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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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September 8, 2025

Cyber Assessment Framework v4.0 Raises the Bar: 6 Questions every security team should ask about their security posture

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What is the Cyber Assessment Framework?

The Cyber Assessment Framework (CAF) acts as guide for organizations, specifically across essential services, critical national infrastructure and regulated sectors, across the UK for assessing, managing and improving their cybersecurity, cyber resilience and cyber risk profile.

The guidance in the Cyber Assessment Framework aligns with regulations such as The Network and Information Systems Regulations (NIS), The Network and Information Security Directive (NIS2) and the Cyber Security and Resilience Bill.

What’s new with the Cyber Assessment Framework 4.0?

On 6 August 2025, the UK’s National Cyber Security Centre (NCSC) released Cyber Assessment Framework 4.0 (CAF v4.0) a pivotal update that reflects the increasingly complex threat landscape and the regulatory need for organisations to respond in smarter, more adaptive ways.

The Cyber Assessment Framework v4.0 introduces significant shifts in expectations, including, but not limited to:

  • Understanding threats in terms of the capabilities, methods and techniques of threat actors and the importance of maintaining a proactive security posture (A2.b)
  • The use of secure software development principles and practices (A4.b)
  • Ensuring threat intelligence is understood and utilised - with a focus on anomaly-based detection (C1.f)
  • Performance of proactive threat hunting with automation where appropriate (C2.a)

This blog post will focus on these components of the framework. However, we encourage readers to get the full scope of the framework by visiting the NCSC website where they can access the full framework here.

In summary, the changes to the framework send a clear signal: the UK’s technical authority now expects organisations to move beyond static rule-based systems and embrace more dynamic, automated defences. For those responsible for securing critical national infrastructure and essential services, these updates are not simply technical preferences, but operational mandates.

At Darktrace, this evolution comes as no surprise. In fact, it reflects the approach we've championed since our inception.

Why Darktrace? Leading the way since 2013

Darktrace was built on the principle that detecting cyber threats in real time requires more than signatures, thresholds, or retrospective analysis. Instead, we pioneered a self-learning approach powered by artificial intelligence, that understands the unique “normal” for every environment and uses this baseline to spot subtle deviations indicative of emerging threats.

From the beginning, Darktrace has understood that rules and lists will never keep pace with adversaries. That’s why we’ve spent over a decade developing AI that doesn't just alert, it learns, reasons, explains, and acts.

With Cyber Assessment Framework v4.0, the bar has been raised to meet this new reality. For technical practitioners tasked with evaluating their organisation’s readiness, there are five essential questions that should guide the selection or validation of anomaly detection capabilities.

6 Questions you should ask about your security posture to align with CAF v4

1. Can your tools detect threats by identifying anomalies?

Cyber Assessment Framework v4.0 principle C1.f has been added in this version and requires that, “Threats to the operation of network and information systems, and corresponding user and system behaviour, are sufficiently understood. These are used to detect cyber security incidents.”

This marks a significant shift from traditional signature-based approaches, which rely on known Indicators of Compromise (IOCs) or predefined rules to an expectation that normal user and system behaviour is understood to an extent enabling abnormality detection.

Why this shift?

An overemphasis on threat intelligence alone leaves defenders exposed to novel threats or new variations of existing threats. By including reference to “understanding user and system behaviour” the framework is broadening the methods of threat detection beyond the use of threat intelligence and historical attack data.

While CAF v4.0 places emphasis on understanding normal user and system behaviour and using that understanding to detect abnormalities and as a result, adverse activity. There is a further expectation that threats are understood in terms of industry specific issues and that monitoring is continually updated  

Darktrace uses an anomaly-based approach to threat detection which involves establishing a dynamic baseline of “normal” for your environment, then flagging deviations from that baseline — even when there’s no known IoCs to match against. This allows security teams to surface previously unseen tactics, techniques, and procedures in real time, whether it’s:

  • An unexpected outbound connection pattern (e.g., DNS tunnelling);
  • A first-time API call between critical services;
  • Unusual calls between services; or  
  • Sensitive data moving outside normal channels or timeframes.

