Find out how Darktrace identifies and manages shadow IT. Gain valuable insights into detecting unauthorized IT activities within your organization.
Darktrace was recently called into a situation where a department had set up an online questionnaire, which had been included in a newsletter to customers. They’d used a free version of the software and it had not been authorised by IT.
The questionnaire requested some sensitive data from the respondents, but as there was no third-party contractor agreement in place, there was no agreement on data usage, storage, protection or maintenance. Unfortunately, the software provider had a security vulnerability in their solution, and this resulted in a massive data breach of the questionnaire answers – a situation that could have been avoided, had the organization been using Darktrace PREVENT.
This type of unauthorized usage is a common instance of the growing problem of shadow IT. Unlike formal IT, which is routed through an IT department closely involved in approving, setting up, and maintaining it, shadow IT falls outside of that team’s control. It is made up of systems – including cloud and SaaS applications – which the IT department are either unaware exist, or are unable to remove without disrupting workflows.
Because it lacks proper involvement from IT, shadow IT’s impact on a company’s overall security risk can be ill-defined, not least because it is difficult for many organizations to know how much of it exists within their digital estates. Full visibility over the digital environment, and every asset it contains, is necessary before the problem can begin to be addressed.
The reality is: shadow IT happens.
Shadow IT crops up for a number of reasons. This is often employees taking steps to save time: having your IT team acquire and set up new infrastructure and software is important for managing security risks, but they necessarily take time. For some employees, the time taken to go through these formal channels is enough to push them to use shadow IT systems, which are generally quicker and cheaper to set up and begin using. It’s easier than ever, for instance, to spin up cloud IT environments. The pressure of completing projects within strict budgetary limitations may also tempt employees down this cheaper, but more hazardous path.
There is also a problem of business-led IT, whereby business decisions involving the use of new systems are made without consulting IT departments. Organizations should always look to adopt a security-first approach, because when business interests lead the way, IT teams can struggle to keep up, leading to the emergence of new vulnerabilities. In cases where these business decisions are intentionally hidden from the IT team, Shadow IT becomes a serious concern.
Reducing the effects of Shadow IT
In the end, security teams, particularly those charged with securing large organizations, will never entirely prevent employees from occasionally turning to unauthorized systems. They can, however, reduce the impact that these systems have on the organization’s overall risk landscape.
One way to do this is to reexamine the organization’s workflows. Try to identify which formal processes are unnecessarily cumbersome, forcing employees to work around them, and figure out whether they can be improved. When properly managed, formal adoption of the Shadow IT systems employees are already using can be an effective solution.
Improving workflows in this way will begin to help to address the problem, but it will not be the first step an organization takes in the fight against Shadow IT: first it must discover that it has a problem that needs solving, and this cannot be done until its security team uncovers the sheer amount of shadow IT lurking within the organization. The first step, therefore, is to find a way of obtaining total visibility over every system in the digital environment.
The Power of Prevention
For years Darktrace has illuminated the assets silently lurking within an organization, and now Darktrace / Attack Surface Management is finally giving security teams a clear and complete view over the external attack surface of their digital estates, including all of the shadow IT they didn’t previously know about. It does so by continuously monitoring assets and connections on the attack surface for risks and vulnerabilities. On average, this process reveals 30-50% more externally-facing assets than were previously known to the organization’s IT team and, importantly, analyzes the respective risk posed by each.
This information is visualized for the security team in a way which makes it simple to determine the ownership of each system and asset, and helps teams to prioritize those vulnerabilities which require the most attention.
This was crucial recently for an organization that had just been hacked through a shadow IT website created by the marketing department, without the knowledge of the security team. The company immediately brought on Darktrace / Attack Surface Management (ASM) to increase their cyber resilience, and the technology identified 12 urgent vulnerabilities due to shadow IT and misconfigurations which allowed the company to plug those holes before a repeat event occurred.
