Bridging the cyber skills gap: Cyber AI Analyst for OT
25
Feb 2020
25
Feb 2020
Groundbreaking technology combines the skill of human expertise with the speed and scale of AI, filling in gaps in domain knowledge.
Security analysts investigate threats by finding patterns, forming hypotheses, reaching conclusions, and sharing their findings with the rest of the business. These are labor-intensive steps that take not only time, but years of training and expertise. And as operational technology (OT) becomes further integrated with the corporate network, and as threat-actors continue to advance their methods of attack, the emergence of a cyber security skills gap in the OT world becomes more and more evident.
The trend towards interconnected IT and OT environments is matched in equal measure by converging security teams. CISOs have assumed responsibility for the security of ICS environments without necessarily possessing specialized OT skills. Similarly, OT engineers are often handed security roles involving IT without sufficient training. As a result, a knowledge gap is emerging, with organizations struggling to find experts with the necessary skills in both operational technology and traditional IT.
However, developments in artificial intelligence are being leveraged to fill this skills shortage, and technology exists today that can stitch together related security events across OT and IT into a single incident — generating a meaningful, natural-language summary of the suspicious activity.
Darktrace’s Cyber AI Analyst for OT combines the skill of human expertise with the speed and scale of AI, empowering it to conduct expert investigations into hundreds of parallel threads simultaneously. This groundbreaking technology is the result of over 3 years of research and development at Darktrace’s R&D Center in Cambridge, UK — harnessing supervised machine learning to replicate the actions of expert OT and IT analysts. Every time a security alert is triggered, Cyber AI Analyst automatically pulls together a full incident report, drawing upon multiple related alerts and useful surrounding context to complete the picture.
Cyber AI Analyst for OT has domain knowledge from both OT and IT “baked in” to ensure that it can do a lot of the interpretation. An IT SOC can receive the specialized and detailed OT information relating to an incident, but also the higher-level abstractions and meaning to help them triage. Equally, OT engineers can, for example, be presented with a complete timeline of a zero-day ransomware infection as it emerges, without needing to know how to investigate file-sharing activity or command and control beaconing. Cyber AI Analyst for OT therefore not only saves security teams crucial time, but bridges the skills gap that increasingly widens as OT and IT environments continue to converge.
Investigating a ‘Triton 2.0’ attack
Cyber AI Analyst presents its findings in Darktrace’s graphical user interface, the Threat Visualizer. We can view an example of this by looking at a Triton-style cyber-attack captured within a customer environment.
Figure 1: Three models are breached by a desktop device
The threat tray above shows three individual alerts pertaining to a particular device — expdev127.scada.local, a desktop belonging to a domain administrator. Working in real time in the background, Cyber AI Analyst for OT now stitches together these multiple alerts into a single security incident, and then surfaces this incident in a high-level narrative, displaying all stages of the attack lifecycle on a single timeline.
Figure 2: The Threat Visualizer surfaces a timeline of the suspicious events
We can see that over the span of three hours, Darktrace identified a suspicious file download, possible command and control traffic, and a chain of administrative connections it deemed worthy of investigation. The Threat Visualizer then surfaced this series of suspicious connections, showing how the malware penetrated from the upper parts of the control system through to a workstation that can interact with PLCs.
Figure 3: A graphical representation of the RDP communication
Since the initial compromise infected a domain administrator’s desktop, the primary ‘hop’ of remote desktop to the local domain controller illustrated here is not unusual at all — the usage of legitimate administrative RDP credentials is commonplace from this device. However, as the incident unfolds, Cyber AI Analyst subsequently recognizes that this is related to more suspicious events, and is able to go back and include these events in a single narrative.
The malware then makes a second hop — also via RDP — to an engineering workstation and finally reprograms a related PLC, all the while retaining the remote access chain. As with the Triton attack that targeted various power plants in 2017, this attack relied on commonplace administration sessions to transfer tools, and for remote command/program execution. The Threat Visualizer shows us the destination port, as well as the application protocol used to deliver the final stage of the attack.
Figure 4: Further details of the reprogramming
Cyber AI Analyst converts the initial alerts into this incident report in real time, and the security team enter the fray armed with a much clearer and broader description of the incident, far sooner than if they had needed to perform these steps themselves. In this case, Cyber AI Analyst eventually includes seven alerts of different suspicious activities within this one incident, as well as multiple details that did not create alerts themselves but are strongly related and could have been omitted by an inexperienced analyst.
