Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO
Written by
Adam Stevens
Senior Director of Product, Cloud | Darktrace
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09
Aug 2023
TLDR: There are too many technical vulnerabilities and there is too little organizational context for IT teams to patch effectively. Attack path modelling provides the organizational context, allowing security teams to prioritize vulnerabilities. The result is a system where CVEs can be parsed in, organizational context added, and attack paths considered, ultimately providing a prioritized list of vulnerabilities that need to be patched.
Figure 1: The Darktrace user interface presents risk-prioritized vulnerabilities
This blog post explains how Darktrace addresses the challenge of vulnerability prioritization. Most of the industry focusses on understanding the technical impact of vulnerabilities globally (‘How could this CVE generally be exploited? Is it difficult to exploit? Are there pre-requisites to exploitation? …’), without taking local context of a vulnerability into account. We’ll discuss here how we create that local context through attack path modelling and map it to technical vulnerability information. The result is a stunningly powerful way to prioritize vulnerabilities.
We will explore:
1) The challenge and traditional approach to vulnerability prioritization 2) Creating local context through machine learning and attack path modelling 3) Examining the result – contextualized, vulnerability prioritization
The Challenge
Anyone dealing with Threat and Vulnerability Management (TVM) knows this situation:
You have a vulnerability scanning report with dozens or hundreds of pages. There is a long list of ‘critical’ vulnerabilities. How do you start prioritizing these vulnerabilities, assuming your goal is reducing the most risk?
Sometimes the challenge is even more specific – you might have 100 servers with the same critical vulnerability present (e.g. MoveIT). But which one should you patch first, as all of those have the same technical vulnerability priority (‘critical’)? Which one will achieve the biggest risk reduction (critical asset e.g.)? Which one will be almost meaningless to patch (asset with no business impact e.g.) and thus just a time-sink for the patch and IT team?
There have been recent improvements upon flat CVE-scoring for vulnerability prioritization by adding threat-intelligence about exploitability of vulnerabilities into the mix. This is great, examples of that additional information are Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities Catalogue (KEV).
With CVE and CVSS scores we have the theoretical technical impact of vulnerabilities, and with EPSS and KEV we have information about the likelihood of exploitation of vulnerabilities. That’s a step forward, but still doesn’t give us any local context. Now we know even more about the global and generic technical risk of a vulnerability, but we still lack the local impact on the organization.
Let’s add that missing link via machine learning and attack path modelling.
Adding Attack Path Modelling for Local Context
To prioritize technical vulnerabilities, we need to know as much as we can about the asset on which the vulnerability is present in the context of the local organization. Is it a crown jewel? Is it a choke point? Does it sit on a critical attack path? Is it a dead end, never used and has no business relevance? Does it have organizational priority? Is the asset used by VIP users, as part of a core business or IT process? Does it share identities with elevated credentials? Is the human user on the device susceptible to social engineering?
Those are just a few typical questions when trying to establish local context of an asset. Knowing more about the threat landscape, exploitability, or technical information of a CVE won’t help answer any of the above questions. Gathering, evaluating, maintaining, and using this local context for vulnerability prioritization is the hard part. This local context often resides informally in the head of the TVM or IT team member, having been assembled by having been at the organization for a long time, ‘knowing’ systems, applications and identities in question and talking to asset and application owners if time permits. This does unfortunately not scale, is time-consuming and heavily dependent on individuals.
Understanding all attack paths for an organization provides this local context programmatically.
We discover those attack paths, and these are bespoke for each organization through Darktrace PREVENT™, using the following method (simplified):
1) Build an adaptive model of the local business. Collect, combine, and analyze (using machine learning and non-machine learning techniques) data from various data domains:
a. Network, Cloud, IT, and OT data (network-based attack paths, communication patterns, peer-groups, choke-points, …). Natively collected by Darktrace technology.
b. Email data (social engineering attack paths, phishing susceptibility, external exposure, security awareness level, …). Natively collected by Darktrace technology.
c. Identity data (account privileges, account groups, access levels, shared permissions, …). Collected via various integrations, e.g. Active Directory.
d. Attack surface data (internet-facing exposure, high-impact vulnerabilities, …). Natively collected by Darktrace technology.
e. SaaS information (further identity context). Natively collected by Darktrace
f. Vulnerability information (CVEs, CVSS, EPSS, KEV, …). Collected via integrations, e.g. Vulnerability Scanners or Endpoint products.