But having a holistic understanding of the risks of shadow IT requires looking beyond the external attack surface. To this end, Darktrace / Proactive Exposure Management identifies and evaluates all of the attack paths which exist in an organization, and reveals unknown devices which may sit along them. These devices may prove to be components in the middle of critical attack paths leading to precious data or vulnerable assets, but Darktrace minimizes this risk by identifying them and assessing the risk they pose to the environment.
At another organization, Darktrace recently identified a disaster recovery domain controller that was supposed to be an exact replication of the production domain controller. Being a standby for the main domain controller, this device was not regularly monitored by the IT team. However, Darktrace continuously monitors assets within a customer’s environment and identified that, even though they should in theory be the exact same, the back-up domain controller had different potential damage scores due to a Microsoft patch failing to install. No one in the IT team had identified this risk, with Darkrtace identifying the need for patching before to avoid the vulnerability being exploited – and critical data falling into the wrong hands.
From there, it’s up to security teams how they wish to proceed. Some systems and assets may pose too great a risk and will need to be closed off, while others, particularly those which are already widely used within the organization and can be easily secured by the IT department, may be allowed to stay. What matters, is that the ‘shadow’ of shadow IT – the element of mystery which makes these systems such a hazard to security teams – has been lifted. With full visibility over every system and asset, and a clear understanding of which ones constitute network vulnerabilities, security teams no longer need to live in fear of their own organization’s digital environments.
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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.
Author
Dan Fein
VP, Product
Based in New York, Dan joined Darktrace’s technical team in 2015, helping customers quickly achieve a complete and granular understanding of Darktrace’s product suite. Dan has a particular focus on Darktrace/Email, ensuring that it is effectively deployed in complex digital environments, and works closely with the development, marketing, sales, and technical teams. Dan holds a Bachelor’s degree in Computer Science from New York University.
Navigating buying and adoption journeys for AI cybersecurity tools
Enterprise AI tools go mainstream
In this dawning Age of AI, CISOs are increasingly exploring investments in AI security tools to enhance their organizations’ capabilities. AI can help achieve productivity gains by saving time and resources, mining intelligence and insights from valuable data, and increasing knowledge sharing and collaboration.
While investing in AI can bring immense benefits to your organization, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.
Challenges of a muddied marketplace
Key challenges in AI purchasing come from consumer doubt and lack of vendor transparency. The AI software market is buzzing with hype and flashy promises, which are not necessarily going to be realized immediately. This has fostered uncertainty among potential buyers, especially in the AI cybersecurity space.
As Gartner writes, “There is a general lack of transparency and understanding about how AI-enhanced security solutions leverage AI and the effectiveness of those solutions within real-world SecOps. This leads to trust issues among security leaders and practitioners, resulting in slower adoption of AI features” [1].
Given this widespread uncertainty generated through vague hype, buyers must take extra care when considering new AI tools to adopt.
Goals of AI adoption
Buyers should always start their journeys with objectives in mind, and a universal goal is to achieve return on investment. When organizations adopt AI, there are key aspects that will signal strong payoff. These include:
Wide-ranging application across operations and areas of the business
Actual, enthusiastic adoption and application by the human security team
Integration with the rest of the security stack and existing workflows
Business and operational benefits, including but not limited to:
Reduced risk
Reduced time to response
Reduced potential downtime, damage, and disruption
Increased visibility and coverage
Improved SecOps workflows
Decreased burden on teams so they can take on more strategic tasks
Ideally, most or all these measurements will be fulfilled. It is not enough for AI tools to benefit productivity and workflows in theory, but they must be practically implemented to provide return on investment.
Investigation before investment
Before investing in AI tools, buyers should ask questions pertaining to each stage of the adoption journey. The answers to these questions will not only help buyers gauge if a tool could be worth the investment, but also plan how the new tool will practically fit into the organization’s existing technology and workflows.
Figure 1: Initial questions to consider when starting to shop for AI [2].
These questions are good to imagine how a tool will fit into your organization and determine if a vendor is worth further evaluation. Once you decide a tool has potential use and feasibility in your organization, it is time to dive deeper and learn more.
Ask vendors specific questions about their technology. This information will most likely not be on their websites, and since it involves intellectual property, it may require an NDA.
