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November 15, 2022

Early-Adopter Customers on Darktrace PREVENT

Discover crucial insights from early adopters of Darktrace Prevent and how this cybersecurity tool is making a huge difference for organizations.
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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.
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15
Nov 2022

Darktrace PREVENT™

PREVENT empowers the CISO and the security team to reduce cyber risk by continuously monitoring the organization’s internal and external attack surface, highlighting and prioritizing risks, and then autonomously hardening defenses as part of Darktrace’s Cyber AI Loop. PREVENT, which is now generally available, is already proving its value to early-adopter customers. 

“We know that the bad guys are gaining knowledge every day. We need to as well. And I think that this type of proactive approach is a requirement now. I don’t think it is an option,” said Jim Davies, the Director of IT at US supply chain management company Ongweoweh.

PREVENT brings together several capabilities, including attack surface management, attack path modeling, breach and attack emulation, and pentest augmentation. By combining these into one end-to-end solution, the system and the humans who use it benefit from a full understanding of which countermeasures will mitigate risk to the greatest extent. 

While the security team works on these countermeasures, PREVENT feeds its findings into Darktrace’s DETECTTM and RESPONDTM capabilities, which in turn harden defenses by heightening their sensitivity around risky assets. This happens autonomously, so the human security team can prioritize other work while the AI continuously hardens the security stack.

Surfacing Risks on the External Attack Surface

The Darktrace PREVENT product family currently consists of two interconnected modules: PREVENT/Attack Surface ManagementTM (ASM) and PREVENT/End-to-EndTM (E2E). 

PREVENT/ASM uses AI to distinguish the company’s external assets on the internet, while only requiring the company’s brand name as input. Early adopters saw it reveal 30-50% more assets than they realized they had. 

“As early as the proof of concept, there was demonstrated value with PREVENT which revealed some attack surface opportunities that none of our other security providers had come across.” said Jenny Moshea, Direct of Technology for Sellen Construction.

PREVENT/ASM is now being adopted by organizations large and small across a number of industries, revealing a wide range of surprising risks and vulnerabilities the security team was not previously aware of. 

In one trial at a utilities organization, PREVENT/ASM identified unexpected access to a control system that was mission critical and could potentially impact the water facilities. Another customer was testing a new project in a cloud environment that was not meant to be publicly visible, let alone accessible. After PREVENT/ASM revealed that sensitive data was exposed and at risk of falling into the wrong hands, the security team was able to proactively get ahead of this risk by reconfiguring the system. 

A Level Deeper: An Internal View of Risk

While PREVENT/ASM examines a company’s external assets, PREVENT/E2E leverages the AI understanding of a company’s internal digital infrastructure. This industry-first product consolidates and optimizes several risk management capabilities, including attack path modeling, pentest augmentation, breach and attack simulation, security awareness training, and cyber risk prioritization. 

One early adopter benefited from PREVENT/E2E’s evolving insights, finding that it filled in the gaps of unknown risk between pentests.   

“We’ve run pentests maybe four times a year, that’s at that point in time. We go correct those issues and then we’re basically waiting for the next one before we dig into it. As soon as we saw the tool, we were like wow this is a continual test every day, we’re able to go take a quick peek, see what’s going on out in the environment,” said Mike Sherwood, the Chief Information Officer for the City of Las Vegas.

After assessing the exposure, likelihood, and potential damage of every single device and attack path in the organization, PREVENT/E2E uncovered a major risk in one customer’s environment:  a patch had failed to install on the disaster recovery domain controller – a vulnerability which the security team had not previously been aware of. With PREVENT’s findings, the team was able to quickly address and close this significant risk. 

Another customer deployed PREVENT/E2E and discovered that the building’s air conditioning system was accessed by an account that had domain admin privileges. PREVENT/E2E informed the security team of this configuration, which would have allowed a threat actor easy lateral movement after targeting the IoT device. 

An End-to-End Solution

Having established the most critical attack paths, PREVENT/E2E enables customers to test the validity of these attack paths through emulated attack campaigns. One customer was amazed to discover that the technology had learned the idiosyncrasies of a user’s communication patterns and launched an emulated social engineering attack that reflected the common spelling mistakes of the user being impersonated. 

By learning how susceptible users are to social engineering attacks, the system gains an even better idea of how likely a particular attack path is, and factors this into the prioritization of its risk mitigation advice. This is yet another indicator of how combining different preventative cyber security measures into one solution gives the security team the insights they need to take practical, effective action to reduce cyber risk. 

PREVENT has already boosted the cyber security postures of its early adopters, surfacing misconfigurations, brand abuse, shadow IT, and other significant risks. 

“PREVENT is an incredibly helpful way to understand risk, particularly when comparing changes over time,” said Klint Price, the Head of Technology & Cybersecurity at facilities management company Vixxo. “Understanding vulnerabilities is one thing, but actually being able to digest and prioritize them is even better.”

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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.
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July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

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The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1.  Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

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Carlos Gray
Senior Product Marketing Manager, Email

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July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

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Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

Detecting threats without relying on CVE disclosure

Abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems. Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Often, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender: attack vs patch.

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security. After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

Autonomous Response

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

  1. https://www.first.org/blog/20250607-Vulnerability-Forecast-for-2025
  2. https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575
  3. https://thehackernews.com/2025/05/china-linked-hackers-exploit-sap-and.html

Related Darktrace blogs:

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

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