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October 30, 2023

Exploring AI Threats: Package Hallucination Attacks

Learn how malicious actors exploit errors in generative AI tools to launch packet attacks. Read how Darktrace products detect and prevent these threats!
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
Charlotte Thompson
Cyber Analyst
Written by
Tiana Kelly
Deputy Team Lead, London & Cyber Analyst
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30
Oct 2023

AI tools open doors for threat actors

On November 30, 2022, the free conversational language generation model ChatGPT was launched by OpenAI, an artificial intelligence (AI) research and development company. The launch of ChatGPT was the culmination of development ongoing since 2018 and represented the latest innovation in the ongoing generative AI boom and made the use of generative AI tools accessible to the general population for the first time.

ChatGPT is estimated to currently have at least 100 million users, and in August 2023 the site reached 1.43 billion visits [1]. Darktrace data indicated that, as of March 2023, 74% of active customer environments have employees using generative AI tools in the workplace [2].

However, with new tools come new opportunities for threat actors to exploit and use them maliciously, expanding their arsenal.

Much consideration has been given to mitigating the impacts of the increased linguistic complexity in social engineering and phishing attacks resulting from generative AI tool use, with Darktrace observing a 135% increase in ‘novel social engineering attacks’ across thousands of active Darktrace/Email™ customers from January to February 2023, corresponding with the widespread adoption of ChatGPT and its peers [3].

Less overall consideration, however, has been given to impacts stemming from errors intrinsic to generative AI tools. One of these errors is AI hallucinations.

What is an AI hallucination?

AI “hallucination” is a term which refers to the predictive elements of generative AI and LLMs’ AI model gives an unexpected or factually incorrect response which does not align with its machine learning training data [4]. This differs from regular and intended behavior for an AI model, which should provide a response based on the data it was trained upon.  

Why are AI hallucinations a problem?

Despite the term indicating it might be a rare phenomenon, hallucinations are far more likely than accurate or factual results as the AI models used in LLMs are merely predictive and focus on the most probable text or outcome, rather than factual accuracy.

Given the widespread use of generative AI tools in the workplace employees are becoming significantly more likely to encounter an AI hallucination. Furthermore, if these fabricated hallucination responses are taken at face value, they could cause significant issues for an organization.

Use of generative AI in software development

Software developers may use generative AI for recommendations on how to optimize their scripts or code, or to find packages to import into their code for various uses. Software developers may ask LLMs for recommendations on specific pieces of code or how to solve a specific problem, which will likely lead to a third-party package. It is possible that packages recommended by generative AI tools could represent AI hallucinations and the packages may not have been published, or, more accurately, the packages may not have been published prior to the date at which the training data for the model halts. If these hallucinations result in common suggestions of a non-existent package, and the developer copies the code snippet wholesale, this may leave the exchanges vulnerable to attack.

Research conducted by Vulcan revealed the prevalence of AI hallucinations when ChatGPT is asked questions related to coding. After sourcing a sample of commonly asked coding questions from Stack Overflow, a question-and-answer website for programmers, researchers queried ChatGPT (in the context of Node.js and Python) and reviewed its responses. In 20% of the responses provided by ChatGPT pertaining to Node.js at least one un-published package was included, whilst the figure sat at around 35% for Python [4].

Hallucinations can be unpredictable, but would-be attackers are able to find packages to create by asking generative AI tools generic questions and checking whether the suggested packages exist already. As such, attacks using this vector are unlikely to target specific organizations, instead posing more of a widespread threat to users of generative AI tools.

Malicious packages as attack vectors

Although AI hallucinations can be unpredictable, and responses given by generative AI tools may not always be consistent, malicious actors are able to discover AI hallucinations by adopting the approach used by Vulcan. This allows hallucinated packages to be used as attack vectors. Once a malicious actor has discovered a hallucination of an un-published package, they are able to create a package with the same name and include a malicious payload, before publishing it. This is known as a malicious package.

Malicious packages could also be recommended by generative AI tools in the form of pre-existing packages. A user may be recommended a package that had previously been confirmed to contain malicious content, or a package that is no longer maintained and, therefore, is more vulnerable to hijack by malicious actors.

In such scenarios it is not necessary to manipulate the training data (data poisoning) to achieve the desired outcome for the malicious actor, thus a complex and time-consuming attack phase can easily be bypassed.

An unsuspecting software developer may incorporate a malicious package into their code, rendering it harmful. Deployment of this code could then result in compromise and escalation into a full-blown cyber-attack.

