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October 26, 2022

Strategies to Prolong Quantum Ransomware Attacks

Learn more about how Darktrace combats Quantum Ransomware changing strategy for cyberattacks. Explore the power of AI-driven network cyber security!
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
Nicole Wong
Cyber Security Analyst
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26
Oct 2022

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.
Written by
Nicole Wong
Cyber Security Analyst

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September 8, 2025

Unpacking the Salesloft Incident: Insights from Darktrace Observations

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Introduction

On August 26, 2025, Google Threat intelligence Group released a report detailing a widespread data theft campaign targeting the sales automation platform Salesloft, via compromised OAuth tokens used by the third-party Drift AI chat agent [1][2].  The attack has been attributed to the threat actor UNC6395 by Google Threat Intelligence and Mandiant [1].

The attack is believed to have begun in early August 2025 and continued through until mid-August 2025 [1], with the threat actor exporting significant volumes of data from multiple Salesforce instances [1]. Then sifting through this data for anything that could be used to compromise the victim’s environments such as access keys, tokens or passwords. This had led to Google Threat Intelligence Group assessing that the primary intent of the threat actor is credential harvesting, and later reporting that it was aware of in excess of 700 potentially impacted organizations [3].

Salesloft previously stated that, based on currently available data, customers that do not integrate with Salesforce are unaffected by this campaign [2]. However, on August 28, Google Threat Intelligence Group announced that “Based on new information identified by GTIG, the scope of this compromise is not exclusive to the Salesforce integration with Salesloft Drift and impacts other integrations” [2]. Google Threat Intelligence has since advised that any and all authentication tokens stored in or connected to the Drift platform be treated as potentially compromised [1].

This campaign demonstrates how attackers are increasingly exploiting trusted Software-as-a-Service (SaaS) integrations as a pathway into enterprise environment.

By abusing these integrations, threat actors were able to exfiltrate sensitive business data at scale, bypassing traditional security controls. Rather than relying on malware or obvious intrusion techniques, the adversaries leveraged legitimate credentials and API traffic that resembled legitimate Salesforce activity to achieve their goals. This type of activity is far harder to detect with conventional security tools, since it blends in with the daily noise of business operations.

The incident underscores the escalating significance of autonomous coverage within SaaS and third-party ecosystems. As businesses increasingly depend on interconnected platforms, visibility gaps become evident that cannot be managed by conventional perimeter and endpoint defenses.

By developing a behavioral comprehension of each organization's distinct use of cloud services, anomalies can be detected, such as logins from unexpected locations, unusually high volumes of API requests, or unusual document activity. These indications serve as an early alert system, even when intruders use legitimate tokens or accounts, enabling security teams to step in before extensive data exfiltration takes place

What happened?

The campaign is believed to have started on August 8, 2025, with malicious activity continuing until at least August 18. The threat actor, tracked as UNC6395, gained access via compromised OAuth tokens associated with Salesloft Drift integrations into Salesforce [1]. Once tokens were obtained, the attackers were able to issue large volumes of Salesforce API requests, exfiltrating sensitive customer and business data.

Initial Intrusion

The attackers first established access by abusing OAuth and refresh tokens from the Drift integration. These tokens gave them persistent access into Salesforce environments without requiring further authentication [1]. To expand their foothold, the threat actor also made use of TruffleHog [4], an open-source secrets scanner, to hunt for additional exposed credentials. Logs later revealed anomalous IAM updates, including unusual UpdateAccessKey activity, which suggested attempts to ensure long-term persistence and control within compromised accounts.

Internal Reconnaissance & Data Exfiltration

Once inside, the adversaries began exploring the Salesforce environments. They ran queries designed to pull sensitive data fields, focusing on objects such as Cases, Accounts, Users, and Opportunities [1]. At the same time, the attackers sifted through this information to identify secrets that could enable access to other systems, including AWS keys and Snowflake credentials [4]. This phase demonstrated the opportunistic nature of the campaign, with the actors looking for any data that could be repurposed for further compromise.

Lateral Movement

Salesloft and Mandiant investigations revealed that the threat actor also created at least one new user account in early September. Although follow-up activity linked to this account was limited, the creation itself suggested a persistence mechanism designed to survive remediation efforts. By maintaining a separate identity, the attackers ensured they could regain access even if their stolen OAuth tokens were revoked.