Figure 3: Darktrace PREVENT revealing each stage of an attack path
2) Understand what ‘crown jewels’ are and how to get to them. Calculate entity importance (user, technical asset), exposure levels, potential damage levels (blast radius) weakness levels, and other scores to identify most important entities and their relationships to each other (‘crown jewels’).
Various forms of machine learning and non-machine learning techniques are used to achieve this. Further details on some of the exact methods can be found here. The result is a holistic, adaptive and dynamic model of the organization that shows most important entities and how to get to them across various data domains.
The combination of local context and technical context, around the severity and likelihood of exploitation, creates the Darktrace Vulnerability Score. This enables effective risk-based prioritisation of CVE patching.
Figure 4: List of devices with the highest damage potential in the organization - local context
3) Map the attack path model of the organization to common cyber domain knowledge. We can then combine things like MITRE ATT&CK techniques with those identified connectivity patterns and attack paths – making it easy to understand which techniques, tools and procedures (TTPs) can be used to move through the organization, and how difficult it is to exploit each TTP.
Figure 5: An example attack path with associated MITRE techniques and difficulty scores for each TTP
We can now easily start prioritizing CVE patching based on actual, organizational risk and local context.
Bringing It All Together
Finally, we overlay the attack paths calculated by Darktrace with the CVEs collected from a vulnerability scanner or EDR. This can either happen as a native integration in Darktrace PREVENT, if we are already ingesting CVE data from another solution, or via CSV upload.
Figure 6: Darktrace's global CVE prioritization in action.
But you can also go further than just looking at the CVE that delivers the biggest risk reduction globally in your organization if it is patched. You can also look only at certain group of vulnerabilities, or a sub-set of devices to understand where to patch first in this reduced scope:
Figure 7: An example of the information Darktrace reveals around a CVE
This also provides the TVM team clear justification for the patch and infrastructure teams on why these vulnerabilities should be prioritized and what the positive impact will be on risk reduction.
Attack path modelling can be utilized for various other use cases, such as threat modelling and improving SOC efficiency. We’ll explore those in more depth at a later stage.
Want to explore more on using machine learning for vulnerability prioritization? Want to test it on your own data, for free? Arrange a demo today.
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.
Stopping Stealth Attacks with Precision: How Núclea Prevented a Breach Without Disruption
Núclea prevented a highly targeted phishing attack exploiting trusted relationships—avoiding financial loss, data exposure, and disruption. Darktrace stopped the threat at the point of risk, protecting business continuity and strengthening resilience across the financial ecosystem.
Defend What You Trust: Stories from the Front Lines of Modern Cyber Defense
Security leaders from global organizations across industries sat down with us to share their own front-line experiences and real-world perspectives on how modern attacks unfold, where hidden risks emerge, and how AI is reshaping the way organizations think about the role of security.
AI-powered security for a rapidly growing grocery enterprise
By combining AI-driven detection and autonomous response, this leading grocery holding group has built a security model that delivers continuous protection, accelerates growth, and empowers a small, highly efficient team to safeguard a complex retail ecosystem.
Building Operational Resilience Across Mission-Critical Marine Services
Mission-Critical Marine Services
This marine organization supports offshore energy production, export infrastructure, and regional logistics, delivering critical services through its diverse fleet and a regional shorebase footprint. To power rapid mobilization and 24/7 operational readiness, the organization has embraced cloud adoption and digital transformation, reshaping how crews, contractors, and shore-based teams access services.
While technology modernization has enhanced operations, it has also introduced new security complexities.
From perimeter to identity: As access becomes more distributed, identity has become the primary security control, elevating the risk of credential compromise and privilege misuse.
From confidentiality to availability and resilience: As cloud platforms increasingly underpin fleet and operational systems, cyber incidents can disrupt services and safety.
