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February 8, 2024

How CoinLoader Hijacks Networks

Discover how Darktrace decrypted the CoinLoader malware hijacking networks for cryptomining. Learn about the tactics and protection strategies employed.
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
Signe Zaharka
Principal Cyber Analyst
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08
Feb 2024

About Loader Malware

Loader malware was a frequent topic of conversation and investigation within the Darktrace Threat Research team throughout 2023, with a wide range of existing and novel variants affecting a significant number of Darktrace customers, as detailed in Darktrace’s inaugural End of Year Threat Report. The multi-phase nature of such compromises poses a significant threat to organizations due to the need to defend against multiple threats at the same time.

CoinLoader, a variant of loader malware first observed in the wild in 2018 [1], is an example of one of the more prominent variant of loaders observed by Darktrace in 2023, with over 65 customers affected by the malware. Darktrace’s Threat Research team conducted a deep dive investigation into the patterns of behavior exhibited by devices infected with CoinLoader in the latter part of 2023, with compromises observed in Europe, the Middle East and Africa (EMEA), Asia-Pacific (APAC) and the Americas.

The autonomous threat detection capabilities of Darktrace DETECT™ allowed for the effective identification of these CoinLoader infections whilst Darktrace RESPOND™, if active, was able to quickly curtail attacker’s efforts and prevent more disruptive, and potentially costly, secondary compromises from occurring.

What is CoinLoader?

Much like other strains of loader, CoinLoader typically serves as a first stage malware that allows threat actors to gain initial access to a network and establish a foothold in the environment before delivering subsequent malicious payloads, including adware, botnets, trojans or pay-per-install campaigns.

CoinLoader is generally propagated through trojanized popular software or game installation archive files, usually in the rar or zip formats. These files tend can be easily obtained via top results displayed in search engines when searching for such keywords as "crack" or "keygen" in conjunction with the name of the software the user wishes to pirate [1,2,3,4]. By disguising the payload as a legitimate programme, CoinLoader is more likely to be unknowingly downloaded by endpoint users, whilst also bypassing traditional security measures that trust the download.

It also has several additional counter-detection methods including using junk code, variable obfuscation, and encryption for shellcode and URL schemes. It relies on dynamic-link library (DLL) search order hijacking to load malicious DLLs to legitimate executable files. The malware is also capable of performing a variety of checks for anti-virus processes and disabling endpoint protection solutions.

In addition to these counter-detection tactics, CoinLoader is also able to prevent the execution of its malicious DLL files in sandboxed environments without the presence of specific DNS cache records, making it extremely difficult for security teams and researchers to analyze.

In 2020 it was reported that CoinLoader compromises were regularly seen alongside cryptomining activity and even used the alias “CoinMiner” in some cases [2]. Darktrace’s investigations into CoinLoader in 2023 largely confirmed this theory, with around 15% of observed CoinLoader connections being related to cryptomining activity.

Cryptomining malware consumes large amounts of a hijacked (or cryptojacked) device's resources to perform complex mathematical calculations and generate income for the attacker all while quietly working in the background. Cryptojacking can lead to high electricity costs, device slow down, loss of functionality, and in the worst case scenario can be a potential fire hazard.

Darktrace Coverage of CoinLoader

In September 2023, Darktrace observed several cases of CoinLoader that served to exemplify the command-and-control (C2) communication and subsequent cryptocurrency mining activities typically observed during CoinLoader compromises. While the initial infection method in these cases was outside of Darktrace’s purview, it likely occurred via socially engineered phishing emails or, as discussed earlier, trojanized software downloads.

Command-and-Control Activity

CoinLoader compromises observed across the Darktrace customer base were typically identified by encrypted C2 connections over port 433 to rare external endpoints using self-signed certificates containing "OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US" in their issue fields.

