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January 30, 2025

Reimagining Your SOC: Overcoming Alert Fatigue with AI-Led Investigations  

Reimagining your SOC Part 2/3: This blog explores how the challenges facing the modern SOC can be addressed by transforming the investigation process, unlocking efficiency and scalability in SOC operations with AI.
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
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface
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30
Jan 2025

The efficiency of a Security Operations Center (SOC) hinges on its ability to detect, analyze and respond to threats effectively. With advancements in AI and automation, key early SOC team metrics such as Mean Time to Detect (MTTD) have seen significant improvements:

  • 96% of defenders believing AI-powered solutions significantly boost the speed and efficiency of prevention, detection, response, and recovery.
  • Organizations leveraging AI and automation can shorten their breach lifecycle by an average of 108 days compared to those without these technologies.

While tool advances have improved performance and effectiveness in the detection phase, this has not been as beneficial to the next step of the process where initial alerts are investigated further to determine their relevance and how they relate to other activities. This is often measured with the metric Mean Time to Analysis (MTTA), although some SOC teams operate a two-level process with teams for initial triage to filter out more obviously uninteresting alerts and for more detailed analysis of the remainder. SOC teams continue to grapple with alert fatigue, overwhelmed analysts, and inefficient triage processes, preventing them from achieving the operational efficiency necessary for a high-performing SOC.

Addressing this core inefficiency requires extending AI's capabilities beyond detection to streamline and optimize the following investigative workflows that underpin effective analysis.

Challenges with SOC alert investigation

Detecting cyber threats is only the beginning of a much broader challenge of SOC efficiency. The real bottleneck often lies in the investigation process.

Detection tools and techniques have evolved significantly with the use of machine learning methods, improving early threat detection. However, after a detection pops up, human analysts still typically step in to evaluate the alert, gather context, and determine whether it’s a true threat or a false alarm and why. If it is a threat, further investigation must be performed to understand the full scope of what may be a much larger problem. This phase, measured by the mean time to analysis, is critical for swift incident response.

Challenges with manual alert investigation:

  • Too many alerts
  • Alerts lack context
  • Cognitive load sits with analysts
  • Insufficient talent in the industry
  • Fierce competition for experienced analysts

For many organizations, investigation is where the struggle of efficiency intensifies. Analysts face overwhelming volumes of alerts, a lack of consolidated context, and the mental strain of juggling multiple systems. With a worldwide shortage of 4 million experienced level two and three SOC analysts, the cognitive burden placed on teams is immense, often leading to alert fatigue and missed threats.

Even with advanced systems in place not all potential detections are investigated. In many cases, only a quarter of initial alerts are triaged (or analyzed). However, the issue runs deeper. Triaging occurs after detection engineering and alert tuning, which often disable many alerts that could potentially reveal true threats but are not accurate enough to justify the time and effort of the security team. This means some potential threats slip through unnoticed.

Understanding alerts in the SOC: Stopping cyber incidents is hard

Let’s take a look at the cyber-attack lifecycle and the steps involved in detecting and stopping an attack:

First we need a trace of an attack…

The attack will produce some sort of digital trace. Novel attacks, insider threats, and attacker techniques such as living-off-the-land can make attacker activities extremely hard to distinguish.

A detection is created…

Then we have to detect the trace, for example some beaconing to a rare domain. Initial detection alerts being raised underpin the MTTD (mean time to detection). Reducing this initial unseen duration is where we have seen significant improvement with modern threat detection tools.

When it comes to threat detection, the possibilities are vast. Your initial lead could come from anything: an alert about unusual network activity, a potential known malware detection, or an odd email. Once that lead comes in, it’s up to your security team to investigate further and determine if this is this a legitimate threat or a false alarm and what the context is behind the alert.

Investigation begins…

It doesn’t just stop at a detection. Typically, humans also need to look at the alert, investigate, understand, analyze, and conclude whether this is a genuine threat that needs a response. We normally measure this as MTTA (mean time to analyze).

