz?

The Socks5Systemz botnet is a proxy service where individuals can use infected devices as proxy servers.

These devices act as ‘middlemen’, forwarding connections from malicious actors on to their intended destination. As this additional connectivity conceals the true origin of the connections, threat actors often use botnets to increase their anonymity. Although unauthorized proxy servers on a corporate network may not appear at first glance to be a priority for organizations and their security teams, complicity in proxy botnets could result in reputational damage and significant financial losses.

Since it was first observed in the wild in 2016, the Socks5Systemz botnet has grown steadily, seemingly unnoticed by cyber security professionals, and has infected a reported 10,000 devices worldwide [1]. Cyber security researchers noted a high concentration of compromised devices in India, with lower concentrations of devices infected in the United States, Latin America, Australia and multiple European and African countries [2]. Renting sections of the Socks5Systemz botnet costs between 1 USD and 4,000 USD, with options to increase the threading and time-range of the rentals [2]. Due to the lack of affected devices in Russia, some threat researchers have concluded that the botnet’s operators are likely Russian [2].

Darktrace’s Coverage of Socks5Systemz

The Darktrace Threat Research team conducted investigations into campaign-like activity across the customer base between August and November 2023, where multiple indicators of compromise (IoCs) relating to the Socks5Systemz proxy botnet were observed. Darktrace identified several stages of the attack chain described in static malware analysis by external researchers. Darktrace was also able to uncover additional IoCs and stages of the Socks5Systemz attack chain that had not featured in external threat research.

Delivery and Execution

Prior research on Socks5Systemz notes how the malware is typically delivered via user input, with delivery methods including phishing emails, exploit kits, malicious ads, and trojanized executables downloaded from peer-to-peer (P2P) networks [1].

Threat actors have also used separate malware loaders such as PrivateLoader and Amadey deliver the Socks5Systemz payload. These loaders will drop executable files that are responsible for setting up persistence and injecting the proxy bot into the infected device’s memory [2]. Although evidence of initial payload delivery did not appear during its investigations, Darktrace did discover IoCs relating to PrivateLoader and Amadey on multiple customer networks. Such activity included HTTP POST requests using PHP to rare external IPs and HTTP connections with a referrer header field, indicative of a redirected connection.

However, additional adjacent activity that may suggest initial user execution and was observed during Darktrace’s investigations. For example, an infected device on one deployment made a HTTP GET request to a rare external domain with a “.fun” top-level domain (TLD) for a PDF file. The URI also appears to have contained a client ID. While this download and HTTP request likely corresponded to the gathering and transmission of further telemetry data and infection verification [2], the downloaded PDF file may have represented a malicious payload.

Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.
Figure 1: Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.

Establishing C2 Communication  

Once the proxy bot has been injected into the device’s memory, the malware attempts to contact servers owned by the botnet’s operators. Across several customer environments, Darktrace identified infected devices attempting to establish connections with such C2 servers. First, affected devices would make repeated HTTP GET requests over port 80 to rare external domains; these endpoints typically had “.ua” and “.ru” TLDs. The majority of these connection attempts were not preceded by a DNS host lookup, suggesting that the domains were already loaded in the device’s cache memory or hardcoded into the code of running processes.

Figure 2: Breach log data connections identifying repeated unusual HTTP connections over port 80 for domains without prior DNS host lookup.

While most initial HTTP GET requests across investigated incidents did not feature DNS host lookups, Darktrace did identify affected devices on a small number of customer environments performing a series of DNS host lookups for seemingly algorithmically generated domains (DGA). These domains feature the same TLDs as those seen in connections without prior DNS host lookups.  

Figure 3: Cyber AI Analyst data indicating a subset of DGAs queried via DNS by infected devices.

These DNS requests follow the activity reported by researchers, where infected devices query a hardcoded DNS server controlled by the threat actor for an DGA domain [2]. However, as the bulk of Darktrace’s investigations presented HTTP requests without a prior DNS host lookup, this activity indicates a significant deviation from the behavior reported by OSINT sources. This could indicate that multiple variations of the Socks5Systemz botnet were circulating at the time of investigation.

