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October 26, 2022

Strategies to Prolong Quantum Ransomware Attacks

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Oct 2022
Learn more about how Darktrace combats Quantum Ransomware changing strategy for cyberattacks. Explore the power of AI-driven network cyber security!

Within science and engineering, the word ‘quantum’ may spark associations with speed and capability, referencing a superior computer that can perform tasks a classical computer cannot. In cyber security, some may recognize ‘quantum’ in relation to cryptography or, more recently, as the name of a new ransomware group, which achieved network-wide encryption a mere four hours after an initial infection.   

Although this group now has a reputation for carrying out fast and efficient attacks, speed is not their only tactic. In August 2022, Darktrace detected a Quantum Ransomware incident where attackers remained in the victim’s network for almost a month after the initial signs of infection, before detonating ransomware. This was a stark difference to previously reported attacks, demonstrating that as motives change, so do threat actors’ strategies. 

The Quantum Group

Quantum was first identified in August 2021 as the latest of several rebrands of MountLocker ransomware [1]. As part of this rebrand, the extension ‘.quantum’ is appended to filenames that are encrypted and the associated ransom notes are named ‘README_TO_DECRYPT.html’ [2].  

From April 2022, media coverage of this group has increased following a DFIR report detailing an attack that progressed from initial access to domain-wide ransomware within four hours [3]. To put this into perspective, the global median dwell time for ransomware in 2020 and 2021 is 5 days [4]. In the case of Quantum, threat actors gained direct keyboard access to devices merely 2 hours after initial infection. The ransomware was staged on the domain controller around an hour and a half later, and executed 12 minutes after that.   

Quantum’s behaviour bears similarities to other groups, possibly due to their history and recruitment. Several members of the disbanded Conti ransomware group are reported to have joined the Quantum and BumbleBee operations. Security researchers have also identified similarities in the payloads and C2 infrastructure used by these groups [5 & 6].  Notably, these are the IcedID initial payload and Cobalt Strike C2 beacon used in this attack. Darktrace has also observed and prevented IcedID and Cobalt Strike activity from BumbleBee across several customer environments.

The Attack

From 11th July 2022, a device suspected to be patient zero made repeated DNS queries for external hosts that appear to be associated with IcedID C2 traffic [7 & 8]. In several reported cases [9 & 10], this banking trojan is delivered through a phishing email containing a malicious attachment that loads an IcedID DLL. As Darktrace was not deployed in the prospect’s email environment, there was no visibility of the initial access vector, however an example of a phishing campaign containing this payload is presented below. It is also possible that the device was already infected prior to joining the network. 

Figure 1- An example phishing email used to distribute IcedID. If configured, Darktrace/Email would be able to detect that the email was sent from an anomalous sender, was part of a fake reply chain, and had a suspicious attachment containing compressed content of unusual mime type [11].    

 

Figure 2- The DNS queries to endpoints associated with IcedID C2 servers, taken from the infected device’s event log.  Additional DNS queries made to other IcedID C2 servers are in the list of IOCs in the appendices.  The repeated DNS queries are indicative of beaconing.


It was not until 22nd July that activity was seen which indicated the attack had progressed to the next stage of the kill chain. This contrasts the previously seen attacks where the progression to Cobalt Strike C2 beaconing and reconnaissance and lateral movement occurred within 2 hours of the initial infection [12 & 13]. In this case, patient zero initiated numerous unusual connections to other internal devices using a compromised account, connections that were indicative of reconnaissance using built-in Windows utilities:

·      DNS queries for hostnames in the network

·      SMB writes to IPC$ shares of those hostnames queried, binding to the srvsvc named pipe to enumerate things such as SMB shares and services on a device, client access permissions on network shares and users logged in to a remote session

·      DCE-RPC connections to the endpoint mapper service, which enables identification of the ports assigned to a particular RPC service

These connections were initiated using an existing credential on the device and just like the dwelling time, differed from previously reported Quantum group attacks where discovery actions were spawned and performed automatically by the IcedID process [14]. Figure 3 depicts how Darktrace detected that this activity deviated from the device’s normal behaviour.  

Figure 3- This figure displays the spike in active internal connections initiated by patient zero. The coloured dots represent the Darktrace models that were breached, detecting this unusual reconnaissance and lateral movement activity.