Figure 1: Flow diagram depicting the initial stages of an AI Package Hallucination Attack.

For providers of Software-as-a-Service (SaaS) products, this attack vector may represent an even greater risk. Such organizations may have a higher proportion of employed software developers than other organizations of comparable size. A threat actor, therefore, could utilize this attack vector as part of a supply chain attack, whereby a malicious payload becomes incorporated into trusted software and is then distributed to multiple customers. This type of attack could have severe consequences including data loss, the downtime of critical systems, and reputational damage.

How could Darktrace detect an AI Package Hallucination Attack?

In June 2023, Darktrace introduced a range of DETECT™ and RESPOND™ models designed to identify the use of generative AI tools within customer environments, and to autonomously perform inhibitive actions in response to such detections. These models will trigger based on connections to endpoints associated with generative AI tools, as such, Darktrace’s detection of an AI Package Hallucination Attack would likely begin with the breaching of one of the following DETECT models:

  • Compliance / Anomalous Upload to Generative AI
  • Compliance / Beaconing to Rare Generative AI and Generative AI
  • Compliance / Generative AI

Should generative AI tool use not be permitted by an organization, the Darktrace RESPOND model ‘Antigena / Network / Compliance / Antigena Generative AI Block’ can be activated to autonomously block connections to endpoints associated with generative AI, thus preventing an AI Package Hallucination attack before it can take hold.

Once a malicious package has been recommended, it may be downloaded from GitHub, a platform and cloud-based service used to store and manage code. Darktrace DETECT is able to identify when a device has performed a download from an open-source repository such as GitHub using the following models:

  • Device / Anomalous GitHub Download
  • Device / Anomalous Script Download Followed By Additional Packages

Whatever goal the malicious package has been designed to fulfil will determine the next stages of the attack. Due to their highly flexible nature, AI package hallucinations could be used as an attack vector to deliver a large variety of different malware types.

As GitHub is a commonly used service by software developers and IT professionals alike, traditional security tools may not alert customer security teams to such GitHub downloads, meaning malicious downloads may go undetected. Darktrace’s anomaly-based approach to threat detection, however, enables it to recognize subtle deviations in a device’s pre-established pattern of life which may be indicative of an emerging attack.

Subsequent anomalous activity representing the possible progression of the kill chain as part of an AI Package Hallucination Attack could then trigger an Enhanced Monitoring model. Enhanced Monitoring models are high-fidelity indicators of potential malicious activity that are investigated by the Darktrace analyst team as part of the Proactive Threat Notification (PTN) service offered by the Darktrace Security Operation Center (SOC).

Conclusion

Employees are often considered the first line of defense in cyber security; this is particularly true in the face of an AI Package Hallucination Attack.

As the use of generative AI becomes more accessible and an increasingly prevalent tool in an attacker’s toolbox, organizations will benefit from implementing company-wide policies to define expectations surrounding the use of such tools. It is simple, yet critical, for example, for employees to fact check responses provided to them by generative AI tools. All packages recommended by generative AI should also be checked by reviewing non-generated data from either external third-party or internal sources. It is also good practice to adopt caution when downloading packages with very few downloads as it could indicate the package is untrustworthy or malicious.

As of September 2023, ChatGPT Plus and Enterprise users were able to use the tool to browse the internet, expanding the data ChatGPT can access beyond the previous training data cut-off of September 2021 [5]. This feature will be expanded to all users soon [6]. ChatGPT providing up-to-date responses could prompt the evolution of this attack vector, allowing attackers to publish malicious packages which could subsequently be recommended by ChatGPT.

It is inevitable that a greater embrace of AI tools in the workplace will be seen in the coming years as the AI technology advances and existing tools become less novel and more familiar. By fighting fire with fire, using AI technology to identify AI usage, Darktrace is uniquely placed to detect and take preventative action against malicious actors capitalizing on the AI boom.

Credit to Charlotte Thompson, Cyber Analyst, Tiana Kelly, Analyst Team Lead, London, Cyber Analyst

References

[1] https://seo.ai/blog/chatgpt-user-statistics-facts

[2] https://darktrace.com/news/darktrace-addresses-generative-ai-concerns

[3] https://darktrace.com/news/darktrace-email-defends-organizations-against-evolving-cyber-threat-landscape

[4] https://vulcan.io/blog/ai-hallucinations-package-risk?nab=1&utm_referrer=https%3A%2F%2Fwww.google.com%2F

[5] https://twitter.com/OpenAI/status/1707077710047216095

[6] https://www.reuters.com/technology/openai-says-chatgpt-can-now-browse-internet-2023-09-27/

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
Charlotte Thompson
Cyber Analyst
Written by
Tiana Kelly
Deputy Team Lead, London & Cyber Analyst

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September 15, 2025

SEO Poisoning and Fake PuTTY sites: Darktrace’s Investigation into the Oyster backdoor

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What is SEO poisoning?