All observed CoinLoader C2 servers were associated with the ASN of MivoCloud, a Virtual Private Server (VPS) hosting service (AS39798 MivoCloud SRL). It had been reported that Russian-state sponsored threat actors had previously abused MivoCloud’s infrastructure in order to bypass geo-blocking measures during phishing attacks against western nations [5].

Darktrace observed that the majority of CoinLoader infrastructure utilized IP addresses in the 185.225.0.0/19 range and were associated with servers hosted in Romania, with just one instance of an IP address based in Moldova. The domain names of these servers typically followed the naming pattern ‘*[a-d]{1}[.]info’, with 'ams-updatea[.]info’, ‘ams-updateb[.]info’, ‘ams-updatec[.]info’, and ‘ams-updated[.]info’ routinely identified on affected networks.

Researchers found that CoinLoader typically uses DNS tunnelling in order to covertly exchange information with attacker-controlled infrastructure, including the domains ‘candatamsnsdn[.]info’, ‘mapdatamsnsdn[.]info’, ‘rqmetrixsdn[.]info’ [4].

While Darktrace did not observe these particular domains, it did observer similar DNS lookups to a similar suspicous domain, namely ‘ucmetrixsdn[.]info’, in addition to the aforementioned HTTPS C2 connections.

Cryptomining Activity and Possible Additional Tooling

After establishing communication channels with CoinLoader servers, affected devices were observed carrying out a range of cryptocurrency mining activities. Darktrace detected devices connecting to multiple MivoCloud associated IP addresses using the MinerGate protocol alongside the credential “x”, a MinerGate credential observed by Darktrace in previous cryptojacking compromises, including the Sysrv-hello botnet.

Figure 1: Darktrace DETECT breach log showing an alerted mining activity model breach on an infected device.
Figure 2: Darktrace's Cyber AI Analyst providing details about unusual repeated connections to multiple endpoints related to CoinLoader cryptomining.

In a number of customer environments, Darktrace observed affected devices connected to endpoints associated with other malware such as the Andromeda botnet and the ViperSoftX information stealer. It was, however, not possible to confirm whether CoinLoader had dropped these additional malware variants onto infected devices.

On customer networks where Darktrace RESPOND was enabled in autonomous response mode, Darktrace was able to take swift targeted steps to shut down suspicious connections and contain CoinLoader compromises. In one example, following DETECT’s initial identification of an affected device connecting to multiple MivoCloud endpoints, RESPOND autonomously blocked the device from carrying out such connections, effectively shutting down C2 communication and preventing threat actors carrying out any cryptomining activity, or downloading subsequent malicious payloads. The autonomous response capability of RESPOND provides customer security teams with precious time to remove infected devices from their network and action their remediation strategies.

Figure 3: Darktrace RESPOND autonomously blocking CoinLoader connections on an affected device.

Additionally, customers subscribed to Darktrace’s Proactive Threat Notification (PTN) service would be alerted about potential CoinLoader activity observed on their network, prompting Darktrace’s Security Operations Center (SOC) to triage and investigate the activity, allowing customers to prioritize incidents that require immediate attention.

Conclusion

By masquerading as free or ‘cracked’ versions of legitimate popular software, loader malware like CoinLoader is able to indiscriminately target a large number of endpoint users without arousing suspicion. What’s more, once a network has been compromised by the loader, it is then left open to a secondary compromise in the form of potentially costly information stealers, ransomware or, in this case, cryptocurrency miners.

While urging employees to think twice before installing seemingly legitimate software unknown or untrusted locations is an essential first step in protecting an organization against threats like CoinLoader, its stealthy tactics mean this may not be enough.

In order to fully safeguard against such increasingly widespread yet evasive threats, organizations must adopt security solutions that are able to identify anomalies and subtle deviations in device behavior that could indicate an emerging compromise. The Darktrace suite of products, including DETECT and RESPOND, are well-placed to identify and contain these threats in the first instance and ensure they cannot escalate to more damaging network compromises.