Accomplishing the mission

The data taken from Salesforce environments included valuable business records, which attackers used to harvest credentials and identify high-value targets. According to Mandiant, once the data was exfiltrated, the actors actively sifted through it to locate sensitive information that could be leveraged in future intrusions [1]. In response, Salesforce and Salesloft revoked OAuth tokens associated with Drift integrations on August 20 [1], a containment measure aimed at cutting off the attackers’ primary access channel and preventing further abuse.

How did the attack bypass the rest of the security stack?

The campaign effectively bypassed security measures by using legitimate credentials and OAuth tokens through the Salesloft Drift integration. This rendered traditional security defenses like endpoint protection and firewalls ineffective, as the activity appeared non-malicious [1]. The attackers blended into normal operations by using common user agents and making queries through the Salesforce API, which made their activity resemble legitimate integrations and scripts. This allowed them to operate undetected in the SaaS environment, exploiting the trust in third-party connections and highlighting the limitations of traditional detection controls.

Darktrace Coverage

Anomalous activities have been identified across multiple Darktrace deployments that appear associated with this campaign. This included two cases on customers based within the United States who had a Salesforce integration, where the pattern of activities was notably similar.

On August 17, Darktrace observed an account belonging to one of these customers logging in from the rare endpoint 208.68.36[.]90, while the user was seen active from another location. This IP is a known indicator of compromise (IoC) reported by open-source intelligence (OSINT) for the campaign [2].

Cyber AI Analyst Incident summarizing the suspicious login seen for the account.
Figure 1: Cyber AI Analyst Incident summarizing the suspicious login seen for the account.

The login event was associated with the application Drift, further connecting the events to this campaign.

Advanced Search logs showing the Application used to login.
Figure 2: Advanced Search logs showing the Application used to login.

Following the login, the actor initiated a high volume of Salesforce API requests using methods such as GET, POST, and DELETE. The GET requests targeted endpoints like /services/data/v57.0/query and /services/data/v57.0/sobjects/Case/describe, where the former is used to retrieve records based on a specific criterion, while the latter provides metadata for the Case object, including field names and data types [5,6].

Subsequently, a POST request to /services/data/v57.0/jobs/query was observed, likely to initiate a Bulk API query job for extracting large volumes of data from the Ingest Job endpoint [7,8].

Finally, a DELETE request to remove an ingestion job batch, possibly an attempt to obscure traces of prior data access or manipulation.

A case on another US-based customer took place a day later, on August 18. This again began with an account logging in from the rare IP 208.68.36[.]90 involving the application Drift. This was followed by Salesforce GET requests targeting the same endpoints as seen in the previous case, and then a POST to the Ingest Job endpoint and finally a DELETE request, all occurring within one minute of the initial suspicious login.

The chain of anomalous behaviors, including a suspicious login and delete request, resulted in Darktrace’s Autonomous Response capability suggesting a ‘Disable user’ action. However, the customer’s deployment configuration required manual confirmation for the action to take effect.

An example model alert for the user, triggered due to an anomalous API DELETE request.
Figure 3: An example model alert for the user, triggered due to an anomalous API DELETE request.
Figure 4: Model Alert Event Log showing various model alerts for the account that ultimately led to an Autonomous Response model being triggered.

Conclusion

In conclusion, this incident underscores the escalating risks of SaaS supply chain attacks, where third-party integrations can become avenues for attacks. It demonstrates how adversaries can exploit legitimate OAuth tokens and API traffic to circumvent traditional defenses. This emphasizes the necessity for constant monitoring of SaaS and cloud activity, beyond just endpoints and networks, while also reinforcing the significance of applying least privilege access and routinely reviewing OAuth permissions in cloud environments. Furthermore, it provides a wider perspective into the evolution of the threat landscape, shifting towards credential and token abuse as opposed to malware-driven compromise.