Search Engine Optimization (SEO) is the legitimate marketing technique of improving the visibility of websites in organic search engine results. Businesses, publishers, and organizations use SEO to ensure their content is easily discoverable by users. Techniques may include optimizing keywords, creating backlinks, or even ensuring mobile compatibility.

SEO poisoning occurs when attackers use these same techniques for malicious purposes. Instead of improving the visibility of legitimate content, threat actors use SEO to push harmful or deceptive websites to the top of search results. This method exploits the common assumption that top-ranking results are trustworthy, leading users to click on URLs without carefully inspecting them.

As part of SEO poisoning, the attacker will first register a typo-squatted domain, slightly misspelled or otherwise deceptive versions of real software sites, such as putty[.]run or puttyy[.]org. These sites are optimized for SEO and often even backed by malicious Google ads, increasing the visibility when users search for download links. To achieve that, threat actors may embed pages with strategically chosen, high-value keywords or replicate content from reputable sources to elevate the domain’s perceived authority in search engine algorithms [4]. In more advanced operations, these tactics are reinforced with paid promotion, such as Google ads, enabling malicious domains to appear above organic search results as sponsored links. This placement not only accelerates visibility but also impacts an unwarranted sense of legitimacy to unsuspected users.

Once a user lands on one of these fake pages, they are presented with what looks like a legitimate software download option. Upon clicking the download indicator, the user will be redirected to another separate domain that actually hosts the payload. This hosting domain is usually unrelated to the nominally referenced software. These third-party sites can involve recently registered domains but may also include legitimate websites that have been recently compromised. By hosting malware on a variety of infrastructure, attackers can prolong the availability of distribution methods for these malicious files before they are taken down.

What is the Oyster backdoor?

Oyster, also known as Broomstick or CleanUpLoader, is a C++ based backdoor malware first identified in July 2023. It enables remote access to infected systems, offering features such as command-line interaction and file transfers.

Oyster has been widely adopted by various threat actors, often as an entry point for ransomware attacks. Notable examples include Vanilla Tempest and Rhysida ransomware groups, both of which have been observed leveraging the Oyster backdoor to enhance their attack capabilities. Vanilla Tempest is known for using Oyster’s stealth persistence to maintain long-term access within targeted networks, often aligning their operations with ransomware deployment [5]. Rhysida has taken this further by deploying Oyster as an initial access tool in ransomware campaigns, using it to conduct reconnaissance and move laterally before executing encryption activities [6].

Once installed, the backdoor gathers basic system information before communicating with a command-and-control (C2) server. The malware largely relies on a ‘cmd.exe’ instance to execute commands and launch other files [1].

In previous SEO poisoning cases, the file downloaded from the fake pages is not just PuTTY, but a trojanized version that includes the stealthy Oyster backdoor. PuTTY is a free and open-source terminal emulator for Windows that allows users to connect to remote servers and devices using protocols like SSH and Telnet. In the recent campaign, once a user visits the fake software download site, ranked highly through SEO poisoning, the malicious payload is downloaded through direct user interaction and subsequently installed on the local device, initiating the compromise. The malware then performs two actions simultaneously: it installs a fully functional version of PuTTY to avoid user suspicion, while silently deploying the Oyster backdoor. Given PuTTY’s nature, it is prominently used by IT administrators with highly privileged account as opposed to standard users in a business, possibly narrowing the scope of the targets.

Oyster’s persistence mechanism involves creating a Windows Scheduled Task that runs every few minutes. Notably, the infection uses Dynamic Link Library (DLL) side loading, where a malicious DLL, often named ‘twain_96.dll’, is executed via the legitimate Windows utility ‘rundll32.exe’, which is commonly used to run DLLs [2]. This technique is frequently used by malicious actors to blend their activity with normal system operations.