Four days later, on the 26th of July, patient zero performed SMB writes of DLL and MSI executables to the C$ shares of internal devices including domain controllers, using a privileged credential not previously seen on the patient zero device. The deviation from normal behaviour that this represents is also displayed in Figure 3. Throughout this activity, patient zero made DNS queries for the external Cobalt Strike C2 server shown in Figure 4. Cobalt Strike has often been seen as a secondary payload delivered via IcedID, due to IcedID’s ability to evade detection and deploy large scale campaigns [15]. It is likely that reconnaissance and lateral movement was performed under instructions received by the Cobalt Strike C2 server.   

Figure 4- This figure is taken from Darktrace’s Advanced Search interface, showing a DNS query for a Cobalt Strike C2 server occurring during SMB writes of .dll files and DCE-RPC requests to the epmapper service, demonstrating reconnaissance and lateral movement.


The SMB writes to domain controllers and usage of a new account suggests that by this stage, the attacker had achieved domain dominance. The attacker also appeared to have had hands-on access to the network via a console; the repetition of the paths ‘programdata\v1.dll’ and ‘ProgramData\v1.dll’, in lower and title case respectively, suggests they were entered manually.  

These DLL files likely contained a copy of the malware that injects into legitimate processes such as winlogon, to perform commands that call out to C2 servers [16]. Shortly after the file transfers, the affected domain controllers were also seen beaconing to external endpoints (‘sezijiru[.]com’ and ‘gedabuyisi[.]com’) that OSINT tools have associated with these DLL files [17 & 18]. Moreover, these SSL connections were made using a default client fingerprint for Cobalt Strike [19], which is consistent with the initial delivery method. To illustrate the beaconing nature of these connections, Figure 5 displays the 4.3 million daily SSL connections to one of the C2 servers during the attack. The 100,000 most recent connections were initiated by 11 unique source IP addresses alone.

Figure 5- The Advanced Search interface, querying for external SSL connections from devices in the network to an external host that appears to be a Cobalt Strike C2 server. 4.3 million connections were made over 8 days, even after the ransomware was eventually detonated on 2022-08-03.


Shortly after the writes, the attack progressed to the penultimate stage. The next day, on the 27th of July, the attackers moved to achieve their first objective: data exfiltration. Data exfiltration is not always performed by the Quantum ransomware gang. Researchers have noted discrepancies between claims of data theft made in their ransom notes versus the lack of data seen leaving the network, although this may have been missed due to covert exfiltration via a Cobalt Strike beacon [20]. 

In contrast, this attack displayed several gigabytes of data leaving internal devices including servers that had previously beaconed to Cobalt Strike C2 servers. This data was transferred overtly via FTP, however the attacker still attempted to conceal the activity using ephemeral ports (FTP in EPSV mode). FTP is an effective method for attackers to exfiltrate large files as it is easy to use, organizations often neglect to monitor outbound usage, and it can be shipped through ports that will not be blocked by traditional firewalls [21].   

Figure 6 displays an example of the FTP data transfer to attacker-controlled infrastructure, in which the destination share appears structured to identify the organization that the data was stolen from, suggesting there may be other victim organizations’ data stored. This suggests that data exfiltration was an intended outcome of this attack. 

Figure 6- This figure is from Darktrace’s Advanced Search interface, displaying some of the data transferred from an internal device to the attacker’s FTP server.

 
Data was continuously exfiltrated until a week later when the final stage of the attack was achieved and Quantum ransomware was detonated. Darktrace detected the following unusual SMB activity initiated from the attacker-created account that is a hallmark for ransomware (see Figure 7 for example log):

·      Symmetric SMB Read to Write ratio, indicative of active encryption

·      Sustained MIME type conversion of files, with the extension ‘.quantum’ appended to filenames

·      SMB writes of a ransom note ‘README_TO_DECRYPT.html’ (see Figure 8 for an example note)

Figure 7- The Model Breach Event Log for a device that had files encrypted by Quantum ransomware, showing the reads and writes of files with ‘.quantum’ appended to encrypted files, and an HTML ransom note left where the files were encrypted.

 

Figure 8- An example of the ransom note left by the Quantum gang, this one is taken from open-sources [22].