From isolated tools to unified visibility: Because Information Technology (IT) and Operational Technology (OT) often intersect, and legacy systems coexist with modern cloud platforms, fragmented monitoring makes it harder to understand risk and respond decisively across domains.
“Our cybersecurity priorities expanded along with our business goals, placing availability and resilience at the forefront. The impact of a potential threat became an operational risk, which elevated cybersecurity from an IT issue to an operational safety and resilience enabler.” - Information and Communications Technology (ICT) Manager.
A Unified, AI-Driven Platform for IT and OT
To strengthen visibility, detection, and response across its highly distributed environment, the customer adopted the Darktrace ActiveAI Security Platform™ in 2022.
Darktrace’s contextual detection capability was a key driver. Unlike traditional tools that rely on known threat signatures, Darktrace’s Self-Learning AI learns “normal” behavior to identify emerging threats and correlate visibility across the customer's siloed on-premises and cloud domains.
Today, the customer relies on:
Darktrace / EMAIL™ to reduce phishing risk and minimize disruption from legacy mail controls and false positives
Darktrace / IDENTITY™ to support identity-centric security as cloud access expands across vessels and shore-based operations
Darktrace / NETWORK™ to strengthen oversight across the broader environment, including operational contexts where IT and OT intersect
Darktrace / CLOUD™ (Azure), added in 2024, to extend detection and response into Azure and support cloud transformation without treating cloud as a separate security silo
Darktrace / Incident Readiness & Recovery to strengthen incident readiness and recovery planning
Darktrace Managed Detection and Response Services to provide 24/7/365 monitoring and support
This combination supports a single operating model for the customer: security that can adapt as the environment changes while remaining practical for a lean team responsible for safeguarding both business operations and safety-critical services.
Extending cloud protection without complexity
As the customer accelerated cloud adoption, it expanded coverage in 2024 with Darktrace / CLOUD for Azure to bring cloud workloads under the same AI-driven visibility and response model – without adding operational burden. “This matters in hybrid environments because attacks rarely stay in one place,” explains the ICT Manager. “A compromised identity can trigger activity in the cloud, which can open pathways back into on-premises systems.”
In parallel, Darktrace / CLOUD’s posture management capabilities support governance and audit readiness by surfacing misconfigurations and exposure risks earlier, before they become incidents.
A Stronger, Faster, More Resilient Business
Since adopting Darktrace, the customer has strengthened cyber resilience while reducing operational burden on its small ICT team.
Darktrace continuously analyzes millions of individual events that can contribute to a wider incident. Within a single month, the solution autonomously investigated 88% of all potential threats, taking appropriate action within just 39.4 seconds on average.
Autonomous capabilities ensure threats are stopped and contained until the ICT team can investigate. In one standout instance, Darktrace autonomously blocked malicious links during a mass phishing/spam event before other controls flagged the threat. the ICT Manager later confirmed Microsoft reported the link as malicious, but Darktrace had already acted to prevent delivery and reduce exposure.
“Whether something happens during off hours, while we’re on vacation, or when our attention is focused elsewhere, we’re confident Darktrace will take control and stop a threat before it spreads,” says the ICT Manager.
Darktrace’s Self-Learning AI combines multiple AI methods and advanced techniques to improve threat detection, investigation, and response dramatically reducing alert overload and manual triage. Within a single month, the solution saved the customer's IT group 411 equivalent human investigation hours.
“For a lean team supporting a 24/7 operational footprint, this autonomous action eliminates the constant firefighting and stress, giving us the space to focus on higher-value priorities.”- Information and Communications Technology (ICT) Manager.
Protecting communications without disruption
the customer experienced friction from legacy email and network controls prior to Darktrace, which generated high false positive rates, disrupted legitimate communications, created operational drag, and added workload for ICT. With Darktrace / EMAIL learning normal email behavior and applying context-aware actions, the team reduced unnecessary interruptions while maintaining protection.
“That shift matters in marine services, where business communications directly support coordination across vessels, shore bases, clients, ports, and regulators,” says the ICT Manager. “Darktrace doesn’t just block more threats, it autonomously makes decisions that preserve operational continuity and enable my team to focus on credible threats instead of chasing volume.”