Darktrace’s Coverage of the Oyster Backdoor

In June 2025, security analysts at Darktrace identified a campaign leveraging search engine manipulation to deliver malware masquerading as the popular SSH client, PuTTY. Darktrace / NETWORK’s anomaly-based detection identified signs of malicious activity, and when properly configured, its Autonomous Response capability swiftly shut down the threar before it could escalate into a more disruptive attack. Subsequent analysis by Darktrace’s Threat Research team revealed that the payload was a variant of the Oyster backdoor.

The first indicators of an emerging Oyster SEO campaign typically appeared when user devices navigated to a typosquatted domain, such as putty[.]run or putty app[.]naymin[.]com, via a TLS/SSL connection.

Figure 1: Darktrace’s detection of a device connecting to the typosquatted domain putty[.]run.

The device would then initiate a connection to a secondary domain that hosts the malicious installer, likely triggered by user interaction with redirect elements on the landing page. This secondary site may not have any immediate connection to PuTTY itself but is instead a hijacked blog, a file-sharing service, or a legitimate-looking content delivery subdomain.

Figure 2: Darktrace’s detection of the device making subsequent connections to the payload domain.

Following installation, multiple affected devices were observed attempting outbound connectivity to rare external IP addresses, specifically requesting the ‘/secure’ endpoint as noted within the declared URIs. After the initial callback, the malware continued communicating with additional infrastructure, maintaining its foothold and likely waiting for tasking instructions. Communication patterns included:

·       Endpoints with URIs /api/kcehc and /api/jgfnsfnuefcnegfnehjbfncejfh

·       Endpoints with URI /reg and user agent “WordPressAgent”, “FingerPrint” or “FingerPrintpersistent”

This tactic has been consistently linked to the Oyster backdoor, which has shown similar URI patterns across multiple campaigns [3].

Darktrace analysts also noted the sophisticated use of spoofed user agent strings across multiple investigated customer networks. These headers, which are typically used to identify the application making an HTTP request, are carefully crafted to appear benign or mimic legitimate software. One common example seen in the campaign is the user agent string “WordPressAgent”. While this string references a legitimate web application or plugin, it does not appear to correspond to any known WordPress services or APIs. Its inclusion is most likely designed to mimic background web traffic commonly associated with WordPress-based content management systems.

Figure 3: Cyber AI Analyst investigation linking the HTTP C2 activity.

Case-Specific Observations

While the previous section focused on tactics and techniques common across observed Oyster infections, a closer examination reveals notable variations and unique elements in specific cases. These distinct features offer valuable insights into the diverse operational approaches employed by threat actors. These distinct features, from unusual user agent strings to atypical network behavior, offer valuable insights into the diverse operational approaches employed by the threat actors. Crucially, the divergence in post-exploitation activity reflects a broader trend in the use of widely available malware families like Oyster as flexible entry points, rather than fixed tools with a single purpose. This modular use of the backdoor reflects the growing Malware-as-a-Service (MaaS) ecosystem, where a single initial infection can be repurposed depending on the operator’s goals.

From Infection to Data Egress

In one observed incident, Darktrace observed an infected device downloading a ZIP file named ‘host[.]zip’ via curl from the URI path /333/host[.]zip, following the standard payload delivery chain. This file likely contained additional tools or payloads intended to expand the attacker’s capabilities within the compromised environment. Shortly afterwards, the device exhibited indicators of probable data exfiltration, with outbound HTTP POST requests featuring the URI pattern: /upload?dir=NAME_FOLDER/KEY_KEY_KEY/redacted/c/users/public.

This format suggests the malware was actively engaged in local host data staging and attempting to transmit files from the target machine. The affected device, identified as a laptop, aligns with the expected target profile in SEO poisoning scenarios, where unsuspecting end users download and execute trojanized software.

Irregular RDP Activity and Scanning Behavior

Several instances within the campaign revealed anomalous or unexpected Remote Desktop Protocol (RDP) sessions occurring shortly after DNS requests to fake PuTTY domains. Unusual RDP connections frequently followed communication with Oyster backdoor C2 servers. Additionally, Darktrace detected patterns of RDP scanning, suggesting the attackers were actively probing for accessible systems within the network. This behavior indicates a move beyond initial compromise toward lateral movement and privilege escalation, common objectives once persistence is established.

The presence of unauthorized and administrative RDP sessions following Oyster infections aligns with the malware’s historical role as a gateway for broader impact. In previous campaigns, Oyster has often been leveraged to enable credential theft, lateral movement, and ultimately ransomware deployment. The observed RDP activity in this case suggests a similar progression, where the backdoor is not the final objective but rather a means to expand access and establish control over the target environment.

