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April 25, 2022

How AI Keeps Priefert Productive and Secure

Find out how AI empowers Priefert Manufacturing to stay productive and secure, with insights from Darktrace.
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
Mike Autrey
Lead Network Administrator at Priefert (Guest Contributor)
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25
Apr 2022

Founded in 1964, Priefert Manufacturing has grown into one of the largest farm, ranch, and rodeo equipment manufacturers in the world. With a huge range of equipment in locations that span several acres in the US, it is imperative that all our devices can safely communicate in real time.

We recognize that our biggest vulnerability comes from within – from our own employees. With one misstep or oversight, from a neglected software download to an accidental engagement with a phishing email, a threat actor can get inside our systems and potentially disrupt our business.

A lot of our employees are non-technical, and to continue being productive, we have to accept the risk that comes with giving computer system access to users who are unfamiliar with certain technologies and security protocols.

So, we needed another layer of security that went beyond our existing controls: something that could pick up on any malicious activity within our systems, wherever it may arise and however subtle it may be. Security should never hamper productivity, and we needed technology that could intervene in real time, so that we could keep giving our users access to systems, without having to worry so much about a breach.

As we looked into solutions, we decided to install Darktrace’s AI. We were drawn to the fact that it was effective with virtually any type of technology and had the ability to both detect and take autonomous action against attacks.

Stopping ransomware in its tracks

We had only just begun deploying Antigena, Darktrace’s Autonomous Response technology, when it detected and shut down a ransomware attack.

Still in its learning phase, Darktrace was beginning to understand the ‘pattern of life’ across our digital infrastructure when it discovered strange activity on one of our public-facing servers: a series of highly unusual and suspicious connections.

Alerted to the activity, we went ahead and switched Antigena to Active Mode, and we saw the technology in action: it blocked connections to the suspect IP addresses and allowed me to kill the malware on the server, without further spread. Before the ransomware had the chance to create any real damage, Darktrace had shut it down.

We started to understand the full capacity of the technology: not only could it stop in-progress attacks at machine speed, but it was uncovering activity in our network that was previously invisible to us. If we are hit by another similar attack in the future, with Antigena now fully autonomous in our environment, we know that it will take action on its own, responding to any threat in seconds.

Thwarting phishing attempts

Our experience with Darktrace for SaaS was a similar story. We had just begun rolling out the technology for our Microsoft 365 users when it identified one user account that had been compromised.

At 02:00, a few failed login attempts paired with odd timing prompted Darktrace to flag the account as having unusual user behavior and notify our IT team. Alerted to the situation, we confirmed the account had been hijacked and the threat actor was attempting to send out phishing emails.

Darktrace enabled our team to understand what was going on quickly. With all the information in front of us, we could see that the user did not have multi-factor authentication enabled. They had reused their password for multiple accounts, which meant the attacker was able to get a hold of their credentials. Swiftly, our team attended to the account, halting the outbound emails, and terminating the hacker’s access.

Before Darktrace, we would have never known to activate multi-factor authentication and change the password on this account because we wouldn’t have been aware that the account was exhibiting abnormal behavior in the first place. Previously, until there was a problem, we were left blind to what was going on in our network.

Staying ahead of the threat

As we continue to give our employees more access to new IT systems, we remain confident that Darktrace will neutralize any threat that may arise from a human error before it becomes a crisis. The technology has empowered our team to be proactive instead of reactive – no longer are we reliant on retrospective data and left unaware of a situation until it’s too late.

Without having to go and dig through loads of information, we are notified of potential problems before something or someone in our network presents a problem. We don’t need to wait for any sign of an attack to manifest before we can take action.

The technology has also freed up an extraordinary amount of time for myself, no longer having to focus on manually responding to things that pop up in our systems. I can now spend my time on work I’d like to prioritize, without sacrificing security.

Having a single AI system operate across our entire digital estate – whether it be our network, cloud, or Microsoft 365 users – has only further enhanced the protection Darktrace gives us. It has enabled the technology to absorb huge amounts of data, strengthening its understanding of our environment at the most granular level, so it can pick up on the slightest anomalies indicative of a cyber threat.

And because Darktrace’s AI protects all our digital environments, there are no gaps in protection. Not only can we detect threats that develop in our network, cloud, and email, but we can also now see the full scope of an incident as it progresses across multiple areas of our digital estate. Darktrace shines a light on everything.

Hear more from Darktrace customers

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
Mike Autrey
Lead Network Administrator at Priefert (Guest Contributor)

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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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About the author
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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