Conducting the investigation effectively requires a high degree of skill and efficiency, as every second counts in mitigating potential damage. Security teams must analyze the available data, correlate it across multiple sources, and piece together the timeline of events to understand the full scope of the incident. This process involves navigating through vast amounts of information, identifying patterns, and discerning relevant details. All while managing the pressure of minimizing downtime and preventing further escalation.

Containment begins…

Once we confirm something as a threat, and the human team determines a response is required and understand the scope, we need to contain the incident. That's normally the MTTC (mean time to containment) and can be further split into immediate and more permanent measures.

For more about how AI-led solutions can help in the containment stage read here: Autonomous Response: Streamlining Cybersecurity and Business Operations

The challenge is not only in 1) detecting threats quickly, but also 2) triaging and investigating them rapidly and with precision, and 3) prioritizing the most critical findings to avoid missed opportunities. Effective investigation demands a combination of advanced tools, robust workflows, and the expertise to interpret and act on the insights they generate. Without these, organizations risk delaying critical containment and response efforts, leaving them vulnerable to greater impacts.

While there are further steps (remediation, and of course complete recovery) here we will focus on investigation.

Developing an AI analyst: How Darktrace replicates human investigation

Darktrace has been working on understanding the investigative process of a skilled analyst since 2017. By conducting internal research between Darktrace expert SOC analysts and machine learning engineers, we developed a formalized understanding of investigative processes. This understanding formed the basis of a multi-layered AI system that systematically investigates data, taking advantage of the speed and breadth afforded by machine systems.

With this research we found that the investigative process often revolves around iterating three key steps: hypothesis creation, data collection, and results evaluation.

All these details are crucial for an analyst to determine the nature of a potential threat. Similarly, they are integral components of our Cyber AI Analyst which is an integral component across our product suite. In doing so, Darktrace has been able to replicate the human-driven approach to investigating alerts using machine learning speed and scale.

Here’s how it works:

  • When an initial or third-party alert is triggered, the Cyber AI Analyst initiates a forensic investigation by building multiple hypotheses and gathering relevant data to confirm or refute the nature of suspicious activity, iterating as necessary, and continuously refining the original hypothesis as new data emerges throughout the investigation.
  • Using a combination of machine learning including supervised and unsupervised methods, NLP and graph theory to assess activity, this investigation engine conducts a deep analysis with incidents raised to the human team only when the behavior is deemed sufficiently concerning.
  • After classification, the incident information is organized and processed to generate the analysis summary, including the most important descriptive details, and priority classification, ensuring that critical alerts are prioritized for further action by the human-analyst team.
  • If the alert is deemed unimportant, the complete analysis process is made available to the human team so that they can see what investigation was performed and why this conclusion was drawn.
Darktrace cyber ai analyst workflow, how it works

To illustrate this via example, if a laptop is beaconing to a rare domain, the Cyber AI Analyst would create hypotheses including whether this could be command and control traffic, data exfiltration, or something else. The AI analyst then collects data, analyzes it, makes decisions, iterates, and ultimately raises a new high-level incident alert describing and detailing its findings for human analysts to review and follow up.

Learn more about Darktrace's Cyber AI Analyst

  • Cost savings: Equivalent to adding up to 30 full-time Level 2 analysts without increasing headcount
  • Minimize business risk: Takes on the busy work from human analysts and elevates a team’s overall decision making
  • Improve security outcomes: Identifies subtle, sophisticated threats through holistic investigations

Unlocking an efficient SOC

To create a mature and proactive SOC, addressing the inefficiencies in the alert investigation process is essential. By extending AI's capabilities beyond detection, SOC teams can streamline and optimize investigative workflows, reducing alert fatigue and enhancing analyst efficiency.

This holistic approach not only improves Mean Time to Analysis (MTTA) but also ensures that SOCs are well-equipped to handle the evolving threat landscape. Embracing AI augmentation and automation in every phase of threat management will pave the way for a more resilient and proactive security posture, ultimately leading to a high-performing SOC that can effectively safeguard organizational assets.