Credit to: Signe Zaharka, Senior Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendix

Darktrace DETECT Model Detections

  • Anomalous Connection/Multiple Connections to New External TCP Port
  • Anomalous Connection/Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection/Rare External SSL Self-Signed
  • Anomalous Connection/Repeated Rare External SSL Self-Signed
  • Anomalous Connection/Suspicious Self-Signed SSL
  • Anomalous Connection/Young or Invalid Certificate SSL Connections to Rare
  • Anomalous Server Activity/Rare External from Server
  • Compromise/Agent Beacon (Long Period)
  • Compromise/Beacon for 4 Days
  • Compromise/Beacon to Young Endpoint
  • Compromise/Beaconing Activity To External Rare
  • Compromise/High Priority Crypto Currency Mining
  • Compromise/High Volume of Connections with Beacon Score
  • Compromise/Large Number of Suspicious Failed Connections
  • Compromise/New or Repeated to Unusual SSL Port
  • Compromise/Rare Domain Pointing to Internal IP
  • Compromise/Repeating Connections Over 4 Days
  • Compromise/Slow Beaconing Activity To External Rare
  • Compromise/SSL Beaconing to Rare Destination
  • Compromise/Suspicious File and C2
  • Compromise/Suspicious TLS Beaconing To Rare External
  • Device/ Anomalous Github Download
  • Device/ Suspicious Domain
  • Device/Internet Facing Device with High Priority Alert
  • Device/New Failed External Connections

Indicators of Compromise (IoCs)

IoC - Hostname C2 Server

ams-updatea[.]info

ams-updateb[.]info

ams-updatec[.]info

ams-updated[.]info

candatamsna[.]info

candatamsnb[.]info

candatamsnc[.]info

candatamsnd[.]info

mapdatamsna[.]info

mapdatamsnb[.]info

mapdatamsnc[.]info

mapdatamsnd[.]info

res-smarta[.]info

res-smartb[.]info

res-smartc[.]info

res-smartd[.]info

rqmetrixa[.]info

rqmetrixb[.]info

rqmetrixc[.]info

rqmetrixd[.]info

ucmetrixa[.]info

ucmetrixb[.]info

ucmetrixc[.]info

ucmetrixd[.]info

any-updatea[.]icu

IoC - IP Address - C2 Server

185.225[.]16.192

185.225[.]16.61

185.225[.]16.62

185.225[.]16.63

185.225[.]16.88

185.225[.]17.108

185.225[.]17.109

185.225[.]17.12

185.225[.]17.13

185.225[.]17.135

185.225[.]17.14

185.225[.]17.145

185.225[.]17.157

185.225[.]17.159

185.225[.]18.141

185.225[.]18.142

185.225[.]18.143

185.225[.]19.218

185.225[.]19.51

194.180[.]157.179

194.180[.]157.185

194.180[.]158.55

194.180[.]158.56

194.180[.]158.62

194.180[.]158.63

5.252.178[.]74

94.158.246[.]124

IoC - IP Address - Cryptocurrency mining related endpoint

185.225.17[.]114

185.225.17[.]118

185.225.17[.]130

185.225.17[.]131

185.225.17[.]132

185.225.17[.]142

IoC - SSL/TLS certificate issuer information - C2 server certificate example

emailAddress=admin@example[.]ltd,CN=example[.]ltd,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

emailAddress=admin@'res-smartd[.]info,CN=res-smartd[.]info,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