Find a longer list of questions to ask vendors and what to look for in their responses in the white paper “CISO’s Guide to Buying AI.”
Committing to transparency amidst the AI hype
For security teams to make the most out of new AI tools, they must trust the AI. Especially in an AI marketplace full of hype and obfuscation, transparency should be baked into both the descriptions of the AI tool and the tool’s functionality itself. With that in mind, here are some specifics about what techniques make up Darktrace’s AI.
Darktrace as an AI cybersecurity vendor
Darktrace has been using AI technology in cybersecurity for over 10 years. As a pioneer in the space, we have made innovation part of our process.
The Darktrace ActiveAI Security Platform™ uses multi-layered AI that trains on your unique business operations data for tailored security across the enterprise. This approach ensures that the strengths of one AI technique make up for the shortcomings of another, providing well-rounded and reliable coverage. Our models are always on and always learning, allowing your team to stop attacks in real time.
The machine learning techniques used in our solution include:
Unsupervised machine learning
Multiple Clustering Techniques
Multiple anomaly detection models in tandem analyzing data across hundreds of metrics
Bayesian probabilistic methods
Bayesian metaclassifier for autonomous fine-tuning of unsupervised machine learning models
Deep learning engines
Graph theory
Applied supervised machine learning for investigative AI
Neural networks
Reinforcement Learning
Generative and applied AI
Natural Language Processing (NLP) and Large Language Models (LLMs)
Post-processing models
Additionally, since Darktrace focuses on using the customer’s data across its entire digital estate, it brings a range of advantages in data privacy, interpretability, and data transfer costs.
Building trust with Darktrace AI
Darktrace further supports the human security team’s adoption of our technology by building trust. To do that, we designed our platform to give your team visibility and control over the AI.
Instead of functioning as a black box, our products focus on interpretability and sharing confidence levels. This includes specifying the threshold of what triggered a certain alert and the details of the AI Analyst’s investigations to see how it reached its conclusions. The interpretability of our AI uplevels and upskills the human security team with more information to drive investigations and remediation actions.
For complete control, the human security team can modify all the detection and response thresholds for our model alerts to customize them to fit specific business preferences.
Conclusion
CISO’s are increasingly considering investing in AI cybersecurity tools, but in this rapidly growing field, it’s not always clear what to look for.
Buyers should first determine their goals for a new AI tool, then research possible vendors by reviewing validation and asking deeper questions. This will reveal if a tool is a good match for the organization to move forward with investment and adoption.
As leaders in the AI cybersecurity industry, Darktrace is always ready to help you on your AI journey.
Learn more about the most common types of machine learning in cybersecurity in the white paper “CISO’s Guide to Buying AI.”
References
Gartner, April 17, 2024, “Emerging Tech: Navigating the Impact of AI on SecOps Solution Development.”
Triaging Triada: Understanding an Advanced Mobile Trojan and How it Targets Communication and Banking Applications
The rise of android malware
Recently, there has been a significant increase in malware strains targeting mobile devices, with a growing number of Android-based malware families, such as banking trojans, which aim to steal sensitive banking information from organizations and individuals worldwide.
These malware families attempt to access users’ accounts to steal online banking credentials and cookies, bypass multi-factor authentication (MFA), and conduct automatic transactions to steal funds [1]. They often masquerade as legitimate software or communications from social media platforms to compromise devices. Once installed, they use tactics such as keylogging, dumping cached credentials, and searching the file system for stored passwords to steal credentials, take over accounts, and potentially perform identity theft [1].
One recent example is the Antidot Trojan, which infects devices by disguising itself as an update page for Google Play. It establishes a command-and-control (C2) channel with a server, allowing malicious actors to execute commands and collect sensitive data [2].
Despite these malware’s ability to evade detection by standard security software, for example, by changing their code [3], Darktrace recently detected another Android malware family, Triada, communicating with a C2 server and exfiltrating data.