The example in Figure 8 mentions that the attacker also possessed large volumes of victim data.  It is likely that the gigabytes of data exfiltrated over FTP were leveraged as blackmail to further extort the victim organization for payment.  

Darktrace Coverage

 

Figure 9- Timeline of Quantum ransomware incident


If Darktrace/Email was deployed in the prospect’s environment, the initial payload (if delivered through a phishing email) could have been detected and held from the recipient’s inbox. Although DETECT identified anomalous network behaviour at each stage of the attack, since the incident occurred during a trial phase where Darktrace could only detect but not respond, the attack was able to progress through the kill chain. If RESPOND/Network had been configured in the targeted environment, the unusual connections observed during the initial access, C2, reconnaissance and lateral movement stages of the attack could have been blocked. This would have prevented the attackers from delivering the later stage payloads and eventual ransomware into the target network.

It is often thought that a properly implemented backup strategy is sufficient defense against ransomware [23], however as discussed in a previous Darktrace blog, the increasing frequency of double extortion attacks in a world where ‘data is the new oil’ demonstrates that backups alone are not a mitigation for the risk of a ransomware attack [24]. Equally, the lack of preventive defenses in the target’s environment enabled the attacker’s riskier decision to dwell in the network for longer and allowed them to optimize their potential reward. 

Recent crackdowns from law enforcement on ransomware groups have shifted these groups’ approaches to aim for a balance between low risk and significant financial rewards [25]. However, given the Quantum gang only have a 5% market share in Q2 2022, compared to the 13.2% held by LockBit and 16.9% held by BlackCat [26], a riskier strategy may be favourable, as a longer dwell time and double extortion outcome offers a ‘belt and braces’ approach to maximizing the rewards from carrying out this attack. Alternatively, the gaps in-between the attack stages may imply that more than one player was involved in this attack, although this group has not been reported to operate a franchise model before [27]. Whether assisted by others or driving for a risk approach, it is clear that Quantum (like other actors) are continuing to adapt to ensure their financial success. They will continue to be successful until organizations dedicate themselves to ensuring that the proper data protection and network security measures are in place. 

Conclusion 

Ransomware has evolved over time and groups have merged and rebranded. However, this incident of Quantum ransomware demonstrates that regardless of the capability to execute a full attack within hours, prolonging an attack to optimize potential reward by leveraging double extortion tactics is sometimes still the preferred action. The pattern of network activity mirrors the techniques used in other Quantum attacks, however this incident lacked the continuous progression of the group’s attacks reported recently and may represent a change of motives during the process. Knowing that attacker motives can change reinforces the need for organizations to invest in preventative controls- an organization may already be too far down the line if it is executing its backup contingency plans. Darktrace DETECT/Network had visibility over both the early network-based indicators of compromise and the escalation to the later stages of this attack. Had Darktrace also been allowed to respond, this case of Quantum ransomware would also have had a very short dwell time, but a far better outcome for the victim.

Thanks to Steve Robinson for his contributions to this blog.

Appendices

References

[1] https://community.ibm.com/community/user/security/blogs/tristan-reed/2022/07/13/ibm-security-reaqta-vs-quantum-locker-ransomware

 

[2] https://www.bleepingcomputer.com/news/security/quantum-ransomware-seen-deployed-in-rapid-network-attacks/

 

[3], [12], [14], [16], [20] https://thedfirreport.com/2022/04/25/quantum-ransomware/

 

[4] https://www.mandiant.com/sites/default/files/2022-04/M-Trends%202022%20Executive%20Summary.pdf

 

[5] https://cyware.com/news/over-650-healthcare-organizations-affected-by-the-quantum-ransomware-attack-d0e776bb/

 

[6] https://www.kroll.com/en/insights/publications/cyber/bumblebee-loader-linked-conti-used-in-quantum-locker-attacks

 

[7] https://github.com/pan-unit42/tweets/blob/master/2022-06-28-IOCs-for-TA578-IcedID-Cobalt-Strike-and-DarkVNC.txt 

 

[8] https://github.com/stamparm/maltrail/blob/master/trails/static/malware/icedid.txt

 

[9], [15] https://www.cynet.com/blog/shelob-moonlight-spinning-a-larger-web-from-icedid-to-conti-a-trojan-and-ransomware-collaboration/

 

[10] https://www.microsoft.com/security/blog/2021/04/09/investigating-a-unique-form-of-email-delivery-for-icedid-malware/

 

[11] https://twitter.com/0xToxin/status/1564289244084011014

 

[13], [27] https://cybernews.com/security/quantum-ransomware-gang-fast-and-furious/

 

[17] https://www.virustotal.com/gui/domain/gedabuyisi.com/relations

 

[18] https://www.virustotal.com/gui/domain/sezijiru.com/relations.