The near future of ICS attacks
Cyber-attacks on ICS are continuously evolving, with adversaries using the latest open-source technologies to launch evasive and machine-speed campaigns globally. While many organizations are turning to AI to face the scale, complexity, and speed of the cyber-threats they face in their IT and OT environments, we can also expect that these threat-actors will also start to use AI to achieve their objectives.
The threat-actors behind Triton blended mainstream IT attack techniques with specialized OT payloads and backed both up with strong operational discipline. The future addition of AI into such malware will allow it to achieve more inside a target network without persistent human oversight — and therefore dramatically decrease its chances of detection.
By combining both IT and OT analyst domain knowledge whilst operating at machine speed with a computer’s unwavering attention to detail, Cyber AI Analyst for OT will prove crucial for security teams by saving them vital time and filling in for any gaps in domain knowledge.
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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.
AUTHOR
ABOUT ThE AUTHOR
David Masson
VP, Enterprise Security
David Masson is Darktrace’s Director of Enterprise Security, and has over two decades of experience working in fast moving security and intelligence environments in the UK, Canada and worldwide. With skills developed in the civilian, military and diplomatic worlds, he has been influential in the efficient and effective resolution of various unique national security issues. David is an operational solutions expert and has a solid reputation across the UK and Canada for delivery tailored to customer needs. At Darktrace, David advises strategic customers across North America and is also a regular contributor to major international and national media outlets in Canada where he is based. He holds a master’s degree from Edinburgh University.
A Busy Agenda: Darktrace’s Detection of Qilin Ransomware-as-a-Service Operator
04
Jul 2024
Qilin ransomware has recently dominated discussions across the cyber security landscape following its deployment in an attack on Synnovis, a UK-based medical laboratory company. The ransomware attack ultimately affected patient services at multiple National Health Service (NHS) hospitals that rely on Synnovis diagnostic and pathology services. Qilin’s origins, however, date back further to October 2022 when the group was observed seemingly posting leaked data from its first known victim on its Dedicated Leak Site (DLS) under the name Agenda[1].
The Darktrace Threat Research team investigated network artifacts related to Qilin and identified three probable cases of the ransomware across the Darktrace customer base between June 2022 and May 2024.
Qilin Ransomware-as-a-Service Operator
Qilin operates as a Ransomware-as-a-Service (RaaS) that employs double extortion tactics, whereby harvested data is exfiltrated and threatened of publication on the group's DLS, which is hosted on Tor. Qilin ransomware has samples written in both the Golang and Rust programming languages, making it compilable with various operating systems, and is highly customizable. When building Qilin ransomware variants to be used on their target(s), affiliates can configure settings such as the encryption mode (i.e., skip-step, percent, and speed), the file extension being appended, files, extensions and directories to be skipped during the encryption, and the processes and services to be terminated, among others[1] [2].
Trend Micro analysts, who were the first to discover Qilin samples in August 2022, when the name "Agenda" was still used in ransom notes, found that each analyzed sample was customized for the intended victims and that "unique company IDs were used as extensions of encrypted files" [3]. This information is configurable from within the Qilin's affiliate panel's 'Targets' section, shown below. The panel's background image features the eponym Chinese legendary chimerical creature Qilin (pronounced “Ke Lin”). Despite this Chinese mythology reference, Russian language was observed being used by a Qilin operator in an underground forum post aimed at hiring affiliates and advertising their RaaS operation[2].
Figure 1: Qilin ransomware’s affiliate panel.
Qilin's RaaS program purportedly has an attractive affiliates' payment structure, with affiliates allegedly able to earn 80% of ransom payments of USD 3m or less and 85% for payments above that figure[2], making it a possibly appealing option in the RaaS ecosystem. Publication of stolen data and ransom payment negotiations are purportedly handled by Qilin operators. Qilin affiliates have been known to target companies located around the world and within a variety of industries, including critical sectors such as healthcare and energy.
As Qilin is a RaaS operation, the choice of targets does not necessarily reflect Qilin operators' intentions, but rather that of its affiliates. Similarly, the tactics, techniques, procedures (TTPs) and indicators of compromise (IoC) identified by Darktrace are associated with the given affiliate deploying Qilin ransomware for their own purpose, rather than TTPs and IoCs of the Qilin group. Likewise, initial vectors of infection may vary from affiliate to affiliate. Previous studies show that initial access to networks were gained via spear phishing emails or by leveraging exposed applications and interfaces.