CN=ucmetrixd[.]info,OU=IT,O=MyCompany LLC,L=San Francisco,ST=California,C=US

MITRE ATT&CK Mapping

INITIAL ACCESS

Exploit Public-Facing Application - T1190

Spearphishing Link - T1566.002

Drive-by Compromise - T1189

COMMAND AND CONTROL

Non-Application Layer Protocol - T1095

Non-Standard Port - T1571

External Proxy - T1090.002

Encrypted Channel - T1573

Web Protocols - T1071.001

Application Layer Protocol - T1071

DNS - T1071.004

Fallback Channels - T1008

Multi-Stage Channels - T1104

PERSISTENCE

Browser Extensions

T1176

RESOURCE DEVELOPMENT

Web Services - T1583.006

Malware - T1588.001

COLLECTION

Man in the Browser - T1185

IMPACT

Resource Hijacking - T1496

References

1. https://www.avira.com/en/blog/coinloader-a-sophisticated-malware-loader-campaign

2. https://asec.ahnlab.com/en/17909/

3. https://www.cybereason.co.jp/blog/cyberattack/5687/

4. https://research.checkpoint.com/2023/tunnel-warfare-exposing-dns-tunneling-campaigns-using-generative-models-coinloader-case-study/

5. https://securityboulevard.com/2023/02/three-cases-of-cyber-attacks-on-the-security-service-of-ukraine-and-nato-allies-likely-by-russian-state-sponsored-gamaredon/

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
Signe Zaharka
Principal Cyber Analyst

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August 14, 2025

From Exploit to Escalation: Tracking and Containing a Real-World Fortinet SSL-VPN Attack

Fortinet SSL-VPN AttackDefault blog imageDefault blog image

Threat actors exploiting Fortinet CVEs

Over the years, Fortinet has issued multiple alerts about a wave of sophisticated attacks targeting vulnerabilities in its SSL-VPN infrastructure. Despite the release of patches to address these vulnerabilities, threat actors have continued to exploit a trio of Common Vulnerabilities and Exposures (CVEs) disclosed between 2022 and 2024 to gain unauthorized access to FortiGate devices.

Which vulnerabilities are exploited?

The vulnerabilities—CVE-2022-42475, CVE-2023-27997, and CVE-2024-21762—affect Fortinet’s SSL-VPN services and have been actively exploited by threat actors to establish initial access into target networks.

The vulnerabilities affect core components of FortiOS, allowing attackers to execute remote code on affected systems.

CVE-2022-42475

Type: Heap-Based Buffer Overflow in FortiOS SSL-VPN

Impact: Remote Code Execution (Actively Exploited)

This earlier vulnerability also targets the SSL-VPN interface and has been actively exploited in the wild. It allows attackers to execute arbitrary code remotely by overflowing a buffer in memory, often used to deploy malware or establish persistent backdoors [6].

CVE-2023-27997

Type: Heap-Based Buffer Overflow in FortiOS and FortiProxy

Impact: Remote Code Execution

This flaw exists in the SSL-VPN component of both FortiOS and FortiProxy. By exploiting a buffer overflow in the heap memory, attackers can execute malicious code remotely. This vulnerability is particularly dangerous because it can be triggered without authentication, making it ideal for an initial compromise [5].

CVE-2024-21762

Type: Out-of-Bounds Write in sslvpnd

Impact: Remote Code Execution

This vulnerability affects the SSL-VPN daemon (sslvpnd) in FortiOS. It allows unauthenticated remote attackers to send specially crafted HTTP requests that write data outside of allocated memory bounds. This can lead to arbitrary code execution, giving attackers full control over a device [4].

In short, these flaws enable remote attackers to execute arbitrary code without authentication by exploiting memory corruption issues such as buffer overflows and out-of-bounds writes. Once inside, threat actors use symbolic link (symlink) in order to maintain persistence on target devices across patches and firmware updates. This persistence then enables them to bypass security controls and manipulate firewall configurations, effectively turning patched systems into long-term footholds for deeper network compromise [1][2][3].

Darktrace’s Coverage

Darktrace detected a series of suspicious activities originating from a compromised Fortinet VPN device, including anomalous HTTP traffic, internal network scanning, and SMB reconnaissance, all indicative of post-exploitation behavior. Following initial detection by Darktrace’s real-time models, its Autonomous Response capability swiftly acted on the malicious activity, blocking suspicious connections and containing the threat before further compromise could occur.