 

[19] https://github.com/ByteSecLabs/ja3-ja3s-combo/blob/master/master-list.txt 

 

[21] https://www.darkreading.com/perimeter/ftp-hacking-on-the-rise

 

[22] https://www.pcrisk.com/removal-guides/23352-quantum-ransomware

 

[23] https://www.cohesity.com/resource-assets/tip-sheet/5-ways-ransomware-renders-backup-useless-tip-sheet-en.pdf

 

[24] https://www.forbes.com/sites/nishatalagala/2022/03/02/data-as-the-new-oil-is-not-enough-four-principles-for-avoiding-data-fires/ 

 

[25] https://www.bleepingcomputer.com/news/security/access-to-hacked-corporate-networks-still-strong-but-sales-fall/

 

[26] https://www.bleepingcomputer.com/news/security/ransom-payments-fall-as-fewer-victims-choose-to-pay-hackers/ 

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.
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Nicole Wong
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January 29, 2025

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Inside the SOC

Bytesize Security: Insider Threats in Google Workspace

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What is an insider threat?

An insider threat is a cyber risk originating from within an organization. These threats can involve actions such as an employee inadvertently clicking on a malicious link (e.g., a phishing email) or an employee with malicious intent conducting data exfiltration for corporate sabotage.

Insiders often exploit their knowledge and access to legitimate corporate tools, presenting a continuous risk to organizations. Defenders must protect their digital estate against threats from both within and outside the organization.

For example, in the summer of 2024, Darktrace / IDENTITY successfully detected a user in a customer environment attempting to steal sensitive data from a trusted Google Workspace service. Despite the use of a legitimate and compliant corporate tool, Darktrace identified anomalies in the user’s behavior that indicated malicious intent.

Attack overview: Insider threat

In June 2024, Darktrace detected unusual activity involving the Software-as-a-Service (SaaS) account of a former employee from a customer organization. This individual, who had recently left the company, was observed downloading a significant amount of data in the form of a “.INDD” file (an Adobe InDesign document typically used to create page layouts [1]) from Google Drive.

While the use of Google Drive and other Google Workspace platforms was not unexpected for this employee, Darktrace identified that the user had logged in from an unfamiliar and suspicious IPv6 address before initiating the download. This anomaly triggered a model alert in Darktrace / IDENTITY, flagging the activity as potentially malicious.

A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.
Figure 1: A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.

Following this detection, the customer reached out to Darktrace’s Security Operations Center (SOC) team via the Security Operations Support service for assistance in triaging and investigating the incident further. Darktrace’s SOC team conducted an in-depth investigation, enabling the customer to identify the exact moment of the file download, as well as the contents of the stolen documents. The customer later confirmed that the downloaded files contained sensitive corporate data, including customer details and payment information, likely intended for reuse or sharing with a new employer.

In this particular instance, Darktrace’s Autonomous Response capability was not active, allowing the malicious insider to successfully exfiltrate the files. If Autonomous Response had been enabled, Darktrace would have immediately acted upon detecting the login from an unusual (in this case 100% rare) location by logging out and disabling the SaaS user. This would have provided the customer with the necessary time to review the activity and verify whether the user was authorized to access their SaaS environments.

Conclusion

Insider threats pose a significant challenge for traditional security tools as they involve internal users who are expected to access SaaS platforms. These insiders have preexisting knowledge of the environment, sensitive data, and how to make their activities appear normal, as seen in this case with the use of Google Workspace. This familiarity allows them to avoid having to use more easily detectable intrusion methods like phishing campaigns.

