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September 13, 2022

Compliance Threat: RedLine Information Stealer

Darktrace reveals the compliance risks posed by the RedLine information stealer. Read about their analysis and how to defend against this cyber threat.
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
Steven Sosa
Analyst Team Lead
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13
Sep 2022

With the continued rise of malware as a service (MaaS), it is now easier than ever to find and deploy information stealers [1]. Given this, it is crucial that companies begin to prioritize good cyber hygiene, and address compliance issues within their environments. Thanks to MaaS, attackers with little to no experience can amplify what might seem like a low-risk attack, into a significant compromise. This blog will investigate a compromise that could have been mitigated with better cyber hygiene and enhanced awareness around compliance issues.

Figure 1: Timeline of the attack

In May 2022 Darktrace DETECT/Network identified a device linked with multiple compliance alerts for ‘torrent’ activity within a Latin American telecommunications company. This culminated in the device downloading a suspicious executable file from an archived webpage. At first, analysis of the downloaded file indicated that it could be a legitimate, albeit outdated software relevant to the client’s industry vertical (SNMPc management tool for GeoDesy GD-300). However, as this was the first event before further suspicious activities, it was also possible that the software downloaded was packaged with malware and marked an initial compromise. Since early April, the device had regularly breached compliance alerts for both BitTorrent and uTorrent (a BitTorrent client). These connections occurred over a common torrenting port, 6881, and may have represented the infection vector.  

Figure 2: View of archived webpage which the suspicious executable was downloaded from

Shortly after the executable was downloaded, Darktrace DETECT alerted a new outbound SSH connection with the following notice in Advanced Search: ‘SSH::Heuristic_Login_Success’. This was highlighted because the breach device did not commonly make connections over this protocol and the destination was a never-before-seen Bulgarian IP address (79.142.70[.]239). The connection lasted 4 minutes, and the device downloaded 31.36 MB of data. 

Following this, the breach device was seen making unusual HTTP connections to rare Russian and Danish endpoints using suspicious user agents. The Russian endpoint was noted for hosting a text file (‘incricinfo[.]com') that listed a single domain which was recently registered. The connections to the Danish endpoint were made to an IP with a URI that OSINT connected to the use of the BeamWinHTTP loader [2]. This loader can be used to download and execute other malware strains, in particular information stealers [3]. 

Figure 3: Screenshot of Russian endpoint with link to incricinfo[.]com 
Figure 4: Cyber AI Analyst highlighting the unusual HTTP connectivity that occurred prior to the multiple suspicious file downloads

At the same time as the connections with the unusual user agents, the device was also seen downloading an executable file from the endpoint, ‘Yuuichirou-hanma[.]s3[.]pl-waw[.]scw[.]cloud’. Analysis of the file indicated that it may be used to deploy further malware and potentially unwanted programs (PUPs). BeamWinHTTP also causes installation of these PUPs which helps to load more nefarious programs and spread compromise. 

This behavior was then seen as the device downloaded 5 different executable files from the endpoint, ‘hakhaulogistics[.]com’. This domain is linked to a Vietnamese logistics company that Darktrace had marked as new within the environment; it is possible that this domain was compromised and being used to host malicious infrastructure. At the point of compromise, several of the downloads were labeled as malicious by popular OSINT [4]. Additionally, at least one of the files was explicitly linked to the RedLine Information Stealer.  

Shortly after, the device made connections to a known Tor relay node. Tor is commonly used as an avenue for C2 communication as it offers a way for attackers to anonymize and obfuscate their activity. It was at this point that the first Proactive Threat Notification (PTN) for this activity occurred. This ensured immediate follow-up investigation from Darktrace SOC and a timeline of events and impacted devices were issued to the customer’s security team directly. 

Figure 5: Cyber AI Analyst highlighting the unusual executable downloads as well as the subsequent Tor connections. The file poweroff[.]exe has been highlighted by several OSINT sources as being potentially malicious

By this point, Darktrace had identified a large volume of unusual outbound HTTP POSTs to a variety of endpoints that seemed to have no obvious function or service. Following these POST requests, the compromised device was seen initiating a long SSL connection to the domain, ‘www[.]qfhwji6fnpiad3gs[.]com’, which is likely to have be generated by an algorithm (DGA). Lastly, a little while after the SSL connections, the device was seen downloading another executable file from the Russian domain ‘test-hf[.]su’. Research on the file again suggested that it was associated with RedLine Stealer [5].  