Further investigation by Darktrace’s Threat Research team uncovered a stealthy and persistent attack that leveraged known Fortinet SSL-VPN vulnerabilities to facilitate lateral movement and privilege escalation within the network.

Phase 1: Initial Compromise – Fortinet VPN Exploitation

The attack on a Darktrace customer likely began on April 11 with the exploitation of a Fortinet VPN device running an outdated version of FortiOS. Darktrace observed a high volume of HTTP traffic originating from this device, specifically targeting internal systems. Notably, many of these requests were directed at the /cgi-bin/ directory,  a common target for attackers attempting to exploit web interfaces to run unauthorized scripts or commands. This pattern strongly indicated remote code execution attempts via the SSL-VPN interface [7].

Once access was gained, the threat actor likely modified existing firewall rules, a tactic often used to disable security controls or create hidden backdoors for future access. While Darktrace does not have direct visibility into firewall configuration changes, the surrounding activity and post-exploitation behavior indicated that such modifications were made to support long-term persistence within the network.

HTTP activity from the compromised Fortinet device, including repeated requests to /cgi-bin/ over port 8080.
Figure 1: HTTP activity from the compromised Fortinet device, including repeated requests to /cgi-bin/ over port 8080

Phase 2: Establishing Persistence & Lateral Movement

Shortly after the initial compromise of the Fortinet VPN device, the threat actor began to expand their foothold within the internal network. Darktrace detected initial signs of network scanning from this device, including the use of Nmap to probe the internal environment, likely in an attempt to identify accessible services and vulnerable systems.

Darktrace’s detection of unusual network scanning activities on the affected device.
Figure 2: Darktrace’s detection of unusual network scanning activities on the affected device.

Around the same time, Darktrace began detecting anomalous activity on a second device, specifically an internal firewall interface device. This suggested that the attacker had established a secondary foothold and was leveraging it to conduct deeper reconnaissance and move laterally through the network.

In an effort to maintain persistence within the network, the attackers likely deployed symbolic links in the SSL-VPN language file directory on the Fortinet device. While Darktrace did not directly observe symbolic link abuse, Fortinet has identified this as a known persistence technique in similar attacks [2][3]. Based on the observed post-exploitation behavior and likely firewall modifications, it is plausible that such methods were used here.

Phase 3: Internal Reconnaissance & Credential Abuse

With lateral movement initiated from the internal firewall interface device, the threat actor proceeded to escalate their efforts to map the internal network and identify opportunities for privilege escalation.

Darktrace observed a successful NTLM authentication from the internal firewall interface to the domain controller over the outdated protocol SMBv1, using the account ‘anonymous’. This was immediately followed by a failed NTLM session connection using the hostname ‘nmap’, further indicating the use of Nmap for enumeration and brute-force attempts. Additional credential probes were also identified around the same time, including attempts using the credential ‘guest’.

Darktrace detection of a series of login attempts using various credentials, with a mix of successful and unsuccessful attempts.
Figure 3: Darktrace detection of a series of login attempts using various credentials, with a mix of successful and unsuccessful attempts.

The attacker then initiated DCE_RPC service enumeration, with over 300 requests to the Endpoint Mapper endpoint on the domain controller. This technique is commonly used to discover available services and their bindings, often as a precursor to privilege escalation or remote service manipulation.

Over the next few minutes, Darktrace detected more than 1,700 outbound connections from the internal firewall interface device to one of the customer’s subnets. These targeted common services such as FTP (port 21), SSH (22), Telnet (23), HTTP (80), and HTTPS (443). The threat actor also probed administrative and directory services, including ports 135, 137, 389, and 445, as well as remote access via RDP on port 3389.

Further signs of privilege escalation attempts were observed with the detection of over 300 Netlogon requests to the domain controller. Just over half of these connections were successful, indicating possible brute-force authentication attempts, credential testing, or the use of default or harvested credentials.