Most hostnames observed during this time of investigation follow a specific regular expression format: /[a-z]{7}\.(ua|net|info|com|ru)/ or /[a-z0-9]{15}\.(ua)/. Darktrace also noticed the HTTP GET requests for DGA domains followed a consistent URI pattern: /single.php?c=<STRING>. The requests were also commonly made using the “Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)” user agent over port 80.

This URI pattern observed during Darktrace’s investigations appears to reflect infected devices contacting Socks5Systemz C2 servers to register the system and details of the host, and signal it is ready to receive further instructions [2]. These URIs are encrypted with a RC4 stream cipher and contain information relating to the device’s operating system and architecture, as well as details of the infection.

The HTTP GET requests during this time, which involved devices made to a variety a variety of similar DGA domains, appeared alongside IP addresses that were later identified as Socks5Systemz C2 servers.

Figure 4: Cyber AI Analyst investigation details highlighting HTTP GET activity whereby RC4 encrypted data is sent to proxy C2 domains.

However, not all affected devices observed by Darktrace used DGA domains to transmit RC4 encoded data. Some investigated systems were observed making similar HTTP GET requests over port 80, albeit to the external domain: “bddns[.]cc”, using the aforementioned Mozilla user agent. During these requests, Darktrace identified a consistent URI pattern, similar to that seen in the DGA domain GET requests: /sign/<RC4 cipher text>.  

Darktrace DETECT recognized the rarity of the domains and IPs that were connected to by affected devices, as well as the usage of the new Mozilla user agent.  The HTTP connections, and the corresponding Darktrace DETECT model breaches, parallel the analysis made by external researchers: if the initial DGA DNS requests do not return a valid C2 server, infected devices connect to, and request the IP address of a server from, the above-mentioned domain [2].

Connection to Proxy

After sending host and infection details via HTTP and receiving commands from the C2 server, affected devices were frequently observed initiating activity to join the Sock5Systemz botnet. Infected hosts would first make HTTP GET requests to an IP identified as Socks5Systemz’s proxy checker application, usually sending the URI “proxy-activity.txt” to the domain over the HTTP protocol. This likely represents an additional validation check to confirm that the infected device is ready to join the botnet.

Figure 5: Cyber AI Analyst investigation detailing HTTP GET requests over port 80 to the Socks5Systemz Proxy Checker Application.

Following the final validation checks, devices would then attempt TCP connections to a range of IPs, which have been associated with BackConnect proxy servers, over port 1074. At this point, the device is able to receive commands from actors who login to and operate the corresponding BackConnect server. This BackConnect server will transmit traffic from the user renting the segment of the botnet [2].

Darktrace observed a range of activity associated with this stage of the attack, including the use of new or unusual user agents, connections to suspicious IPs, and other anomalous external connectivity which represented a deviation from affected devices’ expected behavior.

Additional Activities Following Proxy Addition

The Darktrace Threat Research team found evidence of the possible deployment of additional malware strains during their investigation into devices affected by Socks5Systemz. IoCs associated with both the Amadey and PrivateLoader loader malware strains, both of which are known to distribute Socks5Systemz, were also observed on affected devices. Additionally, Darktrace observed multiple infected systems performing cryptocurrency mining operations around the time of the Sock5Systemz compromise, utilizing the MinerGate protocol to conduct login and job functions, as well as making DNS requests for mining pools.

While such behavior would fall outside of the expected activity for Socks5Systemz and cannot be definitively attributed to it, Darktrace did observe devices affected by the botnet performing additional malicious downloads and operations during its investigations.

Conclusion

Ultimately, Darktrace’s anomaly-based approach to threat detection enabled it to effectively identify and alert for malicious Socks5Systemz botnet activity long before external researchers had documented its IoCs and tactics, techniques, and procedures (TTPs).  

In fact, Darktrace not only identified multiple distinct attack phases later outlined in external research but also uncovered deviations from these expected patterns of behavior. By proactively detecting emerging threats through anomaly detection rather than relying on existing threat intelligence, Darktrace is well positioned to detect evolving threats like Socks5Systemz, regardless of what their future iterations might look like.

Faced with the threat of persistent botnets, it is crucial for organizations to detect malicious activity in its early stages before additional devices are compromised, making it increasingly difficult to remediate. Darktrace’s suite of products enables the swift and effective detection of such threats. Moreover, when enabled in autonomous response mode, Darktrace RESPOND is uniquely positioned to take immediate, targeted actions to contain these attacks from the onset.