Triada: Background and tactics
First surfacing in 2016, Triada is a modular mobile trojan known to target banking and financial applications, as well as popular communication applications like WhatsApp, Facebook, and Google Mail [4]. It has been deployed as a backdoor on devices such as CTV boxes, smartphones, and tablets during the supply chain process [5]. Triada can also be delivered via drive-by downloads, phishing campaigns, smaller trojans like Leech, Ztorg, and Gopro, or more recently, as a malicious module in applications such as unofficial versions of WhatsApp, YoWhatsApp, and FM WhatsApp [6] [7].
How does Triada work?
Once downloaded onto a user’s device, Triada collects information about the system, such as the device’s model, OS version, SD card space, and list of installed applications, and sends this information to a C2 server. The server then responds with a configuration file containing the device’s personal identification number and settings, including the list of modules to be installed.
After a device has been successfully infected by Triada, malicious actors can monitor and intercept incoming and outgoing texts (including two-factor authentication messages), steal login credentials and credit card information from financial applications, divert in-application purchases to themselves, create fake messaging and email accounts, install additional malicious applications, infect devices with ransomware, and take control of the camera and microphone [4] [7].
For devices infected by unofficial versions of WhatsApp, which are downloaded from third-party app stores [9] and from mobile applications such as Snaptube and Vidmate , Triada collects unique device identifiers, information, and keys required for legitimate WhatsApp to work and sends them to a remote server to register the device [7] [12]. The server then responds by sending a link to the Triada payload, which is downloaded and launched. This payload will also download additional malicious modules, sign into WhatsApp accounts on the target’s phone, and request the same permissions as the legitimate WhatsApp application, such as access to SMS messages. If granted, a malicious actor can sign the user up for paid subscriptions without their knowledge. Triada then collects information about the user’s device and mobile operator and sends it to the C2 server [9] [12].
How does Triada avoid detection?
Triada evades detection by modifying the Zygote process, which serves as a template for every application in the Android OS. This enables the malware to become part of every application launched on a device [3]. It also substitutes system functions and conceals modules from the list of running processes and installed apps, ensuring that the system does not raise the alarm [3]. Additionally, as Triada connects to a C2 server on the first boot, infected devices remain compromised even after a factory reset [4].
Triada attack overview
Across multiple customer deployments, devices were observed making a large number of connections to a range of hostnames, primarily over encrypted SSL and HTTPS protocols. These hostnames had never previously been observed on the customers’ networks and appear to be algorithmically generated. Examples include “68u91.66foh90o[.]com”, “92n7au[.]uhabq9[.]com”, “9yrh7.mea5ms[.]com”, and “is5jg.3zweuj[.]com”.
Figure 1: External Sites Summary Graph showing the rarity of the hostname “92n7au[.]uhabq9[.]com” on a customer network.
Most of the IP addresses associated with these hostnames belong to an ASN associated with the cloud provider Alibaba (i.e., AS45102 Alibaba US Technology Co., Ltd). These connections were made over a range of high number ports over 1000, most commonly over 30000 such as 32091, which Darktrace recognized as extremely unusual for the SSL and HTTPS protocols.
Figure 2: Screenshot of a Model Alert Event log showing a device connecting to the endpoint “is5jg[.]3zweuj[.]com” over port 32091.
On several customer deployments, devices were seen exfiltrating data to hostnames which also appeared to be algorithmically generated. This occurred via HTTP POST requests containing unusual URI strings that were made without a prior GET request, indicating that the infected device was using a hardcoded list of C2 servers.
Figure 3: Screenshot of a Model Alert Event Log showing the device posting the string “i8xps1” to the hostname “72zf6.rxqfd[.]com.
Figure 4: Screenshot of a Model Alert Event Log showing the device posting the string “sqyjyadwwq” to the hostname “9yrh7.mea5ms[.]com”.
These connections correspond with reports that devices affected by Triada communicate with the C2 server to transmit their information and receive instructions for installing the payload.
A number of these endpoints have communicating files associated with the unofficial WhatsApp versions YoWhatsApp and FM WhatsApp [11] [12] [13] . This could indicate that the devices connecting to these endpoints were infected via malicious modules in the unofficial versions of WhatsApp, as reported by open-source intelligence (OSINT) [10] [12]. It could also mean that the infected devices are using these connections to download additional files from the C2 server, which could infect systems with additional malicious modules related to Triada.