Netlogon and DCE-RPC activity from the affected device, showing repeated service bindings to epmapper and Netlogon, followed by successful and failed NetrServerAuthenticate3 attempts.
Figure 4: Netlogon and DCE-RPC activity from the affected device, showing repeated service bindings to epmapper and Netlogon, followed by successful and failed NetrServerAuthenticate3 attempts.

Phase 4: Privilege Escalation & Remote Access

A few minutes later, the attacker initiated an RDP session from the internal firewall interface device to an internal server. The session lasted over three hours, during which more than 1.5MB of data was uploaded and over 5MB was downloaded.

Notably, no RDP cookie was observed during this session, suggesting manual access, tool-less exploitation, or a deliberate attempt to evade detection. While RDP cookie entries were present on other occasions, none were linked to this specific session—reinforcing the likelihood of stealthy remote access.

Additionally, multiple entries during and after this session show SSL certificate validation failures on port 3389, indicating that the RDP connection may have been established using self-signed or invalid certificates, a common tactic in unauthorized or suspicious remote access scenarios.

Darktrace’s detection of an RDP session from the firewall interface device to the server, lasting over 3 hours.
Figure 5: Darktrace’s detection of an RDP session from the firewall interface device to the server, lasting over 3 hours.

Darktrace Autonomous Response

Throughout the course of this attack, Darktrace’s Autonomous Response capability was active on the customer’s network. This enabled Darktrace to autonomously intervene by blocking specific connections and ports associated with the suspicious activity, while also enforcing a pre-established “pattern of life” on affected devices to ensure they were able to continue their expected business activities while preventing any deviations from it. These actions were crucial in containing the threat and prevent further lateral movement from the compromised device.

Darktrace’s Autonomous Response targeted specific connections and restricted affected devices to their expected patterns of life.
Figure 6: Darktrace’s Autonomous Response targeted specific connections and restricted affected devices to their expected patterns of life.

Conclusion

This incident highlights the importance of important staying on top of patching and closely monitoring VPN infrastructure, especially for internet-facing systems like Fortinet devices. Despite available patches, attackers were still able to exploit known vulnerabilities to gain access, move laterally and maintain persistence within the customer’s network.

Attackers here demonstrated a high level of stealth and persistence. Not only did they gain access to the network and carry out network scans and lateral movement, but they also used techniques such as symbolic link abuse, credential probing, and RDP sessions without cookies to avoid detection.  Darktrace’s detection of the post-exploitation activity, combined with the swift action of its Autonomous Response technology, successfully blocked malicious connections and contained the attack before it could escalate

Credit to Priya Thapa (Cyber Analyst), Vivek Rajan (Cyber Analyst), and Ryan Traill (Analyst Content Lead)

Appendices

Real-time Detection Model Alerts

·      Device / Suspicious SMB Scanning Activity

·      Device / Anomalous Nmap Activity

·      Device / Network Scan

·      Device / RDP Scan

·      Device / ICMP Address Scan

Autonomous Response Model Alerts:  

·      Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK Mapping

Initial Access – External Remote Services – T1133

Initial Access – Valid Accounts – T1078

Execution – Exploitation for Client Execution – T1203

Persistence – Account Manipulation – T1098

Persistence – Application Layer Protocol – T1071.001

Privilege Escalation – Exploitation for Privilege Escalation – T1068

Privilege Escalation – Valid Accounts – T1078

Defense Evasion – Masquerading – T1036

Credential Access – Brute Force – T1110

Discovery – Network Service Scanning – T1046

Discovery – Remote System Discovery – T1018

Lateral Movement – Remote Services – T1021

Lateral Movement – Software Deployment Tools – T1072

Collection – Data from Local System – T1005

Collection – Data Staging – T1074

Exfiltration – Exfiltration Over Alternative Protocol – T1048

References

[1]  https://www.tenable.com/blog/cve-2024-21762-critical-fortinet-fortios-out-of-bound-write-ssl-vpn-vulnerability