Credit to Adam Potter, Cyber Security Analyst, Anna Gilbertson, Cyber Security Analyst

Appendices

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compromise / Beaconing Activity To External Rare
  • Compromise / DGA Beacon
  • Compromise / Beacon to Young Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Device / New User Agent
  • Device / New User Agent and New IP

Cyber AI Analyst Incidents

  • Possible HTTP Command and Control
  • Possible HTTP Command and Control to Multiple Endpoints
  • Unusual Repeated Connections
  • Unusual Repeated Connections to Multiple Endpoints
  • Multiple DNS Requests for Algorithmically Generated Domains

Indicators of Compromise

IoC - Type - Description

185.141.63[.]172 - IP Address - Socks5Systemz C2 Endpoint

193.242.211[.]141 - IP Address - Socks5Systemz C2 Endpoint

109.230.199[.]181 - IP Address - Socks5Systemz C2 Endpoint

109.236.88[.]134 - IP Address - Socks5Systemz C2 Endpoint

217.23.5[.]14 - IP Address - Socks5Systemz Proxy Checker App

88.80.148[.]8 - IP Address - Socks5Systemz Backconnect Endpoint

88.80.148[.]219 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]4 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]2 - IP Address - Socks5Systemz Backconnect Endpoint

195.154.188[.]211 - IP Address - Socks5Systemz Backconnect Endpoint

91.92.111[.]132 - IP Address - Socks5Systemz Backconnect Endpoint

91.121.30[.]185 - IP Address - Socks5Systemz Backconnect Endpoint

94.23.58[.]173 - IP Address - Socks5Systemz Backconnect Endpoint

37.187.148[.]204 - IP Address - Socks5Systemz Backconnect Endpoint

188.165.192[.]18 - IP Address - Socks5Systemz Backconnect Endpoint

/single.php?c=<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/sign/<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/proxy-activity.txt - URI - Socks5Systemz HTTP GET Request

datasheet[.]fun - Hostname - Socks5Systemz C2 Endpoint

bddns[.]cc - Hostname - Socks5Systemz C2 Endpoint

send-monitoring[.]bit - Hostname - Socks5Systemz C2 Endpoint

MITRE ATT&CK Mapping

Command and Control

T1071 - Application Layer Protocol

T1071.001 – Web protocols

T1568 – Dynamic Resolution

T1568.002 – Domain Generation Algorithms

T1132 – Data Encoding

T1132 – Non-Standard Encoding

T1090 – Proxy

T1090.002 – External Proxy

Exfiltration

T1041 – Exfiltration over C2 channel

Impact

T1496 – Resource Hijacking

References

1. https://www.bleepingcomputer.com/news/security/socks5systemz-proxy-service-infects-10-000-systems-worldwide/

2. https://www.bitsight.com/blog/unveiling-socks5systemz-rise-new-proxy-service-privateloader-and-amadey

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
Adam Potter
Senior Cyber Analyst

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July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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AI adoption in cybersecurity: Beyond the hype

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

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

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

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

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

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

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

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

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How is maturity assessed in this model?

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

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

Why Darktrace?

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

Security teams today are asking big, important questions:

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

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

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

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

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

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

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

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

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

Strategic benefits for security leaders

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

SOC fatigue is real, and AI can help

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

GenAI is error prone, requiring strong human oversight

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

AI’s real value lies in progression

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

Trust and oversight are key initially but evolves in later levels

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

People’s roles shift meaningfully

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

Outcome, not hype, defines maturity

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

[related-resource]

Outcomes across the AI Security Maturity Model

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

L0 – Manual Operations

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

L1 – Automation Rules

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

L2 – AI Assistance

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

L3 – AI Collaboration

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

L4 – AI Delegation

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

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

Join the webinar

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

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July 17, 2025

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

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

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

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

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

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

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1. Depth of data

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

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

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

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

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

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

2. Chain of custody

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

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

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

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

3. Automated collection and isolation

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

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

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

4. Ease of use

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

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

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

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

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

5. Incident preparedness

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

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

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

Cloud forensics with Darktrace

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

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

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