Every relevant alert is investigated

The Cyber AI Analyst is not a generative AI system, or an XDR or SEIM aggregator that simply prompts you on what to do next. It uses a multi-layered combination of many different specialized AI methods to investigate every relevant alert from across your enterprise, native, 3rd party, and manual triggers, operating at machine speed and scale. This also positively affects detection engineering and alert tuning, because it does not suffer from fatigue when presented with low accuracy but potentially valuable alerts.

Retain and improve analyst skills

Transferring most analysis processes to AI systems can risk team skills if they don't maintain or build them and if the AI doesn't explain its process. This can reduce the ability to challenge or build on AI results and cause issues if the AI is unavailable. The Cyber AI Analyst, by revealing its investigation process, data gathering, and decisions, promotes and improves these skills. Its deep understanding of cyber incidents can be used for skill training and incident response practice by simulating incidents for security teams to handle.

Create time for cyber risk reduction

Human cybersecurity professionals excel in areas that require critical thinking, strategic planning, and nuanced decision-making. With alert fatigue minimized and investigations streamlined, your analysts can avoid the tedious data collection and analysis stages and instead focus on critical decision-making tasks such as implementing recovery actions and performing threat hunting.

Stay tuned for part 3/3

Part 3/3 in the Reimagine your SOC series explores the preventative security solutions market and effective risk management strategies.

Coming soon!

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
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

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April 29, 2025

MFA Under Attack: AiTM Phishing Kits Abusing Legitimate Services

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In late 2024 and early 2025, the Darktrace Security Operations Center (SOC) investigated alerts regarding separate cases of Software-as-a-Service (SaaS) account compromises on two customer environments that presented several similarities, suggesting they were part of a wider phishing campaign.

This campaign was found to leverage the project collaboration and note-taking application, Milanote, and the Tycoon 2FA phishing kit.

Legitimate services abused

As highlighted in Darktrace's 2024 Annual Threat Report [1], threat actors are abusing legitimate services, like Milanote, in their phishing campaigns. By leveraging these trusted platforms and domains, malicious actors can bypass traditional security measures, making their phishing emails appear benign and increasing the likelihood of successful attacks.

Darktrace categorizes these senders and platforms as free content senders. These services allow users to send emails containing custom content (e.g., files) from fully validated, fixed service address belonging to legitimate corporations. Although some of these services permit full body and subject customization by attackers, the structure of these emails is generally consistent, making it challenging to differentiate between legitimate and malicious emails.

What is Tycoon 2FA?

Tycoon 2FA is an Adversary-in-the-Middle (AitM) phishing kit, first seen in August 2023 and distributed via the Phishing-as-a-Service (PhaaS) model [2]. It targets multi-factor authentication (MFA) by intercepting credentials and MFA tokens during authentication on fake Microsoft or Google login pages. The attacker captures session cookies after MFA is completed, allowing them to replay the session and access the user account, even if credentials are reset. The rise in MFA use has increased the popularity of AitM phishing kits like Tycoon 2FA and Mamba 2FA, another AiTM phishing kit investigated by Darktrace.

Initial access via phishing email

At the beginning of 2025, Darktrace observed phishing emails leveraging Milanote being sent to multiple internal recipients in an organization. In this attack, the same email was sent to 19 different users, all of which were held by Darktrace.

The subject line of the emails mentioned both a legitimate internal user of the company, the company name, as well as a Milanote board regarding a “new agreement” in German. It is a common social engineering technique to mention urgent matters, such as unpaid invoices, expired passwords, or awaiting voicemails, in the subject line to prompt immediate action from the user. However, this tactic is now widely covered in phishing awareness training, making users more suspicious of such emails. In this case, while the subject mentioned a “new agreement,” likely raising the recipient’s curiosity, the tone remained professional and not overly alarming. Additionally, the mention of a colleague and the standardized language typical of free content sender emails further helped dispel concerns regarding the email.

These emails were sent by the legitimate address support@milanote[.]com and referenced "Milanote" in the personal field of the header but originated from the freemail address “ahnermatternk.ef.od.13@gmail[.]com”. Darktrace / EMAIL recognized that none of the recipients had previously received a file share email from Milanote, making this sender unfamiliar in the customer's email environment

The emails contained several benign links to legitimate Milanote endpoints (including an unsubscribe link) which were not flagged by Darktrace. However, they also included a malicious link designed to direct recipients to a pre-filled credential harvesting page hosted on Milanote, prompting them to register for an account. Despite not blocking the legitimate Milanote links in the same email, Darktrace locked the malicious link, preventing users from visiting the credential harvester.