The requirement that organisations must be equipped to monitor their environment, create an understanding of normal and detect anomalous behaviour aligns closely with Darktrace’s capabilities.

2. Is threat hunting structured, repeatable, and improving over time?

CAF v4.0 introduces a new focus on structured threat hunting to detect adverse activity that may evade standard security controls or when such controls are not deployable.  

Principle C2.a outlines the need for documented, repeatable threat hunting processes and stresses the importance of recording and reviewing hunts to improve future effectiveness. This inclusion acknowledges that reactive threat hunting is not sufficient. Instead, the framework calls for:

  • Pre-determined and documented methods to ensure threat hunts can be deployed at the requisite frequency;
  • Threat hunts to be converted  into automated detection and alerting, where appropriate;  
  • Maintenance of threat hunt  records and post-hunt analysis to drive improvements in the process and overall security posture;
  • Regular review of the threat hunting process to align with updated risks;
  • Leveraging automation for improvement, where appropriate;
  • Focus on threat tactics, techniques and procedures, rather than one-off indicators of compromise.

Traditionally, playbook creation has been a manual process — static, slow to amend, and limited by human foresight. Even automated SOAR playbooks tend to be stock templates that can’t cover the full spectrum of threats or reflect the specific context of your organisation.

CAF v4.0 sets the expectation that organisations should maintain documented, structured approaches to incident response. But Darktrace / Incident Readiness & Recovery goes further. Its AI-generated playbooks are bespoke to your environment and updated dynamically in real time as incidents unfold. This continuous refresh of “New Events” means responders always have the latest view of what’s happening, along with an updated understanding of the AI's interpretation based on real-time contextual awareness, and recommended next steps tailored to the current stage of the attack.

The result is far beyond checkbox compliance: a living, adaptive response capability that reduces investigation time, speeds containment, and ensures actions are always proportionate to the evolving threat.

3. Do you have a proactive security posture?

Cyber Assessment Framework v4.0 does not want organisations to detect threats, it expects them to anticipate and reduce cyber risk before an incident ever occurs. That is s why principle A2.b calls for a security posture that moves from reactive detection to predictive, preventative action.

A proactive security posture focuses on reducing the ease of the most likely attack paths in advance and reducing the number of opportunities an adversary has to succeed in an attack.

To meet this requirement, organisations could benefit in looking for solutions that can:

  • Continuously map the assets and users most critical to operations;
  • Identify vulnerabilities and misconfigurations in real time;
  • Model likely adversary behaviours and attack paths using frameworks like MITRE ATT&CK; and  
  • Prioritise remediation actions that will have the highest impact on reducing overall risk.

When done well, this approach creates a real-time picture of your security posture, one that reflects the dynamic nature and ongoing evolution of both your internal environment and the evolving external threat landscape. This enables security teams to focus their time in other areas such as  validating resilience through exercises such as red teaming or forecasting.

4. Can your team/tools customize detection rules and enable autonomous responses?

CAF v4.0 places greater emphasis on reducing false positives and acting decisively when genuine threats are detected.  

The framework highlights the need for customisable detection rules and, where appropriate, autonomous response actions that can contain threats before they escalate:

The following new requirements are included:  

  • C1.c.: Alerts and detection rules should be adjustable to reduce false positives and optimise responses. Custom tooling and rules are used in conjunction with off the shelf tooling and rules;
  • C1.d: You investigate and triage alerts from all security tools and take action – allowing for improvement and prioritization of activities;
  • C1.e: Monitoring and detection personnel have sufficient understanding of operational context and deal with workload effectively as well as identifying areas for improvement (alert or triage fatigue is not present);
  • C2.a: Threat hunts should be turned into automated detections and alerting where appropriate and automation should be leveraged to improve threat hunting.

Tailored detection rules improve accuracy, while automation accelerates response, both of which help satisfy regulatory expectations. Cyber AI Analyst allows for AI investigation of alerts and can dramatically reduce the time a security team spends on alerts, reducing alert fatigue, allowing more time for strategic initiatives and identifying improvements.

