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May 19, 2020

Understanding a SaaS Attack and How AI Can Investigate

The Cyber AI Platform recently detected and investigated two incidents of SaaS account takeover in real-time. Learn about the importance of cyber security here!
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
Max Heinemeyer
Global Field CISO
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19
May 2020

Executive summary

  • Darktrace has observed a significant increase in attacks against SaaS platforms, including file storage, collaborative work, and email solutions.
  • This blog post details two example threats that are representative of the current threat landscape: an Office 365 business email compromise and a Box.com file sharing account compromise.
  • Organizations are recommended to enable multi-factor authentication to combat credential stuffing attacks and the re-use of stolen credentials from data dumps. It is further advised to actively monitor SaaS environments for in-progress cyber-attacks.
  • SaaS exacerbates the skill gap in security – identifying and investigating threats in SaaS environments is a different skill to traditional security operations skill-sets.

Introduction

The digital transformation – whether planned naturally or forced by the global pandemic – has increased the use of Software-as-a-Service (SaaS) solutions in modern organizations. The annual growth rate of the SaaS market is currently 18%, and as the workforce becomes increasingly remote throughout 2020, this is set to skyrocket.

Attackers have been targeting SaaS solutions for a long time – but almost nobody talks about how the Techniques, Tools & Procedures (TTPs) in SaaS attacks differ significantly from traditional TTPs seen in networks and endpoint attacks.

How do you create meaningful detections in SaaS environments that don’t have endpoint or network data? How can you investigate threats in a SaaS environment as an analyst? What does a ‘good’ SaaS event look like, and what does a threat look like? Finding skilled security analysts that can work in traditional IT environments is already hard – it gets even harder when trying to hire security people with SaaS domain knowledge.

SaaS consumers are left with only a few choices: either use the native SaaS security controls provided in each SaaS solution – and rely on the (non-)maturity of the SaaS provider – or go with a third party SaaS security solution, often in the form of Cloud Access Security Brokers (CASBs). Both cases are often not ideal.

This blog outlines two attacks we have recently observed in SaaS environments that are representative for the broader SaaS threat landscape: a Microsoft (Office) 365 business email compromise (BEC) and the compromise of a corporate Box.com account. The analysis serves to illuminate the sharp distinction between a traditional network attack and a SaaS compromise – demonstrating how using machine learning to detect anomalies in behavior offers crucial hope for defenders as SaaS applications define this new era of work.

Anonymized SaaS Threat 1: Office 365 Business Email Compromise

Figure 1: The timeline of attack for the Microsoft 365 Compromise

In this case of a classic BEC attack, a threat-actor infiltrated an employee’s Microsoft 365 account to access sensitive financial documents hosted in SharePoint, including pay slip and banking details. The attacker went on to make configuration changes to the hacked inbox, deleting items and making updates that may have allowed them to cover their tracks.

Darktrace first observed the employee’s account log in from unusual IP ranges. The particular account had never logged in from Bulgaria before, and the peer accounts belonging to those from the same department had not exhibited similar behavioral traits. This in itself was a low-level anomaly and not necessarily indicative of malicious activity – employees might change locations after all.

The unusual login location was then accompanied by an unusual login time and a new user-agent. All of these anomalies triggered Cyber AI Analyst – Darktrace’s automated threat investigation technology – to launch a deeper analysis.

Darktrace then identified that the account was starting to access highly sensitive information, including payroll information on a Sharepoint. Two examples that were highlighted by AI Analyst are shown below:

  • hxxps://anonymised[.]sharepoint[.]com/anonymised/pages/Understanding-my-payslip[.]aspx
  • hxxps:// anonymised [.]sharepoint[.]com/anonymised /pages/Changing-my-bank-details[.]aspx

The attacker tried to gain insights about payment information and credit card details, with the likely intention of changing the payroll details to an attacker-controlled bank account. But with its ability to automatically analyze events to piece together attack narratives, Cyber AI Analyst was able to put together these weak signals of a threat and illuminate the likely account compromise. The security team was then able to lock the account and alert the user, who subsequently changed their credentials.

Anonymized SaaS Threat 2: Box.com Compromise

Figure 2: The timeline of attack for the Box.com Compromise

Darktrace observed a case of unauthorized access to a corporate Box.com file storage account belonging to an employee of a global supply company. The Box.com account login took place in the US – the same country that this organization operates in – but from an unusual IP space and ASN. Made suspicious by this low-level anomaly, Cyber AI Analyst did further, ongoing investigations into the user’s activity.