Moreover, on certain customer deployments, shortly before or after connecting to algorithmically generated hostnames with communicating files linked to YoWhatsApp and FM WhatsApp, devices were also seen connecting to multiple endpoints associated with WhatsApp and Facebook.
Figure 5: Screenshot from a device’s event log showing connections to endpoints associated with WhatsApp shortly after it connected to “9yrh7.mea5ms[.]com”.
These surrounding connections indicate that Triada is attempting to sign in to the users’ WhatsApp accounts on their mobile devices to request permissions such as access to text messages. Additionally, Triada sends information about users’ devices and mobile operators to the C2 server.
The connections made to the algorithmically generated hostnames over SSL and HTTPS protocols, along with the HTTP POST requests, triggered multiple Darktrace models to alert. These models include those that detect connections to potentially algorithmically generated hostnames, connections over ports that are highly unusual for the protocol used, unusual connectivity over the SSL protocol, and HTTP POSTs to endpoints that Darktrace has determined to be rare for the network.
Conclusion
Recently, the use of Android-based malware families, aimed at stealing banking and login credentials, has become a popular trend among threat actors. They use this information to perform identity theft and steal funds from victims worldwide.
Across affected customers, multiple devices were observed connecting to a range of likely algorithmically generated hostnames over SSL and HTTPS protocols. These devices were also seen sending data out of the network to various hostnames via HTTP POST requests without first making a GET request. The URIs in these requests appeared to be algorithmically generated, suggesting the exfiltration of sensitive network data to multiple Triada C2 servers.
This activity highlights the sophisticated methods used by malware like Triada to evade detection and exfiltrate data. It underscores the importance of advanced security measures and anomaly-based detection systems to identify and mitigate such mobile threats, protecting sensitive information and maintaining network integrity.
Credit to: Justin Torres (Senior Cyber Security Analyst) and Charlotte Thompson (Cyber Security Analyst).
Appendices
Darktrace Model Detections
Model Alert Coverage
Anomalous Connection / Application Protocol on Uncommon Port
Anomalous Connection / Multiple Connections to New External TCP Port
Anomalous Connection / Multiple HTTP POSTS to Rare Hostname
Anomalous Connections / Multiple Failed Connections to Rare Endpoint
Anomalous Connection / Suspicious Expired SSL
Compromise / DGA Beacon
Compromise / Domain Fluxing
Compromise / Fast Beaconing to DGA
Compromise / Sustained SSL or HTTP Increase
Compromise / Unusual Connections to Rare Lets Encrypt
Unusual Activity / Unusual External Activity
AI Analyst Incident Coverage
Unusual Repeated Connections to Multiple Endpoints
![External Sites Summary Graph showing the rarity of the hostname “92n7au[.]uhabq9[.]com” on a customer network.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/670d96b065612663d098df74_670d956f4dda5eb6ac9e650f_Screenshot%25202024-10-14%2520at%25203.04.22%25E2%2580%25AFPM.png)
![Screenshot of a Model Alert Event log showing a device connecting to the endpoint “is5jg[.]3zweuj[.]com” over port 32091.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/670d96b065612663d098df7d_670d959854eb594420fdc971_Screenshot%25202024-10-14%2520at%25203.05.03%25E2%2580%25AFPM.png)
![Screenshot of a Model Alert Event Log showing the device posting the string “i8xps1” to the hostname “72zf6.rxqfd[.]com.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/670d96b065612663d098df80_670d95c562c6e392c7dc3ba8_Screenshot%25202024-10-14%2520at%25203.05.49%25E2%2580%25AFPM.png)
![Screenshot of a Model Alert Event Log showing the device posting the string “sqyjyadwwq” to the hostname “9yrh7.mea5ms[.]com”.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/670d96b065612663d098df7a_670d95e4b1dbad771717553c_Screenshot%25202024-10-14%2520at%25203.06.20%25E2%2580%25AFPM.png)