Delivering clarity and confidence
Darktrace has reduced manual triage by correlating activity across email, identity, network, and cloud, providing the context needed to prioritize what matters without requiring the ICT team to stitch together evidence across multiple tools.
“With unified visibility we can identify patterns across domains, make informed decisions about where risk actually exists, and align security actions with operational impact rather than theoretical threats,” explains the ICT Manager. “I can now prioritize effort and investment across our ICT landscape with far greater confidence.”
Regular Executive Threat Reports reinforce operational confidence by giving leadership clear visibility into threats Darktrace has handled autonomously, supporting decisive action when needed and confidence to avoid unnecessary disruption when it isn’t.
Scaling Securely in a Hybrid World
As the customer advances its cloud transformation, the ICT Manager sees the Darktrace partnership evolving into a foundational layer of resilience and assurance, supporting scale, governance, and operational confidence in an increasingly cloud-centric environment.
Key priorities include:
Shifting from hybrid visibility to cloud-first resilience, using continuous monitoring and posture insights to reduce exposure earlier
Strengthening governance and audit readiness, especially as critical workloads and sensitive data expand in Azure and expectations rise under regulatory and client assurance requirements
Increasing reliance on autonomous response and AI investigation as the number of identities, workloads, and access paths grows faster than headcount
Deepening cross-domain correlation so cloud signals further enrich decision-making, supporting faster containment and more confident prioritization
“As we accelerate our cloud strategy, Darktrace will play an even more strategic role,” says the ICT Manager, “providing the guidance, technology, and expertise that allow us to grow with confidence and innovate securely.”
Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection
Why ransomware has changed
Ransomware attacks have continued to increase globally, with 698 incidents reported in May 2026, representing a 48% rise compared to 472 incidents in May 2025 [1]. At the same time, the ransomware landscape is evolving. Several major ransomware groups, including LockBit [2], have been disrupted by successful joint law enforcement operations, resulting in a shift away from a small number of dominant actors towards a more fragmented and distributed ecosystem. This is increasingly composed of smaller groups who play a specialized role in the attack, such as initial access brokers, affiliates or developers.
As a result, ransomware tactics, techniques, and procedures (TTPs) are becoming more diverse and less predictable. On top of this, adversaries are leveraging native tools and legitimate penetration testing frameworks to evade detection. Anomaly-based detection is therefore critical to identify pre-ransomware activity, rather than relying on signatures associated with a handful of well-known ransomware groups.
As these attacks often unfold over several days, there is a critical window for defenders to act. In this context, behavioral-based detection plays a vital role in identifying suspicious pre-ransomware activity, and enabling early intervention before encryption or exfiltration occurs.
Inside a modern ransomware intrusion
In early 2026, Darktrace detected activity within a customer’s environment related to a multi-stage ransomware intrusion from the initial compromise. This activity does not appear to be attributable to a specific ransomware group, and no known ransomware payload was observed until the final stage.
The attack aligns with a broader industry trend in which compromised virtual private network (VPN) credentials are used as an entry point, followed by rapid internal reconnaissance and lateral movement using legitimate administrative tools. This growing preference for native tools and legitimate frameworks in cyber-attacks illustrates that it is increasingly unreliable to depend solely on traditional indicators of compromise such as known malware signatures or exploit detection.
The intrusion also involved the use of Sliver, an open-source adversary emulation framework, which is increasingly observed in real-world attacks. Originally designed for penetration testing and red teaming, Sliver has gained traction among threat actors as a stealthier alternative to more heavily signatured frameworks such as Cobalt Strike. As a legitimate framework, its use further complicates detection for security tools that rely on known malicious signatures.
Darktrace’s detection of a ransomware event in a customer’s environment
The initial compromise appears to have occurred via compromised credentials used over the VPN shortly before, or at the onset of the first indicators of suspicious activity. While it remains unclear as to how or when the threat actors gained access to these credentials, the use of initial access brokers (IABs) is a common feature of modern ransomware operations. This suggests that access to the environment may have been established several days or weeks beforehand.