The actor behind the account logged in to Box.com successfully, and then proceeded to download expense reports, invoices, and other financial documents. It became evident that the account started accessing files that were highly unusual for the account to access. Darktrace recognized that neither the account itself, nor its peer group were usually accessing the file called ‘PASSWORD SHEET.xlsx’.

With Cyber AI’s bespoke knowledge of ‘self’ for every member of the organization’s workforce, the technology was able to identify the threat immediately. The Darktrace Cyber AI Platform detected that the activity occurred at a highly unusual time for the legitimate user, and that the location of the actor’s IP address was also anomalous compared to the employee’s previous access locations for this particular SaaS service.

While accessing these documents may have been normal for the employee in another context, Darktrace Cyber AI’s deep understanding of user behavior and granular visibility within the Box.com application allowed it to spot the subtle signs of account compromise. Moreover, when Darktrace’s Cyber AI Analyst automatically investigated the threat, it was able to illuminate the wider narrative, understanding that each unauthorized file exposure was part of a connected incident and highlighted the breach as a key concern for the security team.

Conclusion

Traditional detection approaches like ‘more than X failed logins from Y’ are not enough to ensure sufficient security across SaaS applications. Keeping threat intelligence lists up to date is even more difficult, as most SaaS attacks don’t involve any Command & Control – just indiscriminate logins from remote devices. Attackers may use VPN, Tor, other compromised devices, dynamic DNS, or virtual private servers to further mask their tracks.

A more intricate and effective approach to SaaS security requires understanding the dynamic individual behind the account. SaaS applications are fundamentally platforms for humans to communicate – allowing them to exchange and store ideas and information. Abnormal, threatening behavior is therefore impossible to detect without a nuanced understanding of those unique individuals: where and when do they typically access a SaaS account, which files are they like to access, who do they typically connect with?

Cyber AI asks these questions, continuously analyzing data not only across SaaS platforms, but from the unique ‘patterns of life’ of every user and device in the organization as a whole. With this context, it can chain together seemingly disparate anomalies – unusual login times, login locations, access of new or unusual files, and hundreds of other indicators of threat. These anomalies then act as a trigger for more in-depth investigations via Cyber AI Analyst that can link the anomalies together and create a coherent attack narrative.

Both of the above SaaS attacks were comprehensively but succinctly investigated and fully reported on by the Darktrace’s Cyber AI Analyst, which then surfaced an easy-to-understand incident report, ready for executive review. For a more in-depth look at how Cyber AI Analyst investigated an emerging APT threat in the wild, read: Catching APT41 exploiting a zero-day vulnerability.

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
Max Heinemeyer
Global Field CISO

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June 12, 2025

Breaking Silos: Why Unified Security is Critical in Hybrid World

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Hybrid environments demand end-to-end visibility to stop modern attacks

Hybrid environments are a dominant trend in enterprise technology, but they continue to present unique issues to the defenders tasked with securing them. By 2026, Gartner predicts that 75% of organizations will adopt hybrid cloud strategies [1]. At the same time, only 23% of organizations report full visibility across cloud environments [2].

That means a strong majority of organizations do not have comprehensive visibility across both their on-premises and cloud networks. As a result, organizations are facing major challenges in achieving visibility and security in hybrid environments. These silos and fragmented security postures become a major problem when considering how attacks can move between different domains, exploiting the gaps.

For example, an attack may start with a phishing email, leading to the compromise of a cloud-based application identity and then moving between the cloud and network to exfiltrate data. Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.

Given this, unified visibility is essential for security teams to reduce blind spots and detect threats across the entire attack surface.

Risks of fragmented visibility

Silos arise due to separate teams and tools managing on-premises and cloud environments. Many teams have a hand in cloud security, with some common ones including security, infrastructure, DevOps, compliance, and end users, and these teams can all use different tools. This fragmentation increases the likelihood of inconsistent policies, duplicate alerts, and missed threats. And that’s just within the cloud, not even considering the additional defenses involved with network security.

Without a unified security strategy, gaps between these infrastructures and the teams which manage them can leave organizations vulnerable to cyber-attacks. The lack of visibility between on-premises and cloud environments contributes to missed threats and delayed incident response. In fact, breaches involving stolen or compromised credentials take an average of 292 to identify and contain [3]. That’s almost ten months.