5. Is your software secure and supported?  

CAF v4.0 introduced a new principle which requires software suppliers to leverage an established secure software development framework. Software suppliers must be able to demonstrate:  

  • A thorough understanding of the composition and provenance of software provided;  
  • That the software development lifecycle is informed by a detailed and up to date understanding of threat; and  
  • They can attest to the authenticity and integrity of the software, including updates and patches.  

Darktrace is committed to secure software development and all Darktrace products and internally developed systems are developed with secure engineering principles and security by design methodologies in place. Darktrace commits to the inclusion of security requirements at all stages of the software development lifecycle. Darktrace is ISO 27001, ISO 27018 and ISO 42001 Certified – demonstrating an ongoing commitment to information security, data privacy and artificial intelligence management and compliance, throughout the organisation.  

6. Is your incident response plan built on a true understanding of your environment and does it adapt to changes over time?

CAF v4.0 raises the bar for incident response by making it clear that a plan is only as strong as the context behind it. Your response plan must be shaped by a detailed, up-to-date understanding of your organisation’s specific network, systems, and operational priorities.

The framework’s updates emphasise that:

  • Plans must explicitly cover the network and information systems that underpin your essential functions because every environment has different dependencies, choke points, and critical assets.
  • They must be readily accessible even when IT systems are disrupted ensuring critical steps and contact paths aren’t lost during an incident.
  • They should be reviewed regularly to keep pace with evolving risks, infrastructure changes, and lessons learned from testing.

From government expectation to strategic advantage

Cyber Assessment Framework v4.0 signals a powerful shift in cybersecurity best practice. The newest version sets a higher standard for detection performance, risk management, threat hunting software development and proactive security posture.

For Darktrace, this is validation of the approach we have taken since the beginning: to go beyond rules and signatures to deliver proactive cyber resilience in real-time.

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Disclaimer:

This document has been prepared on behalf of Darktrace Holdings Limited. It is provided for information purposes only to provide prospective readers with general information about the Cyber Assessment Framework (CAF) in a cyber security context. It does not constitute legal, regulatory, financial or any other kind of professional advice and it has not been prepared with the reader and/or its specific organisation’s requirements in mind. Darktrace offers no warranties, guarantees, undertakings or other assurances (whether express or implied)  that: (i) this document or its content are  accurate or complete; (ii) the steps outlined herein will guarantee compliance with CAF; (iii) any purchase of Darktrace’s products or services will guarantee compliance with CAF; (iv) the steps outlined herein are appropriate for all customers. Neither the reader nor any third party is entitled to rely on the contents of this document when making/taking any decisions or actions to achieve compliance with CAF. To the fullest extent permitted by applicable law or regulation, Darktrace has no liability for any actions or decisions taken or not taken by the reader to implement any suggestions contained herein, or for any third party products, links or materials referenced. Nothing in this document negates the responsibility of the reader to seek independent legal or other advice should it wish to rely on any of the statements, suggestions, or content set out herein.  

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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September 5, 2025

Rethinking Signature-Based Detection for Power Utility Cybersecurity

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Lessons learned from OT cyber attacks

Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.

2015: BlackEnergy 3 in Ukraine

According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.

2016: Industroyer/CrashOverride

CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.

2017: TRITON Malware

The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.

The broader lessons

These events revealed three enduring truths:

  • Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
  • Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
  • Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.

The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.  

These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.

Strategic risks of overreliance on signatures

  • False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
  • Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
  • Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.

Recommended Alternatives (with real-world OT examples)

 Alternative strategies for detecting cyber attacks in OT
Figure 1: Alternative strategies for detecting cyber attacks in OT

Behavioral and anomaly detection

Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.

Real-world insight:

In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.  

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Threat intelligence sharing & building toward zero-trust philosophy

Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.

Real-world insight:

Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.

This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.

Defense-in-Depth with OT context & unified visibility

OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.

Real-world insight:

Darktrace / OT offers unified AI‑led investigations that break down silos between IT and OT. Smaller teams can see unusual outbound traffic or beaconing from unknown OT devices, swiftly investigate across domains, and get clear visibility into device behavior, even when they lack specialized OT security expertise.  

Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.  

Regulatory alignment and positive direction

Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.

This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.

Conclusion

Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.

A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.

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About the author
Daniel Simonds
Director of Operational Technology
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