The intrusion unfolded over three days, presenting multiple opportunities for early detection and intervention before ransomware deployment. The attack progressed through a compressed but structured sequence: initial access and reconnaissance were completed within hours, followed by privilege escalation and lateral movement the next day, and culminating in data exfiltration and encryption shortly thereafter. Throughout each stage, distinct behavioral anomalies emerged across the network providing clear indicators of malicious activity well before the ransomware was deployed.
While Darktrace’s Autonomous Response capability was enabled within the customer’s environment, it was not fully configured across the impacted devices, allowing the attack to progress to ransomware deployment. Had Autonomous Response been fully deployed across the affected systems, it could have taken targeted action against the earliest stages of malicious activity, potentially disrupting the intrusion before it escalated.
Figure 1: Timeline of the attack progression.
Day 1: Reconnaissance and privilege escalation
The threat actor gained access via compromised VPN credentials and initiated internal reconnaissance. Darktrace detected anomalous scanning behavior, including unusual port scanning activity and widespread network enumeration.
Specifically, Darktrace detected a high volume of east-west scanning activity across a broad range of ports, with TCP connections targeting ports 21, 80, 445, 4899 and 8080. Associated URIs suggested the use of Nmap, a widely used penetration testing tool. This highlights how attackers often leverage legitimate penetration testing tools for malicious reconnaissance, enabling them to blend into normal network activity and evade traditional signature-based detection methods.
Figure 2: Darktrace's detection of a sharp increase in anomalous internal connections, triggering multiple high-severity model alerts associated with reconnaissance activity.
Several devices were observed using administrative credentials to carry out privileged actions in a manner that was highly anomalous for the environment. This activity was accompanied by behavior consistent with SMB authentication scanning, suggesting efforts to identify and access additional systems. As the activity intensified, an increasing number of devices became involved, signalling lateral movement and further spread across the network.
Darktrace also identified privilege escalation through active directory (AD) replication abuse, specifically via the drsuapi::DRSGetNCChanges function. This technique allows an attacker with sufficient privileges to request directory replication data from a domain controller (DC), enabling them to extract credentials, including password hashes, without directly interacting with user accounts. Commonly associated with ‘DCSync’ attacks, this technique is frequently used to obtain highly privileged credentials and enable further escalation within an environment.
Figure 3: Darktrace’s detection of anomalous AD replication activity indicative of privilege escalation.
This activity was seen alongside the use of the now obsolete SMBv1, repeated NTLM authentication attempts using multiple variations of ‘Administrator’ credentials, reverse DNS scanning, and large-scale network scanning. Darktrace observed widespread use of SMBv1 across the customer’s environment, exposing a significant security weakness. As a legacy protocol with well-documented weaknesses, SMBv1 can be exploited to facilitate lateral movement, allowing the attackers to expand their access following initial compromise.
Day 3: Lateral Movement, Command & Control, and Exfiltration
Two days later, the attacker escalated privileges and expanded their foothold using living-off-the-land (LOTL) techniques such as PSExec, WMI, and RDP. Concurrently, Darktrace identified command-and-control (C2)-style communications consistent with the Sliver framework, alongside rare outbound connections to cloud infrastructure indicating potential data exfiltration. The volume and severity of observed activity increased as attack behavior intensified.
The device was observed conducting extensive lateral movement, leveraging LOTL techniques to evade detection. Activity included WMI execution (e.g. ExecQuery), DCE-RPC activity, SMB sessions and file writes, most of which were successful, as well as the deployment of PSEXESVC.exe via ADMIN$ shares and prolonged RDP sessions. Darktrace identified this behavior as highly anomalous for the environment. Such activity is commonly associated with the transfer of attacker tooling, remote command execution, and the establishment of persistent access across compromised systems.
Figure 4: Darktrace’s detection of a spike in RPC binding events indicative of potential lateral movement.