Figure 6: AIA highlighting additional unusual HTTP connections that were linked with the numeric exe download

Dangers of Non-Compliance 

Whilst the RedLine compromise was a matter of customer concern, the gap in their security was not visibility but rather best practice. It is important to note that prior to these events, the device was commonly seen sending and receiving connections associated with torrenting. In the past it has been observed that RedLine Stealer masquerades as ‘cracked’ software (software that has had its copy protection removed) [6]. In this instance, the initial download of the false ‘SNMPc’ executable may have been proof of this behavior. 

This is a reminder that torrenting is also extremely popular as a peer-to-peer vector for transferring malicious files. Combined with the possibility of network throttling or unapproved VPN use, torrents are usually considered non-compliant within corporate settings. Whether the events here were kickstarted due to a user unwittingly downloading malicious software, or exposure to a malicious actor via BitTorrent use, both cases represent a user circumventing existing compliance controls or a lack of compliance control in general. It is important for organizations to make sure that their users are acting in ways that limit the company’s exposure to nefarious actors. Companies should routinely encourage proper cyber hygiene and implement access controls that block certain activities such as torrenting if threats like these are to be stopped in the future.  

Regardless of what users are doing, Darktrace is positioned to detect and take action on compliance breaches and activity resulting from lack of compliance. The variety of C2 domains used in this blog incident were too quick for most security tools to alert on or for human teams to triage. However, this was no problem for Cyber AI analyst, which was able to draw together aspects of the attack across the kill chain and save a significant amount of time for both the customer security team and Darktrace SOC analysts. If active, Darktrace RESPOND could have blocked activities like the initial BitTorrent connections and incoming download, but with the right preventative measures, it wouldn’t have to. Darktrace PREVENT works continuously to harden defenses and preempt attackers, closing any vulnerabilities before they can be exploited. This includes performing attack surface management, attack path modelling, and security awareness training. In this case, Darktrace PREVENT could have highlighted torrenting activity as part of a potentially harmful attack path and recommended the best actions to mitigate it.

‘No Prior Experience required’ 

In the past, only highly skilled attackers could create and use the tools needed to attack organizations. With Ransomware-as-a-Service (RaaS) proving highly profitable, however, it is no surprise that malware is also becoming a lucrative business. As SaaS can help legitimate companies with no development experience to use and maintain apps, MaaS can help attackers with little to no hacking experience compromise organizations and achieve their goals. RedLine Stealer is readily available, and not prohibitively expensive, meaning attacks can be carried out more frequently, and on a wider range of victims. The incident explored in this blog is proof of this, and a strong indication that security comes not only from strong visibility but also compliance and best practice too. With a powerful defensive tool like PREVENT, security teams can save time while feeling confident that they are keeping ahead of these aspects of security.

Thanks to Adam Stevens for his contributions to this blog.

Appendices

Darktrace Model Breaches

·      Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External 

·      Anomalous File / Numeric Exe Download

·      Anomalous Server Activity / New User Agent from Internet Facing System

·      Compliance / SSH to Rare External Destination

·      Compromise / Anomalous File then Tor 

·      Compromise / Possible Tor Usage 

·      Device / Initial Breach Chain Compromise

·      Device / Long Agent Connection to New Endpoint

References

[1] https://blog.sonicwall.com/en-us/2021/12/the-rise-and-growth-of-malware-as-a-service/

[2] https://asec.ahnlab.com/en/33679/  

[3] https://asec.ahnlab.com/en/20930/

[4] https://www.virustotal.com/gui/file/acfc06b4bcda03ecf4f9dc9b27c510b58ae3a6a9baf1ee821fc624467944467b & https://www.virustotal.com/gui/file/dad6311f96df65f40d9599c84907bae98306f902b1489b03768294b7678a5e79 

[5] https://www.virustotal.com/gui/file/ff7574f9f1d15594e409bee206f5db6c76db7c90dda2ae4f241b77cd0c7b6bf6

[6] https://asec.ahnlab.com/en/30445/

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
Steven Sosa
Analyst Team Lead

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January 8, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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January 5, 2026

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI
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