Differences have been observed in terms of data exfiltration and potential C2 external endpoints, suggesting the below investigations are not all related to the same group or actor(s).
Darktrace’s Threat Research Investigation
June 2022
Darktrace first detected an instance of Qilin ransomware back in June 2022, when an attacker was observed successfully accessing a customer’s Virtual Private Network (VPN) and compromising an administrative account, before using RDP to gain access to the customer’s Microsoft System Center Configuration Manager (SCCM) server
From there, an attack against the customer's VMware ESXi hosts was launched. Fortunately, a reboot of their virtual machines (VM) caught the attention of the security team who further uncovered that custom profiles had been created and remote scripts executed to change root passwords on their VM hosts. Three accounts were found to have been compromised and three systems encrypted by ransomware.
Unfortunately, Darktrace was not configured to monitor the affected subnets at the time of the attack. Despite this, the customer was able to work directly with Darktrace analysts via the Ask the Expert (ATE) service to add the subnets in question to Darktrace’s visibility, allowing it to monitor for any further unusual behavior.
Once visibility over the compromised SCCM server was established, Darktrace observed a series of unusual network scanning activities and the use of Kali (a Linux distribution designed for digital forensics and penetration testing). Furthermore, the server was observed making connections to multiple rare external hosts, many using the “[.]ru” Top Level Domain (TLD). One of the external destinations the server was attempting to connect was found to be related to SystemBC, a malware that turns infected hosts into SOCKS5 proxy bots and provides command-and-control (C2) functionality.
Additionally, the server was observed making external connections over ports 993 and 143 (typically associated with the use of the Interactive Message Access Protocol (IMAP) to multiple rare external endpoints. This was likely due to the presence of Tofsee malware on the device.
After the compromise had been contained, Darktrace identified several ransom notes following the naming convention “README-RECOVER-<extension/company_id>.txt”” on the network. This naming convention, as well as the similar “<company_id>-RECOVER-README.txt” have been referenced by open-source intelligence (OSINT) providers as associated with Qilin ransom notes[5] [6] [7].
April 2023
The next case of Qilin ransomware observed by Darktrace took place in April 2023 on the network of a customer in the manufacturing sector in APAC. Unfortunately for the customer in this instance, Darktrace RESPOND™ was not active on their environment and no autonomous response actions were taken to contain the compromise.
Over the course of two days, Darktrace identified a wide range of malicious activity ranging from extensive initial scanning and lateral movement attempts to the writing of ransom notes that followed the aforementioned naming convention (i.e., “README-RECOVER-<extension/company_id>.txt”).
Darktrace observed two affected devices attempting to move laterally through the SMB, DCE-RPC and RDP network protocols. Default credentials (e.g., UserName, admin, administrator) were also observed in the large volumes of SMB sessions initiated by these devices. One of the target devices of these SMB connections was a domain controller, which was subsequently seen making suspicious WMI requests to multiple devices over DCE-RPC and enumerating SMB shares by binding to the ‘server service’ (srvsvc) named pipe to a high number of internal devices within a short time frame. The domain controller was further detected establishing an anomalously high number of connections to several internal devices, notably using the RDP administrative protocol via a default admin cookie.
Repeated connections over the HTTP and SSL protocol to multiple newly observed IPs located in the 184.168.123.0/24 range were observed, indicating C2 connectivity. WebDAV user agent and a JA3 fingerprint potentially associated with Cobalt Strike were notably observed in these connections. A few hours later, Darktrace detected additional suspicious external connections, this time to IPs associated with the MEGA cloud storage solution. Storage solutions such as MEGA are often abused by attackers to host stolen data post exfiltration. In this case, the endpoints were all rare for the network, suggesting this solution was not commonly used by legitimate users. Around 30 GB of data was exfiltrated over the SSL protocol.
Darktrace did not observe any encryption-related activity on this customer’s network, suggesting that encryption may have taken place locally or within network segments not monitored by Darktrace.