Darktrace’s anomaly detection capabilities, which focus on identifying unusual activity rather than relying on specific rules and signatures, enable it to effectively detect deviations from a user’s expected behavior. For instance, an unusual login from a new location, as in this example, can be flagged even if the subsequent malicious activity appears innocuous due to the use of a trusted application like Google Drive.

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

Appendices

Darktrace Model Detections

SaaS / Resource::Unusual Download Of Externally Shared Google Workspace File

References

[1]https://www.adobe.com/creativecloud/file-types/image/vector/indd-file.html

MITRE ATT&CK Mapping

Technqiue – Tactic – ID

Data from Cloud Storage Object – COLLECTION -T1530

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Vivek Rajan
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January 28, 2025

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Reimagining Your SOC: How to Achieve Proactive Network Security

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Introduction: Challenges and solutions to SOC efficiency

For Security Operation Centers (SOCs), reliance on signature or rule-based tools – solutions that are always chasing the latest update to prevent only what is already known – creates an excess of false positives. SOC analysts are therefore overwhelmed by a high volume of context-lacking alerts, with human analysts able to address only about 10% due to time and resource constraints. This forces many teams to accept the risks of addressing only a fraction of the alerts while novel threats go completely missed.

74% of practitioners are already grappling with the impact of an AI-powered threat landscape, which amplifies challenges like tool sprawl, alert fatigue, and burnout. Thus, achieving a resilient network, where SOC teams can spend most of their time getting proactive and stopping threats before they occur, feels like an unrealistic goal as attacks are growing more frequent.

Despite advancements in security technology (advanced detection systems with AI, XDR tools, SIEM aggregators, etc...), practitioners are still facing the same issues of inefficiency in their SOC, stopping them from becoming proactive. How can they select security solutions that help them achieve a proactive state without dedicating more human hours and resources to managing and triaging alerts, tuning rules, investigating false positives, and creating reports?

To overcome these obstacles, organizations must leverage security technology that is able to augment and support their teams. This can happen in the following ways:

  1. Full visibility across the modern network expanding into hybrid environments
  2. Have tools that identifies and stops novel threats autonomously, without causing downtime
  3. Apply AI-led analysis to reduce time spent on manual triage and investigation

Your current solutions might be holding you back

Traditional cybersecurity point solutions are reliant on using global threat intelligence to pattern match, determine signatures, and consequently are chasing the latest update to prevent only what is known. This means that unknown threats will evade detection until a patient zero is identified. This legacy approach to threat detection means that at least one organization needs to be ‘patient zero’, or the first victim of a novel attack before it is formally identified.

Even the point solutions that claim to use AI to enhance threat detection rely on a combination of supervised machine learning, deep learning, and transformers to

train and inform their systems. This entails shipping your company’s data out to a large data lake housed somewhere in the cloud where it gets blended with attack data from thousands of other organizations. The resulting homogenized dataset gets used to train AI systems — yours and everyone else’s — to recognize patterns of attack based on previously encountered threats.

While using AI in this way reduces the workload of security teams who would traditionally input this data by hand, it emanates the same risk – namely, that AI systems trained on known threats cannot deal with the threats of tomorrow. Ultimately, it is the unknown threats that bring down an organization.

The promise and pitfalls of XDR in today's threat landscape

Enter Extended Detection and Response (XDR): a platform approach aimed at unifying threat detection across the digital environment. XDR was developed to address the limitations of traditional, fragmented tools by stitching together data across domains, providing SOC teams with a more cohesive, enterprise-wide view of threats. This unified approach allows for improved detection of suspicious activities that might otherwise be missed in siloed systems.

However, XDR solutions still face key challenges: they often depend heavily on human validation, which can aggravate the already alarmingly high alert fatigue security analysts experience, and they remain largely reactive, focusing on detecting and responding to threats rather than helping prevent them. Additionally, XDR frequently lacks full domain coverage, relying on EDR as a foundation and are insufficient in providing native NDR capabilities and visibility, leaving critical gaps that attackers can exploit. This is reflected in the current security market, with 57% of organizations reporting that they plan to integrate network security products into their current XDR toolset[1].

Why settling is risky and how to unlock SOC efficiency

The result of these shortcomings within the security solutions market is an acceptance of inevitable risk. From false positives driving the barrage of alerts, to the siloed tooling that requires manual integration, and the lack of multi-domain visibility requiring human intervention for business context, security teams have accepted that not all alerts can be triaged or investigated.