Appendices

Darktrace Model Detections

·      SaaS / Access / Unusual External Source for SaaS Credential Use

·      SaaS / Compromise / Login From Rare Endpoint While User Is Active

·      SaaS / Compliance / Anomalous Salesforce API Event

·      SaaS / Unusual Activity / Multiple Unusual SaaS Activities

·      Antigena / SaaS / Antigena Unusual Activity Block

·      Antigena / SaaS / Antigena Suspicious Source Activity Block

Customers should consider integrating Salesforce with Darktrace where possible. These integrations allow better visibility and correlation to spot unusual behavior and possible threats.

IoC List

(IoC – Type)

·      208.68.36[.]90 – IP Address

References

1.     https://cloud.google.com/blog/topics/threat-intelligence/data-theft-salesforce-instances-via-salesloft-drift

2.     https://trust.salesloft.com/?uid=Drift+Security+Update%3ASalesforce+Integrations+%283%3A30PM+ET%29

3.     https://thehackernews.com/2025/08/salesloft-oauth-breach-via-drift-ai.html

4.     https://unit42.paloaltonetworks.com/threat-brief-compromised-salesforce-instances/

5.     https://developer.salesforce.com/docs/atlas.en-us.api_rest.meta/api_rest/resources_query.htm

6.     https://developer.salesforce.com/docs/atlas.en-us.api_rest.meta/api_rest/resources_sobject_describe.htm

7.     https://developer.salesforce.com/docs/atlas.en-us.api_asynch.meta/api_asynch/get_job_info.htm

8.     https://developer.salesforce.com/docs/atlas.en-us.api_asynch.meta/api_asynch/query_create_job.htm

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About the author
Emma Foulger
Global Threat Research Operations Lead

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September 8, 2025

Cyber Assessment Framework v4.0 Raises the Bar: 6 Questions every security team should ask about their security posture

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What is the Cyber Assessment Framework?

The Cyber Assessment Framework (CAF) acts as guide for organizations, specifically across essential services, critical national infrastructure and regulated sectors, across the UK for assessing, managing and improving their cybersecurity, cyber resilience and cyber risk profile.

The guidance in the Cyber Assessment Framework aligns with regulations such as The Network and Information Systems Regulations (NIS), The Network and Information Security Directive (NIS2) and the Cyber Security and Resilience Bill.

What’s new with the Cyber Assessment Framework 4.0?

On 6 August 2025, the UK’s National Cyber Security Centre (NCSC) released Cyber Assessment Framework 4.0 (CAF v4.0) a pivotal update that reflects the increasingly complex threat landscape and the regulatory need for organisations to respond in smarter, more adaptive ways.

The Cyber Assessment Framework v4.0 introduces significant shifts in expectations, including, but not limited to:

  • Understanding threats in terms of the capabilities, methods and techniques of threat actors and the importance of maintaining a proactive security posture (A2.b)
  • The use of secure software development principles and practices (A4.b)
  • Ensuring threat intelligence is understood and utilised - with a focus on anomaly-based detection (C1.f)
  • Performance of proactive threat hunting with automation where appropriate (C2.a)

This blog post will focus on these components of the framework. However, we encourage readers to get the full scope of the framework by visiting the NCSC website where they can access the full framework here.

In summary, the changes to the framework send a clear signal: the UK’s technical authority now expects organisations to move beyond static rule-based systems and embrace more dynamic, automated defences. For those responsible for securing critical national infrastructure and essential services, these updates are not simply technical preferences, but operational mandates.

At Darktrace, this evolution comes as no surprise. In fact, it reflects the approach we've championed since our inception.

Why Darktrace? Leading the way since 2013

Darktrace was built on the principle that detecting cyber threats in real time requires more than signatures, thresholds, or retrospective analysis. Instead, we pioneered a self-learning approach powered by artificial intelligence, that understands the unique “normal” for every environment and uses this baseline to spot subtle deviations indicative of emerging threats.

From the beginning, Darktrace has understood that rules and lists will never keep pace with adversaries. That’s why we’ve spent over a decade developing AI that doesn't just alert, it learns, reasons, explains, and acts.

With Cyber Assessment Framework v4.0, the bar has been raised to meet this new reality. For technical practitioners tasked with evaluating their organisation’s readiness, there are five essential questions that should guide the selection or validation of anomaly detection capabilities.