The risk of fragmented visibility runs especially high as companies undergo cloud migrations. As organizations transition to cloud environments, they still have much of their data in on-premises networks, meaning that maintaining visibility across both on-premises and cloud environments is essential for securing critical assets and ensuring seamless operations.

Unified visibility is the solution

Unified visibility is achieved by having a single-pane-of-glass view to monitor both on-premises and cloud environments. This type of view brings many benefits, including streamlined detection, faster response times, and reduced complexity.

This can only be accomplished through integrations or interactions between the teams and tools involved with both on-premises security and cloud security.

AI-driven platforms, like Darktrace, are especially well equipped to enable the real-time monitoring and insights needed to sustain unified visibility. This is because they can handle the large amounts of data and data types.

Darktrace accomplishes this by plugging into an organization’s infrastructure so the AI can ingest and analyze data and its interactions within the environment to form an understanding of the organization’s normal behavior, right down to the granular details of specific users and devices. The system continually revises its understanding about what is normal based on evolving evidence.

This dynamic understanding of normal means that the AI engine can identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. This helps reduce noise while surfacing real threats, across cloud and on-prem environments without manual tuning.

In this way, given its versatile AI-based, platform approach, Darktrace empowers security teams with real-time monitoring and insights across both the network and cloud.

Unified visibility in the modern threat landscape

As part of the Darktrace ActiveAI Security Platform™, Darktrace / CLOUD works continuously across public, private, hybrid, and multi-cloud deployments. With real-time Cloud Asset Enumeration and Dynamic Architecture Modeling, Darktrace / CLOUD generates up-to-date architecture diagrams, giving SecOps and DevOps teams a unified view of cloud infrastructures.

It is always on the lookout for changes, driven by user and service activity. For example, unusual user activity can significantly raise the asset’s score, prompting Darktrace’s AI to update its architectural view and keep a living record of the cloud’s ever-changing landscape, providing near real-time insights into what’s happening.

This continuous architectural awareness ensures that security teams have a real-time understanding of cloud behavior and not just a static snapshot.

Darktrace / CLOUD’s unified view of AWS and Azure cloud posture and compliance over time.
Figure 1. Darktrace / CLOUD’s unified view of AWS and Azure cloud posture and compliance over time.

With this dynamic cloud visibility and monitoring, Darktrace / CLOUD can help unify and secure environments.

Real world example: Remote access supply chain attacks

Sectop Remote Access Trojan (RAT) malware, also known as ‘ArchClient2,’ is a .NET RAT that contains information stealing capabilities and allows threat actors to monitor and control targeted computers. It is commonly distributed through drive-by downloads of illegitimate software via malvertizing.

Darktrace has been able to detect and respond to Sectop RAT attacks using unified visibility and platform-wide coverage. In one such example, Darktrace observed one device making various suspicious connections to unusual endpoints, likely in an attempt to receive C2 information, perform beaconing activity, and exfiltrate data to the cloud.

This type of supply chain attack can jump from the network to the cloud, so a unified view of both environments helps shorten detection and response times, therefore mitigating potential impact. Darktrace’s ability to detect these cross-domain behaviors stems from its AI-driven, platform-native visibility.

Conclusion

Organizations need unified visibility to secure complex, hybrid environments effectively against threats and attacks. To achieve this type of comprehensive visibility, the gaps between legacy security tools across on-premises and cloud networks can be bridged with platform tools that use AI to boost data analysis for highly accurate behavioral prediction and anomaly detection.

Read more about the latest trends in cloud security in the blog “Protecting Your Hybrid Cloud: The Future of Cloud Security in 2025 and Beyond.”

References:

1. Gartner, May 22, 2023, “10 Strategic Data and Analytics Predictions Through 2028

2. Cloud Security Alliance, February 14, 2024, “Cloud Security Alliance Survey Finds 77% of Respondents Feel Unprepared to Deal with Security Threats

3. IBM, “Cost of a Data Breach Report 2024

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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June 11, 2025

Proactive OT security: Lessons on supply chain risk management from a rogue Raspberry Pi

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Understanding supply chain risk in manufacturing

For industries running Industrial Control Systems (ICS) such as manufacturing and fast-moving consumer goods (FMCG), complex supply chains mean that disruption to one weak node can have serious impacts to the entire ecosystem. However, supply chain risk does not always originate from outside an organization’s ICS network.  