On the same day, Darktrace detected C2-style SSL communications originating from multiple internal devices to rare external endpoints. These connections exhibited anomalous characteristics, including invalid SSL certificates and repeated connection patterns resembling beaconing. Analysis of the observed JA3 fingerprint further linked the activity to Sliver, the adversary simulation framework referenced earlier, as the hash has previously been associated with Sliver-related infrastructure [3]. The use of this framework reflects a broader trend of attackers repurposing legitimate offensive security tools for stealthy C2 communications. Connections to 137[.]220[.]59[.]55 (ASN AS20473 AS-VULTR) indicated that the communications were likely routed via a virtual private server (VPS) hosted by Vultr. Attackers often utilize VPS infrastructure from legitimate cloud providers like Vultr to obscure their true origin, blend into benign traffic, and evade IP-based detection mechanisms [4].
Figure 5: Darktrace’s Cyber AI Analyst detection of two linked unusual connections to Vultr infrastructure.
Darktrace also observed a device initiating SSL connections to safedata.s3[.]wasabisys[.]com, an endpoint associated with Wasabi cloud storage. Darktrace recognized that neither the destination nor the associated IP address had previously been observed within the environment. More than 200 MB of data was subsequently uploaded externally to endpoints sharing the same JA3 client hash, indicating a sustained transfer session and potential data exfiltration to third-party storage. The apparent exfiltration prior to encryption is consistent with a double-extortion ransomware strategy.
Figure 6: Darktrace’s Cyber AI Analyst detection of more than 30 rare outbound connections to a Wasabi cloud storage endpoint, indicative of potential data exfiltration
Day 4: Encryption
The attack culminated in ransomware deployment, marking the transition from suspicious network activity to a business-impacting incident. Using SMB-based propagation, the threat actor encrypted thousands of files across the network, affecting multiple systems and disrupting normal operations. Throughout the encryption event, the legacy SMBv1 protocol was used extensively across multiple internal systems, resulting in a significant increase in newly encrypted files.
Figure 7: Darktrace’s detection of abnormal spikes in SMB activity and associated model alerts, indicative of ransomware encryption and propagation.
Darktrace’s Cyber AI Analyst automatically investigated and correlated the encryption activity and related events into a single incident narrative, providing the customer with real-time visibility into the attack while significantly reducing investigation time.
Figure 8: Darktrace’s Cyber AI Analyst’s investigation into the encryption activity. AI Analyst incident detailing example encryption activity in real time. Related events are automatically correlated and summarized into a clear narrative, reducing investigation time.
Defender action recommendations
What Could Have Stopped the Attack Earlier?
Although the attack ultimately resulted in ransomware deployment, there were multiple opportunities to detect, contain, and disrupt the intrusion before encryption occurred. The following actions could have significantly reduced the overall impact:
Detect and investigate indicators of reconnaissance and lateral movement
Unusual scanning
Active Directory replication anomalies consistent with DCSync activity
Anomalous use of native tools and processes indicative of LOTL attacks
Unusual use of common reconnaissance tools such as Nmap and NetScan
Contain compromised credentials and affected devices
Disable and reset compromised VPN credentials
Isolate devices performing anomalous scanning and lateral movement activity
Block suspicious external communications and data exfiltration
Use anomaly-based detection to detect and block repeated outbound connections to rare external infrastructure
Prevent data exfiltration to unauthorized cloud storage services such as Wasabi
Conclusion
The incident highlights the importance of anomaly-based detection, particularly against attacks that primarily use native or legitimate tools to evade traditional security measures. Darktrace identified suspicious activity from the first day of the compromise, providing multiple opportunities to disrupt the intrusion before it progressed to lateral movement and data exfiltration.
In this instance, detection was not the limiting factor; response time was. Prompt investigation and containment of devices exhibiting anomalous behavior could have prevented lateral movement, data exfiltration, and ultimately ransomware deployment.
As adversaries increasingly prioritize stealth over custom malware, relying instead on legitimate tools, valid credentials, and trusted infrastructure, traditional signature-based detection becomes less effective. Identifying subtle behavioral deviations early remains critical to disrupting attacks before they escalate into full-scale ransomware incidents.
Credit to Alexandra Evzona (Cyber Analyst), Priya Thapa (Senior Cyber Analyst) Edited by Ryan Traill (Content Manager)