May 2024
The most recent instance of Qilin observed by Darktrace took place in May 2024 and involved a customer in the US. In this case, Darktrace initially detected affected devices using unusual administrative and default credentials, before additional internal systems were observed making extensive suspicious DCE-RPC requests to a range of internal locations, performing network scanning, making unusual internal RDP connections, and transferring suspicious executable files like 'a157496.exe' and '83b87b2.exe'. SMB writes of the file "LSM_API_service" were also observed, activity which was considered 100% unusual by Darktrace; this is an RPC service that can be abused to enumerate logged-in users and steal their tokens. Various repeated connections likely representative of C2 communications were detected via both HTTP and SSL to rare external endpoints linked in OSINT to Cobalt Strike use. During these connections, HTTP GET requests for the following URIs were observed:
/asdffHTTPS
/asdfgdf
/asdfgHTTP
/download/sihost64.dll
Notably, this included a GET request a DLL file named "sihost64.dll" from a domain controller using PowerShell.
Over 102 GB of data may have been transferred to another previously unseen endpoint, 194.165.16[.]13, via the unencrypted File Transfer Protocol (FTP). Additionally, many non-FTP connections to the endpoint could be observed, over which more than 783 GB of data was exfiltrated. Regarding file encryption activity, a wide range of destination devices and shares were targeted.
Figure 2: Advanced Search graph displaying the total volume of data transferred over FTP to a malicious IP.
During investigations, Darktrace’s Threat Research team identified an additional customer, also based in the United States, where similar data exfiltration activity was observed in April 2024. Although no indications of ransomware encryption were detected on the network, multiple similarities were observed with the case discussed just prior. Notably, the same exfiltration IP and protocol (194.165.16[.]13 and FTP, respectively) were identified in both cases. Additional HTTP connectivity was further observed to another IP using a self-signed certificate (i.e., CN=ne[.]com,OU=key operations,O=1000,L=,ST=,C=KM) located within the same ASN (i.e., AS48721 Flyservers S.A.). Some of the URIs seen in the GET requests made to this endpoint were the same as identified in that same previous case.
Information regarding another device also making repeated connections to the same IP was described in the second event of the same Cyber AI Analyst incident. Following this C2 connectivity, network scanning was observed from a compromised domain controller, followed by additional reconnaissance and lateral movement over the DCE-RPC and SMB protocols. Darktrace again observed SMB writes of the file "LSM_API_service", as in the previous case, activity which was also considered 100% unusual for the network. These similarities suggest the same actor or affiliate may have been responsible for activity observed, even though no encryption was observed in the latter case.
Figure 3. First event of the Cyber AI Analyst investigation following the compromise activity.
According to researchers at Microsoft, some of the IoCs observed on both affected accounts are associated with Pistachio Tempest, a threat actor reportedly associated with ransomware distribution. The Microsoft threat actor naming convention uses the term "tempest" to reference criminal organizations with motivations of financial gain that are not associated with high confidence to a known non-nation state or commercial entity. While Pistachio Tempest’s TTPs have changed over time, their key elements still involve ransomware, exfiltration, and extortion. Once they've gained access to an environment, Pistachio Tempest typically utilizes additional tools to complement their use of Cobalt Strike; this includes the use of the SystemBC RAT and the SliverC2 framework, respectively. It has also been reported that Pistacho Tempest has experimented with various RaaS offerings, which recently included Qilin ransomware[4].
Conclusion
Qilin is a RaaS group that has gained notoriety recently due to high-profile attacks perpetrated by its affiliates. Despite this, the group likely includes affiliates and actors who were previously associated with other ransomware groups. These individuals bring their own modus operandi and utilize both known and novel TTPs and IoCs that differ from one attack to another.
Darktrace’s anomaly-based technology is inherently threat-agnostic, treating all RaaS variants equally regardless of the attackers’ tools and infrastructure. Deviations from a device’s ‘learned’ pattern of behavior during an attack enable Darktrace to detect and contain potentially disruptive ransomware attacks.
Credit to: Alexandra Sentenac, Emma Foulger, Justin Torres, Min Kim, Signe Zaharka for their contributions.
With the rise of GenAI and novel attacks, organizations can no longer rely solely on traditional network security solutions that depend on historical attack data, such as signatures and detection rules, to identify threats. However, in many cases network security vendors and traditional solutions like IDS/IPS focus on detecting known attacks using historical data. What happens is organizations are left vulnerable to unknown and novel threats, as these approaches only detect known malicious behavior and cannot keep up with unknown threats or zero-day attacks.
Advanced threats
Darktrace's End of Year Threat Report for 2023 highlights significant changes in the cyber threat landscape, particularly due to advancements in technology such as generative AI. The report notes a substantial increase in sophisticated attacks, including those utilizing generative AI, which have made it more challenging for traditional security measures to keep up. The report also details the rise of multi-functional malware, like Black Basta ransomware, which not only encrypts data for ransom but also spreads other types of malware such as the Qbot banking trojan. These complex attacks are increasingly being deployed by advanced cybercriminal groups, underscoring the need for organizations to adopt advanced security measures that can detect and respond to novel threats in real-time.