The implicit trust placed on software or shared services for maintenance within an ICS can be considered a type of insider threat [1], where defenders also need to look ‘from within’ to protect against supply chain risk. Attackers have frequently mobilised this form of insider threat:

  • Many ICS and SCADA systems were compromised during the 2014 Havex Watering Hole attack, where via operators’ implicit trust in the trojanized versions of legitimate applications, on legitimate but compromised websites [2].
  • In 2018, the world’s largest manufacturer of semiconductors and processers shut down production for three days after a supplier installed tainted software that spread to over 10,000 machines in the manufacturer’s network [3].
  • During the 2020 SolarWinds supply chain attack, attackers compromised a version of Orion software that was deployed from SolarWinds’ own servers during a software update to thousands of customers, including tech manufacturing companies such as Intel and Nvidia [4].

Traditional approaches to ICS security have focused on defending against everything from outside the castle walls, or outside of the ICS network. As ICS attacks become more sophisticated, defenders must not solely rely on static perimeter defenses and prevention. 

A critical part of active defense is understanding the ICS environment and how it operates, including all possible attack paths to the ICS including network connections, remote access points, the movement of data across zones and conduits and access from mobile devices. For instance, original equipment manufacturers (OEMs) and vendors often install remote access software or third-party equipment in ICS networks to facilitate legitimate maintenance and support activities, which can unintentionally expand the ICS’ attack surface.  

This blog describes an example of the convergence between supply chain risk and insider risk, when a vendor left a Raspberry Pi device in a manufacturing customer’s ICS network without the customer’s knowledge.

Case study: Using unsupervised machine learning to detect pre-existing security issues

Raspberry Pi devices are commonly used in SCADA environments as low-cost, remotely accessible data collectors [5][6][7]. They are often paired with Industrial Internet of Things (IIoT) for monitoring and tracking [8]. However, these devices also represent a security risk because their small physical size and time-consuming nature of physical inspection makes them easy to overlook. This poses a security risk, as these devices have previously been used to carry out USB-based attacks or to emulate Ethernet-over-USB connections to exfiltrate sensitive data [8][9].

In this incident, a Darktrace customer was unaware that their supplier had installed a Raspberry Pi device on their ICS network. Crucially, the installation occurred prior to Darktrace’s deployment on the customer’s network. 

For other anomaly detection tools, this order of events meant that this third-party device would likely have been treated as part of the customer’s existing infrastructure. However, after Darktrace was deployed, it analyzed the metadata from the encrypted HTTPS and DNS connections that the Raspberry Pi made to ‘call home’ to the supplier and determined that these connections were  unusual compared to the rest of the devices in the network, even in the absence of any malicious indicators of compromise (IoCs).  

Darktrace triggered the following alerts for this unusual activity that consequently notified the customer to the pre-existing threat of an unmanaged device already present in their network:

  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Agent Beacon (Short Period)
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Tags / New Raspberry Pi Device
  • Device / DNS Requests to Unusual Server
  • Device / Anomaly Indicators / Spike in Connections to Rare Endpoint Indicator
Darktrace’s External Sites Summary showing the rarity of the external endpoint that the Raspberry Pi device ‘called home’ to and the model alerts triggered.  
Figure 1: Darktrace’s External Sites Summary showing the rarity of the external endpoint that the Raspberry Pi device ‘called home’ to and the model alerts triggered.  

Darktrace’s Cyber AI Analyst launched an autonomous investigation into the activity, correlating related events into a broader incident and generating a report outlining the potential threat along with supporting technical details.

Darktrace’s anomaly-based detection meant that the Raspberry Pi device did not need to be observed performing clearly malicious behavior to alert the customer to the security risk, and neither can defenders afford to wait for such escalation.

Why is this significant?

In 2021 a similar attack took place. Aiming to poison a Florida water treatment facility, attackers leveraged a TeamViewer instance that had been dormant on the system for six months, effectively allowing the attacker to ‘live off the land’ [10].  

The Raspberry Pi device in this incident also remained outside the purview of the customer’s security team at first. It could have been leveraged by a persistent attacker to pivot within the internal network and communicate externally.