Credential harvesting page sent to recipients, as seen in. sandbox environment.
Figure 1: Credential harvesting page sent to recipients, as seen in. sandbox environment.

Around one minute later, one recipient received a legitimate email from Milanote confirming their successful account registration, indicating they had accessed the phishing page. This email had a lower anomaly score and was not flagged by Darktrace / EMAIL because, unlike the first email, it did not contain any suspicious links and was a genuine account registration notification. Similarly, in the malicious Milanote email, only the link leading to the phishing page was blocked, while the benign and legitimate Milanote links remained accessible, demonstrating Darktrace’s precise and targeted actioning.

A legitimate and a malicious Milanote email received by one recipient.
Figure 2: A legitimate and a malicious Milanote email received by one recipient.

Around the same time, Darktrace / NETWORK observed the same user’s device making DNS query for the domain name “lrn.ialeahed[.]com” , which has been flagged as a Tycoon 2FA domain [2], suggesting the use of this phishing platform.

Once the user had entered their details in the credential harvester, it is likely that they were presented a document hosted on Milanote that contained the final payload link – likely hidden behind text instructing users to access a “new agreement” document.

External research indicates that the user was likely directed to a Cloudflare Turnstile challenge meant to reroute unwanted traffic, such as automated security scripts and penetration testing tools [2] [3]. After these checks and other background processes are completed, the user is directed to the final landing page. In this case, it was likely a fake login prompt hosted on the attacker’s server, where the user is asked to authenticate to their account using MFA. By burrowing malicious links and files in this manner, threat actors can evade analysis by traditional security email gateways, effectively bypassing their protection.

Darktrace’s analysis of the structure and word content of the phishing emails resulted in an 82% probability score that the email was malicious, and the email further received a 67% phishing inducement score, representing how closely the structure and word content of the emails compared to typical phishing emails.

All these unusual elements triggered multiple alerts in Darktrace / EMAIL, focusing on two main suspicious aspects: a new, unknown sender with no prior correspondence with the recipients or the environment, and the inclusion of a link to a previously unseen file storage solution.

Milanote phishing email as seen within Darktrace / EMAIL.
Figure 3: Milanote phishing email as seen within Darktrace / EMAIL.

After detecting the fifth email, the “Sender Surge” model alert was triggered in Darktrace / EMAIL due to a significant number of recipients being emailed by this new suspicious sender in a short period. These recipients were from various departments across the customer’s organization, including sales, marketing, purchasing, and production. Darktrace / EMAIL determined that the emails were sent to a highly unusual group of internal recipients, further raising doubts about the business legitimacy.

Darktrace / EMAIL suggested actions to contain the attack by holding all Milanote phishing emails back from recipient’s inboxes, except for the detailed email with locked links. However, autonomous actions were not enabled at the time, allowing the initial email to reach recipients' inboxes, providing a brief window for interaction. Unfortunately, during this window, one recipient clicked on the Milanote payload link, leading to the compromise of their account.

SaaS account takeover

About three minutes after the malicious Milanote email was received, Darktrace / IDENTITY detected an unusual login to the email recipient’s SaaS account. The SaaS actor was observed accessing files from their usual location in Germany, while simultaneously, a 100% rare login occurred from a location in the US that had never been seen in the customer’s environment before. This login was also flagged as suspicious by Microsoft 365, triggering a 'Conditional Access Policy' that required MFA authentication, which was successfully completed.

Tycoon 2FA adnimistration panel login page dated from October 2023 [3].
Figure 4: Tycoon 2FA adnimistration panel login page dated from October 2023 [3].

Despite the successful authentication, Darktrace / IDENTITY recognized that the login from this unusual location, coupled with simultaneous activity in another geographically distant location, were highly suspicious. Darktrace went on to observe MFA-validated logins from three separate US-based IP addresses: 89.185.80[.]19, 5.181.3[.]68, and 38.242.7[.]252. Most of the malicious activity was performed from the latter, which is associated with the Hide My Ass (HMA) VPN network [5].