6 Questions you should ask about your security posture to align with CAF v4

1. Can your tools detect threats by identifying anomalies?

Cyber Assessment Framework v4.0 principle C1.f has been added in this version and requires that, “Threats to the operation of network and information systems, and corresponding user and system behaviour, are sufficiently understood. These are used to detect cyber security incidents.”

This marks a significant shift from traditional signature-based approaches, which rely on known Indicators of Compromise (IOCs) or predefined rules to an expectation that normal user and system behaviour is understood to an extent enabling abnormality detection.

Why this shift?

An overemphasis on threat intelligence alone leaves defenders exposed to novel threats or new variations of existing threats. By including reference to “understanding user and system behaviour” the framework is broadening the methods of threat detection beyond the use of threat intelligence and historical attack data.

While CAF v4.0 places emphasis on understanding normal user and system behaviour and using that understanding to detect abnormalities and as a result, adverse activity. There is a further expectation that threats are understood in terms of industry specific issues and that monitoring is continually updated  

Darktrace uses an anomaly-based approach to threat detection which involves establishing a dynamic baseline of “normal” for your environment, then flagging deviations from that baseline — even when there’s no known IoCs to match against. This allows security teams to surface previously unseen tactics, techniques, and procedures in real time, whether it’s:

  • An unexpected outbound connection pattern (e.g., DNS tunnelling);
  • A first-time API call between critical services;
  • Unusual calls between services; or  
  • Sensitive data moving outside normal channels or timeframes.

The requirement that organisations must be equipped to monitor their environment, create an understanding of normal and detect anomalous behaviour aligns closely with Darktrace’s capabilities.

2. Is threat hunting structured, repeatable, and improving over time?

CAF v4.0 introduces a new focus on structured threat hunting to detect adverse activity that may evade standard security controls or when such controls are not deployable.  

Principle C2.a outlines the need for documented, repeatable threat hunting processes and stresses the importance of recording and reviewing hunts to improve future effectiveness. This inclusion acknowledges that reactive threat hunting is not sufficient. Instead, the framework calls for:

  • Pre-determined and documented methods to ensure threat hunts can be deployed at the requisite frequency;
  • Threat hunts to be converted  into automated detection and alerting, where appropriate;  
  • Maintenance of threat hunt  records and post-hunt analysis to drive improvements in the process and overall security posture;
  • Regular review of the threat hunting process to align with updated risks;
  • Leveraging automation for improvement, where appropriate;
  • Focus on threat tactics, techniques and procedures, rather than one-off indicators of compromise.

Traditionally, playbook creation has been a manual process — static, slow to amend, and limited by human foresight. Even automated SOAR playbooks tend to be stock templates that can’t cover the full spectrum of threats or reflect the specific context of your organisation.

CAF v4.0 sets the expectation that organisations should maintain documented, structured approaches to incident response. But Darktrace / Incident Readiness & Recovery goes further. Its AI-generated playbooks are bespoke to your environment and updated dynamically in real time as incidents unfold. This continuous refresh of “New Events” means responders always have the latest view of what’s happening, along with an updated understanding of the AI's interpretation based on real-time contextual awareness, and recommended next steps tailored to the current stage of the attack.

The result is far beyond checkbox compliance: a living, adaptive response capability that reduces investigation time, speeds containment, and ensures actions are always proportionate to the evolving threat.

3. Do you have a proactive security posture?

Cyber Assessment Framework v4.0 does not want organisations to detect threats, it expects them to anticipate and reduce cyber risk before an incident ever occurs. That is s why principle A2.b calls for a security posture that moves from reactive detection to predictive, preventative action.

A proactive security posture focuses on reducing the ease of the most likely attack paths in advance and reducing the number of opportunities an adversary has to succeed in an attack.

To meet this requirement, organisations could benefit in looking for solutions that can:

  • Continuously map the assets and users most critical to operations;
  • Identify vulnerabilities and misconfigurations in real time;
  • Model likely adversary behaviours and attack paths using frameworks like MITRE ATT&CK; and  
  • Prioritise remediation actions that will have the highest impact on reducing overall risk.

When done well, this approach creates a real-time picture of your security posture, one that reflects the dynamic nature and ongoing evolution of both your internal environment and the evolving external threat landscape. This enables security teams to focus their time in other areas such as  validating resilience through exercises such as red teaming or forecasting.