A proactive approach to active defense that seeks to minimize and continuously monitor the attack surface and network is crucial.  

The growing interest in manufacturing from attackers and policymakers

Significant motivations for targeting the manufacturing sector and increasing regulatory demands make the convergence of supply chain risk, insider risk, and the prevalence of stealthy living-off-the-land techniques particularly relevant to this sector.

Manufacturing is consistently targeted by cybercriminals [11], and the sector’s ‘just-in-time’ model grants attackers the opportunity for high levels of disruption. Furthermore, under NIS 2, manufacturing and some food and beverage processing entities are now designated as ‘important’ entities. This means stricter incident reporting requirements within 24 hours of detection, and enhanced security requirements such as the implementation of zero trust and network segmentation policies, as well as measures to improve supply chain resilience [12][13][14].

How can Darktrace help?

Ultimately, Darktrace successfully assisted a manufacturing organization in detecting a potentially disruptive 'near-miss' within their OT environment, even in the absence of traditional IoCs.  Through passive asset identification techniques and continuous network monitoring, the customer improved their understanding of their network and supply chain risk.  

While the swift detection of the rogue device allowed the threat to be identified before it could escalate, the customer could have reduced their time to respond by using Darktrace’s built-in response capabilities, had Darktrace’s Autonomous Response capability been enabled.  Darktrace’s Autonomous Response can be configured to target specific connections on a rogue device either automatically upon detection or following manual approval from the security team, to stop it communicating with other devices in the network while allowing other approved devices to continue operating. Furthermore, the exportable report generated by Cyber AI Analyst helps security teams to meet NIS 2’s enhanced reporting requirements.  

Sophisticated ICS attacks often leverage insider access to perform in-depth reconnaissance for the development of tailored malware capabilities.  This case study and high-profile ICS attacks highlight the importance of mitigating supply chain risk in a similar way to insider risk.  As ICS networks adapt to the introduction of IIoT, remote working and the increased convergence between IT and OT, it is important to ensure the approach to secure against these threats is compatible with the dynamic nature of the network.  

Credit to Nicole Wong (Principal Cyber Analyst), Matthew Redrup (Senior Analyst and ANZ Team Lead)

[related-resource]

Appendices

MITRE ATT&CK Mapping

  • Infrastructure / New Raspberry Pi Device - INITIAL ACCESS - T1200 Hardware Additions
  • Device / DNS Requests to Unusual Server - CREDENTIAL ACCESS, COLLECTION - T1557 Man-in-the-Middle
  • Compromise / Agent Beacon - COMMAND AND CONTROL - T1071.001 Web Protocols

References

[1] https://www.cisa.gov/topics/physical-security/insider-threat-mitigation/defining-insider-threats

[2] https://www.trendmicro.com/vinfo/gb/threat-encyclopedia/web-attack/139/havex-targets-industrial-control-systems

[3]https://thehackernews.com/2018/08/tsmc-wannacry-ransomware-attack.html

[4] https://www.theverge.com/2020/12/21/22194183/intel-nvidia-cisco-government-infected-solarwinds-hack

[5] https://www.centreon.com/monitoring-ot-with-raspberry-pi-and-centreon/

[6] https://ieeexplore.ieee.org/document/9107689

[7] https://www.linkedin.com/pulse/webicc-scada-integration-industrial-raspberry-pi-devices-mryff

[8] https://www.rowse.co.uk/blog/post/how-is-the-raspberry-pi-used-in-the-iiot

[9] https://sepiocyber.com/resources/whitepapers/raspberry-pi-a-friend-or-foe/#:~:text=Initially%20designed%20for%20ethical%20purposes,as%20cyberattacks%20and%20unauthorized%20access

[10] https://edition.cnn.com/2021/02/10/us/florida-water-poison-cyber/index.html

[11] https://www.mxdusa.org/2025/02/13/top-cyber-threats-in-manufacturing/

[12] https://www.shoosmiths.com/insights/articles/nis2-what-manufacturers-and-distributors-need-to-know-about-europes-new-cybersecurity-regime

[13] https://www.goodaccess.com/blog/nis2-require-zero-trust-essential-security-measure#zero-trust-nis2-compliance

[14] https://logisticsviewpoints.com/2024/11/06/the-impact-of-nis-2-regulations-on-manufacturing-supply-chains/

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About the author
Nicole Wong
Cyber Security Analyst
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