Darktrace’s detection of the suspicious login from the US while the legitimate user was logged in from Germany.
Figure 5: Darktrace’s detection of the suspicious login from the US while the legitimate user was logged in from Germany.
Darktrace’s detection of the suspicious login following successful MFA authentication.
Figure 6: Darktrace’s detection of the suspicious login following successful MFA authentication.

Following this, the malicious actor accessed the user’s inbox and created a new mailbox rule named “GTH” that deleted any incoming email containing the string “milanote” in the subject line or body. Rules like this are a common technique used by attackers to leverage compromised accounts for launching phishing campaigns and concealing replies to phishing emails that might raise suspicions among legitimate account holders. Using legitimate, albeit compromised, accounts to send additional phishing emails enhances the apparent legitimacy of the malicious emails. This tactic has been reported as being used by Tycoon 2FA attackers [4].

The attacker accessed over 140 emails within the legitimate user’s inbox, including both the inbox and the “Sent Items” folder. Notably, the attacker accessed five emails in the “Sent Items” folder and modified their attachments. These emails were mainly related to invoices, suggesting the threat actor may have been looking to hijack those email threads to send fake invoices or replicate previous invoice emails.

Darktrace’s Cyber AI AnalystTM launched autonomous investigations into the individual events surrounding this suspicious activity. It connected these separate events into a single, broad account takeover incident, providing the customer with a clearer view of the ongoing compromise.

Cyber AI Analyst’s detection of unusual SaaS account activities in a single incident.
Figure 7: Cyber AI Analyst’s detection of unusual SaaS account activities in a single incident.
Cyber AI Analyst investigation of suspicious activities performed by the attacker.
Figure 8: Cyber AI Analyst investigation of suspicious activities performed by the attacker.

Darktrace's response

Within three minutes of the first unusual login alert, Darktrace’s Autonomous Response intervened, disabling the compromised user account for two hours.

As the impacted customer was subscribed to the Managed Threat Detection Service, Darktrace’s SOC team investigated the activity further and promptly alerted the customer’s security team. With the user’s account still disabled by Autonomous Response, the attack was contained, allowing the customer’s security team valuable time to investigate and remediate. Within ten minutes of receiving the alert from Darktrace’s SOC, they reset the user’s password, closed all active SaaS sessions, and deleted the malicious email rule. Darktrace’s SOC further supported the customer through the Security Operations Service Support service by providing information about the data accessed and identifying any other affected users.

Autonomous Response actions carried out by Darktrace / IDENTITY to contain the malicious activity
Figure 9: Autonomous Response actions carried out by Darktrace / IDENTITY to contain the malicious activity.

A wider Milanote phishing campaign?

Around a month before this compromise activity, Darktrace alerted another customer to similar activities involving two compromised user accounts. These accounts created new inbox rules named “GFH” and “GVB” to delete all incoming emails containing the string “milanote” in their subject line and/or body.

The phishing emails that led to the compromise of these user accounts were similar to the ones discussed above. Specifically, these emails were sent via the Milanote platform and referenced a “new agreement” (in Spanish) being shared by a colleague. Additionally, the payload link included in the phishing emails showed the same UserPrincipalName (UPN) attribute (i.e., click?upn=u001.qLX9yCzR), which has been seen in other Milanote phishing emails leveraging Tycoon 2FA reported by OSINT sources [6]. Interestingly, in some cases, the email also referenced a “new agreement” in Portuguese, indicating a global campaign.

Based on the similarities in the rule’s naming convention and action, as well as the similarities in the phishing email subjects, it is likely that these were part of the same campaign leveraging Milanote and Tycoon 2FA to compromise user accounts. Since its introduction, the Tycoon 2FA phishing kit has undergone several enhancements to increase its stealth and obfuscation methods, making it harder for security tools to detect. For example, the latest versions contain special source code to obstruct web page analysis by defenders, prevent users from copying meaningful text from the phishing webpages, and disable the right-click menu to prevent offline analysis [4].