[2] https://thehackernews.com/2025/04/fortinet-warns-attackers-retain.html

[3] https://www.cisa.gov/news-events/alerts/2025/04/11/fortinet-releases-advisory-new-post-exploitation-technique-known-vulnerabilities

[4] https://www.fortiguard.com/psirt/FG-IR-24-015

[5] https://www.tenable.com/blog/cve-2023-27997-heap-based-buffer-overflow-in-fortinet-fortios-and-fortiproxy-ssl-vpn-xortigate

[6]  https://www.tenable.com/blog/cve-2022-42475-fortinet-patches-zero-day-in-fortios-ssl-vpns

[7] https://www.fortiguard.com/encyclopedia/ips/12475

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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Priya Thapa
Cyber Analyst

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August 14, 2025

How Organizations are Addressing Cloud Investigation and Response

Cloud investigation and responseDefault blog imageDefault blog image

Why cloud investigation and response needs to evolve

As organizations accelerate their move to the cloud, they’re confronting two interrelated pressures: a rapidly expanding attack surface and rising regulatory scrutiny. The dual pressure is forcing security practitioners to evolve their strategies in the cloud, particularly around investigation and response, where we see analysts spending the most time. This work is especially difficult in the cloud, often requiring experienced analysts to manually stitch together evidence across fragmented systems, unfamiliar platforms, and short-lived assets.

However, adapting isn’t easy. Many teams are operating with limited budgets and face a shortage of cloud-specific security talent. That’s why more organizations are now prioritizing tools that not only deliver deep visibility and rapid response in the cloud, but also help upskill their analysts to keep pace with threats and compliance demands.

Our 2024 survey report highlights just how organizations are recognizing gaps in their cloud security, feeling the heat from regulators, and making significant investments to bolster their cloud investigation capabilities.

In this blog post, we’ll explore the current challenges, approaches, and strategies organizations are employing to enhance their cloud investigation and incident response.

Recognizing the gaps in current cloud investigation and response methods

Complex environments & static tools

Due to the dynamic nature of cloud infrastructure, ephemeral assets, autoscaling environments, and multi-cloud complexity, traditional investigation and response methods which rely on static snapshots and point-in-time data, are fundamentally mismatched. And with Cloud environment APIs needing deep provider knowledge and scripting skills to extract much needed evidence its unrealistic for one person to master all aspects of cloud incident response.

Analysts are still stitching together logs from fragmented systems, manually correlating events, and relying on post-incident forensics that often arrive too late to drive meaningful response. These approaches were built for environments that rarely changed. In the cloud, where assets may only exist for minutes and attacker movement can span regions or accounts in seconds, point-in-time visibility simply can’t keep up. As a result, critical evidence is missed, timelines are incomplete, and investigations drag on longer than they should.

Even some modern approaches still depend heavily on static configurations, delayed snapshots, or siloed visibility that can’t keep pace with real-time attacker movement.

There is even the problem of  identifying what cloud data sources hold the valuable information needed to investigate in the first place. With AWS alone having over 200 products, each with its own security practices and data sources.It can be challenging to identify where you need to be looking.  

To truly secure the cloud, investigation and response must be continuous, automated, and context-rich. Tools should be able to surface the signal from the noise and support analysts at every step, even without deep forensics expertise.

Increasing compliance pressure

With the rise of data privacy regulations and incident reporting mandates worldwide, organizations face heightened scrutiny. Noncompliance can lead to severe penalties, making it crucial to have robust cloud investigation and response mechanisms in place. 74% of organizations surveyed reported that data privacy regulations complicate incident response, underscoring the urgency to adapt to regulatory requirements.