Cryptic User Agent Strings?

In multiple investigated cases, the user agent string identified in these connections featured formatting that appeared nonsensical or cryptic. One such string containing seemingly random Chinese-language characters translated into an unusual phrase: “Weihe river is where the water and river flow.” Legitimate software would not typically use such wording, suggesting that the string was intended as a symbolic marker rather than a technical necessity. Whether meant as a calling card or deliberately crafted to frame attribution, its presence highlights how subtle linguistic cues can complicate analysis.

Figure 4: Darktrace’s detection of malicious connections using a user agent with randomized Chinese-language formatting.

Strategic Implications

What makes this campaign particularly noteworthy is not simply the use of Oyster, but its delivery mechanism. SEO poisoning has traditionally been associated with cybercriminal operations focused on opportunistic gains, such as credential theft and fraud. Its strength lies in casting a wide net, luring unsuspecting users searching for popular software and tricking them into downloading malicious binaries. Unlike other campaigns, SEO poisoning is inherently indiscriminate, given that the attacker cannot control exactly who lands on their poisoned search results. However, in this case, the use of PuTTY as the luring mechanism possibly indicates a narrowed scope - targeting IT administrators and accounts with high privileges due to the nature of PuTTY’s functionalities.

This raises important implications when considered alongside Oyster. As a backdoor often linked to ransomware operations and persistent access frameworks, Oyster is far more valuable as an entry point into corporate or government networks than small-scale cybercrime. The presence of this malware in an SEO-driven delivery chain suggests a potential convergence between traditional cybercriminal delivery tactics and objectives often associated with more sophisticated attackers. If actors with state-sponsored or strategic objectives are indeed experimenting with SEO poisoning, it could signal a broadening of their targeting approaches. This trend aligns with the growing prominence of MaaS and the role of initial access brokers in today’s cybercrime ecosystem.

Whether the operators seek financial extortion through ransomware or longer-term espionage campaigns, the use of such techniques blurs the traditional distinctions. What looks like a mass-market infection vector might, in practice, be seeding footholds for high-value strategic intrusions.

Credit to Christina Kreza (Cyber Analyst) and Adam Potter (Senior Cyber Analyst)

Appendices

MITRE ATT&CK Mapping

·       T1071.001 – Command and Control – Web Protocols

·       T1008 – Command and Control – Fallback Channels

·       T0885 – Command and Control – Commonly Used Port

·       T1571 – Command and Control – Non-Standard Port

·       T1176 – Persistence – Browser Extensions

·       T1189 – Initial Access – Drive-by Compromise

·       T1566.002 – Initial Access – Spearphishing Link

·       T1574.001 – Persistence – DLL

Indicators of Compromise (IoCs)

·       85.239.52[.]99 – IP address

·       194.213.18[.]89/reg – IP address / URI

·       185.28.119[.]113/secure – IP address / URI

·       185.196.8[.]217 – IP address

·       185.208.158[.]119 – IP address

·       putty[.]run – Endpoint

·       putty-app[.]naymin[.]com – Endpoint

·       /api/jgfnsfnuefcnegfnehjbfncejfh

·       /api/kcehc

Darktrace Model Detections

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / Posting HTTP to IP Without Hostname

·       Compromise / HTTP Beaconing to Rare Destination

·       Compromise / Large Number of Suspicious Failed Connections

·       Compromise / Beaconing Activity to External Rare

·       Compromise / Quick and Regular Windows HTTP Beaconing

·       Device / Large Number of Model Alerts

·       Device / Initial Attack Chain Activity

·       Device / Suspicious Domain

·       Device / New User Agent

·       Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

·       Antigena / Network / External Threat / Antigena Suspicious Activity Block

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

References

[1] https://malpedia.caad.fkie.fraunhofer.de/details/win.broomstick

[2] https://arcticwolf.com/resources/blog/malvertising-campaign-delivers-oyster-broomstick-backdoor-via-seo-poisoning-trojanized-tools/

[3] https://hunt.io/blog/oysters-trail-resurgence-infrastructure-ransomware-cybercrime

[4] https://www.crowdstrike.com/en-us/cybersecurity-101/social-engineering/seo-poisoning/

[5] https://blackpointcyber.com/blog/vanilla-tempest-oyster-backdoor-netsupport-unknown-infostealers-soc-incidents-blackpoint-apg/

[6] https://areteir.com/article/rhysida-using-oyster-backdoor-in-attacks/

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

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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Christina Kreza
Cyber Analyst

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September 9, 2025

The benefits of bringing together network and email security

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In many organizations, network and email security operate in isolation. Each solution is tasked with defending its respective environment, even though both are facing the same advanced, multi-domain threats.  