Defenders need a solution that can level the playing field, especially when they are operating with limited resources and getting overloaded with endless alerts. Most network security tools on the market have a siloed approach and do not integrate with the rest of an organization’s digital estate, but attackers don’t operate in a single domain.
Disparate workforce
With so many organizations continuing to support a remote or hybrid working environment, the need to secure devices that are outside the corporate network or off-VPN is increasingly important. While endpoint protection or endpoint detection and response (EDR) tools are a fundamental part of any security stack, it’s not possible to install an agent on every device, which can leave blind spots in an organization’s attack surface. Managing trust and access policies is also necessary to protect identities, however this comes with its own set of challenges in terms of implementation and minimizing business disruption.
This blog will dive into these challenges and show examples of how Darktrace has helped mitigate risk and stop novel and never-before-seen threats.
Network Security Challenge 1: Managing trust
What is trust in cybersecurity?
Trust in cybersecurity means that an entity can be relied upon. This can involve a person, organization, or system to be authorized or authenticated by proving their identity is legitimate and can be trusted to have access to the network or sensitive information.
Why is trust important in cybersecurity?
Granting access and privileges to your workforce and select affiliates has profound implications for cybersecurity, brand reputation, regulatory compliance, and financial liability. In a traditional network security model, traffic gets divided into two categories — trusted and untrusted — with some entities and segments of the network deemed more creditable than others.
How do you manage trust in cybersecurity?
Zero trust is too little, but any is too much.
Modern network security challenges point to an urgent need for organizations to review and update their approaches to managing trust. External pressure to adopt zero trust security postures literally suggests trusting no one, but that impedes your freedom to do business. IT leaders need a proven but practical process for deciding who should be allowed to use your network and how.
Questions to ask in updating Trusted User policies include:
What process should you follow to place trust in third parties and applications?
Do you subject trusted entities to testing and other due diligence first?
How often do you review this process — and trusted relationships themselves — after making initial decisions?
How do you tell when trusted users should no longer be trusted?
Once trust has been established, security teams need new and better ways to autonomously verify that those transacting within your network are indeed those trusted users that they claim to be, taking only the authorized actions you’ve allowed them to take.
Exploiting trust in the network
Insider threats have a major head start. The opposite of attacks launched by nameless, faceless strangers, insider threats originate through parties once deemed trustworthy. That might mean a current or former member of your workforce or a partner, vendor, investor, or service provider authorized by IT to access corporate systems and data. Threats also arise when a “pawn” gets unwittingly tricked into disclosing credentials or downloading malware.
Common motives for insider attacks include revenge, stealing or leaking sensitive data, taking down IT systems, stealing assets or IP, compromising your organization’s credibility, and simply harassing your workforce. Put simply, rules and signatures based security solutions won’t flag insider threats because an insider does not immediately present themselves as an intruder. Insider threats can only be stopped by an evolving understanding of ‘normal’ for every user that immediately alerts your team when trusted users do something strange.
“By 2026, 10% of large enterprises will have a comprehensive, mature and measurable zero-trust program in place, up from less than 1% today.” [1]
Darktrace/OT detected a subtle deviation from normal behavior when a reprogram command was sent by an engineering workstation to a PLC controlling a pump, an action an insider threat with legitimized access to OT systems would take to alter the physical process without any malware involved. In this instance, AI Analyst, Darktrace’s investigation tool that triages events to reveal the full security incident, detected the event as unusual based on multiple metrics including the source of the command, the destination device, the time of the activity, and the command itself.
As a result, AI Analyst created a complete security incident, with a natural language summary, the technical details of the activity, and an investigation process explaining how it came to its conclusion. By leveraging Explainable AI, a security team can quickly triage and escalate Darktrace incidents in real time before it becomes disruptive, and even when performed by a trusted insider.
Ransomware is a type of malware that encrypts valuable files on a victim’s device, denying the account holder access, and demanding money in exchange for the encryption key. Ransomware has been increasingly difficult to deal with, especially with ransom payments being made in crypto currency which is untraceable. Ransomware can enter a system by clicking a link dangerous or downloading malicious files.