Conclusion

Threat actors are continually employing new methods to bypass security detection tools and measures. As highlighted in this blog, even robust security mechanisms like MFA can be compromised using AitM phishing kits. The misuse of legitimate services such as Milanote for malicious purposes can help attackers evade traditional email security solutions by blurring the distinction between legitimate and malicious content.

This is why security tools based on anomaly detection are crucial for defending against such attacks. However, user awareness is equally important. Delays in processing can impact the speed of response, making it essential for users to be informed about these threats.

Appendices

References

[1] https://www.darktrace.com/resources/annual-threat-report-2024

[2] https://www.validin.com/blog/tycoon_2fa_analyzing_and_hunting_phishing-as-a-service_domains

[3] https://blog.sekoia.io/tycoon-2fa-an-in-depth-analysis-of-the-latest-version-of-the-aitm-phishing-kit/#h-iocs-amp-technical-details

[4] https://blog.barracuda.com/2025/01/22/threat-spotlight-tycoon-2fa-phishing-kit

[5] https://spur.us/context/38.242.7.252    

[6] https://any.run/report/5ef1ac94e4c6c1dc35579321c206453aea80d414108f9f77abd2e2b03ffbd658/be5351d9-53c0-470b-8708-ee2e29300e70

Indicators of Compromise (IoCs)

IoC         Type      Description + Probability

89.185.80[.]19 - IP Address - Malicious login

5.181.3[.]68 - IP Address -Malicious login

38.242.7[.]252 - IP Address - Malicious login and new email inbox rule creation -  Hide My Ass VPN

lrn.ialeahed[.]com – Hostname - Likely Tycoon 2FA domain

Darktrace Model Detections

Email alerts

Platforms / Free Content Sender + High Sender Surge

Platforms / Free Content Sender + Sender Surge

Platforms / Free Content Sender + Unknown Initiator

Platforms / Free Content Sender

Platforms / Free Content Sender + First Time Recipient

Unusual / New Sender Surge

Unusual / Sender Surge

Antigena Anomaly / High Antigena Anomaly

Association / Unknown Sender

History / New Sender

Link / High Rarity Link to File Storage

Link/ Link To File Storage

Link / Link to File Storage + Unknown Sender

Link / Low Link Association

Platforms / Free Content Sender + First Time Initiator

Platforms / Free Content Sender + Unknown Initiator + Freemail

Platforms / Free Content Sender Link

Unusual / Anomalous Association

Unusual / Unlikely Recipient Association

IDENTITY

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compromise / Login from Rare High Risk Endpoint

SaaS / Access / M365 High Risk Level Login

SaaS / Compromise / Login From Rare Endpoint While User Is Active

SaaS / Access / MailItemsAccessed from Rare Endpoint

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

SaaS / Compliance / Anomalous New Email Rule

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS User Block

Antigena / SaaS / Antigena Unusual Activity Block

Antigena / SaaS / Antigena Suspicious SaaS and Email Activity Block

Cyber AI Analyst Incident

Possible Hijack of Office365 Account

MITRE ATT&CK Mapping

Tactic – Technique

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - Cloud Accounts

INITIAL ACCESS - Phishing

CREDENTIAL ACCESS - Steal Web Session Cookie

PERSISTENCE - Account Manipulation

PERSISTENCE - Outlook Rules

RESOURCE DEVELOPMENT - Email Accounts

RESOURCE DEVELOPMENT - Compromise Accounts

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About the author
Alexandra Sentenac
Cyber Analyst

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April 29, 2025

The Importance of NDR in Resilient XDR

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As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.

Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.

This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.

Threat actors see local security agents as targets

Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.

The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

With the local EDR agent silently disabled or evaded, how will the threat be discovered?

What are the limitations of relying solely on EDR?

Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.

In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:

“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”

This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.

Why is network detection & response (NDR) critical?

An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.

An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.

An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.

Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.

Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.

This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.

Unlocking adaptive protection for evolving cyber risks

Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.

With its ability to uncover previously unknown threats as well as detect known threats Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.  

Stay tuned for part II of this series which dives deeper into the differences between NDR types.

Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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