In addition, a majority of organizations surveyed (89%) acknowledged that they suffer damage before they can fully contain and investigate incidents, particularly in cloud environments, highlighting the need for enhanced cloud capabilities.  

Enhancing cloud investigation and response

To address these challenges, organizations are actively growing their capabilities to perform investigations in the cloud. Key steps include:

Allocating and increasing budgets:  

Recognizing the importance of cloud-specific investigation tools, many organizations have started to allocate dedicated budgets for cloud forensics. 83% of organizations have budgeted for cloud forensics, with 77% expecting this budget to increase. This reflects a strong commitment to improving cloud security.

Implementing automation that understands cloud behavior

Automation isn’t just about speeding up tasks. While modern threats require speed and efficiency from defenders, automation aims to achieve this by enabling consistent decision making across unique and dynamic environments. Traditional SOAR platforms, often designed for static on-prem environments, struggle to keep pace with the dynamic and ephemeral nature of the cloud, where resources can disappear before a human analyst even has a chance to look at them. Cloud-native automation, designed to act on transient infrastructure and integrate seamlessly with cloud APIs, is rapidly emerging as the more effective approach for real-time investigation and response. Automation can cover collection, processing, and storage of incident evidence without ever needing to wait for human intervention and the evidence is ready and waiting all in once place, regardless of if the evidence is cloud-provider logs, disk images, or  memory dumps. With the right automation tools you can even go further and automate the full process from end to end covering acquisition, processing, analysis, and response.

Artificial Intelligence (AI) that augments analysts’ intuition not just adds speed

While many vendors tout AI’s ability to “analyze large volumes of data,” that’s table stakes. The real differentiator is how AI understands the narrative of an incident, surfacing high-fidelity alerts, correlating attacker movement across cloud and hybrid environments, and presenting findings in a way that upskills rather than overwhelms analysts.  

In this space, AI isn’t just accelerating investigations, it’s democratizing them by reducing the reliance on highly specialized forensic expertise.  

Strategies for effective cloud investigation and response

Organizations are also exploring various strategies to optimize their cloud investigation and response capabilities:

Enhancing visibility and control:

  • Unified platforms: Implementing platforms that provide a unified view across multiple cloud environments can help organizations achieve better visibility and control. This consolidation reduces the complexity of managing disparate tools and data sources.
  • Improved integration: Ensuring that all security tools and platforms are seamlessly integrated is critical. This integration facilitates better data sharing and cohesive incident management.
  • Cloud specific expertise: Training and Recruitment: Investing in training programs to develop cloud-specific skills among existing staff and recruiting experts with cloud security knowledge can bridge the skill gap.
  • Continuous learning: Given the constantly evolving nature of cloud threats, continuous learning and adaptation are essential for maintaining effective security measures.

Leveraging automation and AI:

  • Automation solutions: Automation solutions for cloud environments can significantly speed up and simplify incident response efficiency. These solutions can handle repetitive tasks, allowing security teams to focus on more complex issues.
  • AI powered analysis: AI can assist in rapidly analyzing incident data, identifying anomalies, and predicting potential threats. This proactive approach can help prevent incidents before they escalate.

Cloud investigation and response with Darktrace

Darktrace’s  forensic acquisition & investigation capabilities helps organizations address the complexities of cloud investigations and incident response with ease. The product seamlessly integrates with AWS, GCP, and Azure, consolidating data from multiple cloud environments into one unified platform. This integration enhances visibility and control, making it easier to manage and respond to incidents across diverse cloud infrastructures.

By leveraging machine learning and automation, Forensic Acquisition & Investigation accelerates the investigation process by quickly analyzing vast amounts of data, identifying patterns, and providing actionable insights. Automation reduces manual effort and response times, allowing your security team to focus on the most pressing issues.

Forensic Acquisition & Investigation can help you stay ahead of threats whilst also meeting regulatory requirements, helping you to maintain a robust cloud security position.

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About the author
Calum Hall
Technical Content Researcher
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