This siloed approach overlooks a critical reality: email remains the most common vector for initiating cyber-attacks, while the network is the primary stage on which those attacks progress. Without direct integration between these two domains, organizations risk leaving blind spots that adversaries can exploit.  

A modern security strategy needs to unify email and network defenses, not just in name, but in how they share intelligence, conduct investigations, and coordinate response actions. Let’s take a look at how this joined-up approach delivers measurable technical, operational, and commercial benefits.

Technical advantages

Pre-alert intelligence: Gathering data before the threat strikes

Most security tools start working when something goes wrong – an unusual login, a flagged attachment, a confirmed compromise. But by then, attackers may already be a step ahead.

By unifying network and email security under a single AI platform (like the Darktrace Active AI Security Platform), you can analyze patterns across both environments in real time, even when there are no alerts. This ongoing monitoring builds a behavioral understanding of every user, device, and domain in your ecosystem.

That means when an email arrives from a suspicious domain, the system already knows whether that domain has appeared on your network before – and whether its behavior has been unusual. Likewise, when new network activity involves a domain first spotted in an email, it’s instantly placed in the right context.

This intelligence isn’t built on signatures or after-the-fact compromise indicators – it’s built on live behavioral baselines, giving your defenses the ability to flag threats before damage is done.

Alert-related intelligence: Connecting the dots in real time

Once an alert does fire, speed and context matter. The Darktrace Cyber AI Analyst can automatically investigate across both environments, piecing together network and email evidence into a single, cohesive incident.

Instead of leaving analysts to sift through fragmented logs, the AI links events like a phishing email to suspicious lateral movement on the recipient’s device, keeping the full attack chain intact. Investigations that might take hours – or even days – can be completed in minutes, with far fewer false positives to wade through.

This is more than a time-saver. It ensures defenders maintain visibility after the first sign of compromise, following the attacker as they pivot into network infrastructure, cloud services, or other targets. That cross-environment continuity is impossible to achieve with disconnected point solutions or siloed workflows.

Operational advantages

Streamlining SecOps across teams

In many organizations, email security is managed by IT, while network defense belongs to the SOC. The result? Critical information is scattered between tools and teams, creating blind spots just when you need clarity.

When email and network data flow into a single platform, everyone is working from the same source of truth. SOC analysts gain immediate visibility into email threats without opening another console or sending a request to another department. The IT team benefits from the SOC’s deeper investigative context.

The outcome is more than convenience: it’s faster, more informed decision-making across the board.

Reducing time-to-meaning and enabling faster response

A unified platform removes the need to manually correlate alerts between tools, reducing time-to-meaning for every incident. Built-in AI correlation instantly ties together related events, guiding analysts toward coordinated responses with higher confidence.

Instead of relying on manual SIEM rules or pre-built SOAR playbooks, the platform connects the dots in real time, and can even trigger autonomous response actions across both environments simultaneously. This ensures attacks are stopped before they can escalate, regardless of where they begin.

Commercial advantages

While purchasing “best-of-breed" for all your different tools might sound appealing, it often leads to a patchwork of solutions with overlapping costs and gaps in coverage. However good a “best-in-breed" email security solution might be in the email realm, it won't be truly effective without visibility across domains and an AI analyst piecing intelligence together. That’s why we think “best-in-suite" is the only “best-in-breed" approach that works – choosing a high-quality platform ensures that every new capability strengthens the whole system.  

On top of that, security budgets are under constant pressure. Managing separate vendors for email and network defense means juggling multiple contracts, negotiating different SLAs, and stitching together different support models.

With a single provider for both, procurement and vendor management become far simpler. You deal with one account team, one support channel, and one unified strategy for both environments. If you choose to layer on managed services, you get consistent expertise across your whole security footprint.

Even more importantly, an integrated AI platform sets the stage for growth. Once email and network are under the same roof, adding coverage for other attack surfaces – like cloud or identity – is straightforward. You’re building on the same architecture, not bolting on new point solutions that create more complexity.

Check out the white paper, The Modern Security Stack: Why Your NDR and Email Security Solutions Need to Work Together, to explore these benefits in more depth, with real-world examples and practical steps for unifying your defenses.

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response
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