Avoiding ransomware attacks ranks at the top of most CISOs’ and risk managers’ priority lists, and with good reason. Extortion was involved in 25% of all breaches in 2022, with front-page attacks wreaking havoc across healthcare, gas pipelines, food processing plants, and other global supply chains. [2]
What else is new?
The availability of “DIY” toolkits and subscription-based ransom- ware-as-a-service (RaaS) on the dark web equips novice threat actors to launch highly sophisticated attacks at machine speed. For less than $500, virtually anyone can acquire and tweak RaaS offerings such as Philadelphia that come with accessible customer interfaces, reviews, discounts, and feature updates — all the signature features of commercial SaaS offerings.
The preeminence of ransomware keeps security teams on high alert for indicators of attack but hypervigilance — and too many tools churning out too many alerts — quickly exhausts analysts’ bandwidth. To reverse this trend, AI needs to help prioritize and resolve versus merely detect risk.
Darktrace uses AI to recognize and contextualize possible signs of ransomware attacks as they appear in your network and across multiple domains. Viewing behaviors in the context of your organization’s normal ‘pattern of life’ updates and enhances detection that watches for a repeat of previous techniques.
Darktrace's AI brings the added advantage of continuously analyzing behavior in your environment at machine speed.
Darktrace AI also performs Autonomous Response, shutting down attacks at every stage of the ransomware cycle, including the first telltale signs of exfiltration and encryption of data for extortion purposes.
Hive is distributed via a RaaS model where its developers update and maintain the code, in return for a percentage of the eventual ransom payment, while users (or affiliates) are given the tools to carry out attacks using a highly sophisticated and complex malware they would otherwise be unable to use.
In early 2022, Darktrace/Network identified several instances of Hive ransomware on the networks of multiple customers. Using its anomaly-based detection, Darktrace was able to successfully detect the attacks and multiple stages of the kill chain, including command and control (C2) activity, lateral movement, data exfiltration, and ultimately data encryption and the writing of ransom notes.
Darktrace’s AI understands customer networks and learns the expected patterns of behavior across an organization’s digital estate. Using its anomaly-based detection Darktrace is able to identify emerging threats through the detection of unusual or unexpected behavior, without relying on rules and signatures, or known IoCs.
You can’t predict tomorrow’s weather by reading yesterday’s forecast, yet that’s essentially what happens when network security tools only look for known attacks.
What are novel attacks?
“Novel attacks” include unknown or previously unseen exploits such as zero-days, or new variations of known threats that evade existing detection rules.
Depending on how threats get executed, the term “novel” can refer to brand new tactics, techniques, and procedures (TTPs), or to subtle new twists on perennial threats like DoS, DDoS, and Domain Name Server (DNS) attacks.
Old tools may be blind to new threats
Stopping novel threats is less about deciding whom to trust than it is about learning to spot something brand new. As we’ve seen with ransomware, the growing “aaS” attack market creates a profound paradigm shift by allowing non-technical perpetrators to tweak, customize, and coin never-before-seen threats that elude traditional network, email, VPN, and cloud security.
Tools based on traditional rules and signatures lack a frame of reference. This is where AI’s ability to spot and analyze abnormalities in the context of normal patterns of life comes into play.
Darktrace AI spots what other tools miss
Instead of training in cloud data lakes that pool data from unrelated attacks worldwide, Darktrace AI learns about your unique environment from your environment. By flagging and analyzing everything unusual — instead of only known signs of compromise — Darktrace’s Self-Learning AI keeps security stacks from missing less obvious but potentially more dangerous events.
The real challenge here is achieving faster “time to meaning” and contextualizing behavior that might — or might not — be part of a novel attack. Darktrace/Network does not require a “patient zero” to identify a novel attack, or one exploiting a zero-day vulnerability.
In late May 2023, Darktrace observed multiple instances of Akira ransomware affecting networks across its customer base. Thanks to its anomaly-based approach to threat detection Darktrace successfully identified the novel ransomware attacks and provided full visibility over the cyber kill chain, from the initial compromise to the eventual file encryptions and ransom notes. Darktrace identified Akira ransomware on multiple customer networks, even when threat actors were utilizing seemingly legitimate services (or spoofed versions of them) to carry out malicious activity. While this may have gone unnoticed by traditional security tools, Darktrace’s anomaly-based detection enabled it to recognize malicious activity for what it was. In cases where Darktrace’s autonomous response was enabled these attacks were mitigated in their early stages, thus minimizing any disruption or damage to customer networks.