While prioritization and processes have improved, the SOC is operating under a model that is overrun with alerts that lack context, meaning that not all of them can be investigated because there is simply too much for humans to parse through. Thus, teams accept the risk of leaving many alerts uninvestigated, rather than finding a solution to eliminate that risk altogether.

Darktrace / NETWORK is designed for your Security Operations Center to eliminate alert triage with AI-led investigations , and rapidly detect and respond to known and unknown threats. This includes the ability to scale into other environments in your infrastructure including cloud, OT, and more.

Beyond global threat intelligence: Self-Learning AI enables novel threat detection & response

Darktrace does not rely on known malware signatures, external threat intelligence, historical attack data, nor does it rely on threat trained machine learning to identify threats.

Darktrace’s unique Self-learning AI deeply understands your business environment by analyzing trillions of real-time events that understands your normal ‘pattern of life’, unique to your business. By connecting isolated incidents across your business, including third party alerts and telemetry, Darktrace / NETWORK uses anomaly chains to identify deviations from normal activity.

The benefit to this is that when we are not predefining what we are looking for, we can spot new threats, allowing end users to identify both known threats and subtle, never-before-seen indicators of malicious activity that traditional solutions may miss if they are only looking at historical attack data.

AI-led investigations empower your SOC to prioritize what matters

Anomaly detection is often criticized for yielding high false positives, as it flags deviations from expected patterns that may not necessarily indicate a real threat or issues. However, Darktrace applies an investigation engine to automate alert triage and address alert fatigue.

Darktrace’s Cyber AI Analyst revolutionizes security operations by conducting continuous, full investigations across Darktrace and third-party alerts, transforming the alert triage process. Instead of addressing only a fraction of the thousands of daily alerts, Cyber AI Analyst automatically investigates every relevant alert, freeing up your team to focus on high-priority incidents and close security gaps.

Powered by advanced machine-learning techniques, including unsupervised learning, models trained by expert analysts, and tailored security language models, Cyber AI Analyst emulates human investigation skills, testing hypotheses, analyzing data, and drawing conclusions. According to Darktrace Internal Research, Cyber AI Analyst typically provides a SOC with up to  50,000 additional hours of Level 2 analysis and written reporting annually, enriching security operations by producing high level incident alerts with full details so that human analysts can focus on Level 3 tasks.

Containing threats with Autonomous Response

Simply quarantining a device is rarely the best course of action - organizations need to be able to maintain normal operations in the face of threats and choose the right course of action. Different organizations also require tailored response functions because they have different standards and protocols across a variety of unique devices. Ultimately, a ‘one size fits all’ approach to automated response actions puts organizations at risk of disrupting business operations.

Darktrace’s Autonomous Response tailors its actions to contain abnormal behavior across users and digital assets by understanding what is normal and stopping only what is not. Unlike blanket quarantines, it delivers a bespoke approach, blocking malicious activities that deviate from regular patterns while ensuring legitimate business operations remain uninterrupted.

Darktrace offers fully customizable response actions, seamlessly integrating with your workflows through hundreds of native integrations and an open API. It eliminates the need for costly development, natively disarming threats in seconds while extending capabilities with third-party tools like firewalls, EDR, SOAR, and ITSM solutions.

Unlocking a proactive state of security

Securing the network isn’t just about responding to incidents — it’s about being proactive, adaptive, and prepared for the unexpected. The NIST Cybersecurity Framework (CSF 2.0) emphasizes this by highlighting the need for focused risk management, continuous incident response (IR) refinement, and seamless integration of these processes with your detection and response capabilities.

Despite advancements in security technology, achieving a proactive posture is still a challenge to overcome because SOC teams face inefficiencies from reliance on pattern-matching tools, which generate excessive false positives and leave many alerts unaddressed, while novel threats go undetected. If SOC teams are spending all their time investigating alerts then there is no time spent getting ahead of attacks.

Achieving proactive network resilience — a state where organizations can confidently address challenges at every stage of their security posture — requires strategically aligned solutions that work seamlessly together across the attack lifecycle.

References

1.       Market Guide for Extended Detection and Response, Gartner, 17thAugust 2023 - ID G00761828

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