4. Can your team/tools customize detection rules and enable autonomous responses?

CAF v4.0 places greater emphasis on reducing false positives and acting decisively when genuine threats are detected.  

The framework highlights the need for customisable detection rules and, where appropriate, autonomous response actions that can contain threats before they escalate:

The following new requirements are included:  

  • C1.c.: Alerts and detection rules should be adjustable to reduce false positives and optimise responses. Custom tooling and rules are used in conjunction with off the shelf tooling and rules;
  • C1.d: You investigate and triage alerts from all security tools and take action – allowing for improvement and prioritization of activities;
  • C1.e: Monitoring and detection personnel have sufficient understanding of operational context and deal with workload effectively as well as identifying areas for improvement (alert or triage fatigue is not present);
  • C2.a: Threat hunts should be turned into automated detections and alerting where appropriate and automation should be leveraged to improve threat hunting.

Tailored detection rules improve accuracy, while automation accelerates response, both of which help satisfy regulatory expectations. Cyber AI Analyst allows for AI investigation of alerts and can dramatically reduce the time a security team spends on alerts, reducing alert fatigue, allowing more time for strategic initiatives and identifying improvements.

5. Is your software secure and supported?  

CAF v4.0 introduced a new principle which requires software suppliers to leverage an established secure software development framework. Software suppliers must be able to demonstrate:  

  • A thorough understanding of the composition and provenance of software provided;  
  • That the software development lifecycle is informed by a detailed and up to date understanding of threat; and  
  • They can attest to the authenticity and integrity of the software, including updates and patches.  

Darktrace is committed to secure software development and all Darktrace products and internally developed systems are developed with secure engineering principles and security by design methodologies in place. Darktrace commits to the inclusion of security requirements at all stages of the software development lifecycle. Darktrace is ISO 27001, ISO 27018 and ISO 42001 Certified – demonstrating an ongoing commitment to information security, data privacy and artificial intelligence management and compliance, throughout the organisation.  

6. Is your incident response plan built on a true understanding of your environment and does it adapt to changes over time?

CAF v4.0 raises the bar for incident response by making it clear that a plan is only as strong as the context behind it. Your response plan must be shaped by a detailed, up-to-date understanding of your organisation’s specific network, systems, and operational priorities.

The framework’s updates emphasise that:

  • Plans must explicitly cover the network and information systems that underpin your essential functions because every environment has different dependencies, choke points, and critical assets.
  • They must be readily accessible even when IT systems are disrupted ensuring critical steps and contact paths aren’t lost during an incident.
  • They should be reviewed regularly to keep pace with evolving risks, infrastructure changes, and lessons learned from testing.

From government expectation to strategic advantage

Cyber Assessment Framework v4.0 signals a powerful shift in cybersecurity best practice. The newest version sets a higher standard for detection performance, risk management, threat hunting software development and proactive security posture.

For Darktrace, this is validation of the approach we have taken since the beginning: to go beyond rules and signatures to deliver proactive cyber resilience in real-time.

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Disclaimer:

This document has been prepared on behalf of Darktrace Holdings Limited. It is provided for information purposes only to provide prospective readers with general information about the Cyber Assessment Framework (CAF) in a cyber security context. It does not constitute legal, regulatory, financial or any other kind of professional advice and it has not been prepared with the reader and/or its specific organisation’s requirements in mind. Darktrace offers no warranties, guarantees, undertakings or other assurances (whether express or implied)  that: (i) this document or its content are  accurate or complete; (ii) the steps outlined herein will guarantee compliance with CAF; (iii) any purchase of Darktrace’s products or services will guarantee compliance with CAF; (iv) the steps outlined herein are appropriate for all customers. Neither the reader nor any third party is entitled to rely on the contents of this document when making/taking any decisions or actions to achieve compliance with CAF. To the fullest extent permitted by applicable law or regulation, Darktrace has no liability for any actions or decisions taken or not taken by the reader to implement any suggestions contained herein, or for any third party products, links or materials referenced. Nothing in this document negates the responsibility of the reader to seek independent legal or other advice should it wish to rely on any of the statements, suggestions, or content set out herein.  

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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