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April 10, 2023

Detecting Malicious Email Activity & AI Impersonating

Discover how two different phishing attempts from some known and unknown senders used a payroll diversion and credential sealing box link to harm users.
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
Isabelle Cheong
Cyber Security Analyst
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10
Apr 2023

Social engineering has become widespread in the cyber threat landscape in recent years, and the near-universal use of social media today has allowed attackers to research and target victims more effectively. Social engineering involves manipulating users to carry out actions such as revealing sensitive information like login credentials or credit card details. It can also lead to user account compromises, causing huge disruption to an organization’s digital estate. 

As people use social media platforms not only for personal reasons, but also for business purposes, attackers gain information they can exploit in social engineering attacks. For example, a threat actor may attempt to impersonate a known individual or legitimate service to take advantage of a user’s established trust. This is a highly successful method of social engineering because mimicking known contacts makes it difficult for traditional security tools that rely on deny-lists to detect the attack.

In October 2022, Darktrace identified and responded to two separate malicious email campaigns in which threat actors attempted to impersonate known contacts in an effort to compromise customer devices. As it learns the normal behavior of every user in the email system, Darktrace was able to instantly detect these threats and mitigate them autonomously, preventing significant disruption to the customer networks.

Payroll Diversion Fraud Attempt Impersonating a Former Employee 

While a customer in the Canadian energy sector was trialing Darktrace in October 2022, Darktrace/Email™ identified a suspicious email seemingly sent from an employee within the organization. The email was sent to the Senior Director of Human Resources (HR) with a subject line of “Change in payroll Direct Deposit.” The email requested a change in bank account information for an employee. However, Darktrace recognized that the sender was using a free mail address that contained random letters, indicating it may have been algorithmically generated. Since this incident occurred during a trial, Darktrace/Email was not configured to take action. Otherwise, it would have prevented the email from landing in the inbox. In this case though, the email went through, bypassing all other security tools in place.

Although the email was from an unknown sender, the HR director believed the email could have been legitimate as the employee who appeared to be the sender had left the organization seven days prior and no longer had access to their corporate email account. However, after reviewing it in the Darktrace/Email dashboard, the customer grew suspicious and contacted the former employee directly to verify if the request was legitimate. The former employee validated the suspicions by confirming they had sent no such email.

Further investigation by the customer revealed that the former employee had been vocal about their departure on various social media platforms. This gave threat actors valuable information to believably impersonate the former employee and defraud the organization. 

Such attempts to target organizations’ HR departments and divert payroll are common tactics for cyber-criminals and are often identified by Darktrace/Email across the customer base. Darktrace/Email is able to instantly identify the indicators associated with these spoofing attempts and immediately bring them to the attention of the customer’s security team. 

Using Legitimate File Sharing Service to Share a Phishing Link 

On October 7, 2022, a customer in the Singaporean construction sector was targeted by a phishing campaign attempting to impersonate a law firm known to the organization. Almost 200 employees received an email with the subject line “Accepted: Valuation Agreement.” 

Figure 1: Sample of an UI view of the message held showing anomaly indicators, history, association, and validation.

Four days earlier, Darktrace observed communication between another email address associated with the law firm and an employee of the customer. Darktrace/Email noted that it was the first time this correspondent had sent emails to the customer. 

Figure 2: Metrics showing how well the sender’s domain is known within the digital environment.

The emails contained a highly unusual link to a file sharing service, (hxxps://ssvilvensstokes[.]app[.]box[.]com/notes), hidden behind the text “PREVIEW OR PRINT COPY OF DOCUMENT HERE.” Darktrace analysts investigated this event further and found that around 30 similar URLs had been identified as suspicious using OSINT security tools in October 2022, suggesting the customer was not the only target of this phishing campaign.

Figure 3: Preview of the phishing email’s body.
Figure 4: Darktrace’s evaluation of the link contained in the phishing email.

Additional OSINT work revealed that the link directed to a website which appeared to host a PDF file named “Valuation Agreement.” The recipient would then be prompted to follow another link (hulking-citrine-krypton[.]glitch[.]me), again hidden behind the text “OPEN OR ACCESS DOCUMENT HERE” to view the file. Subsequently, the user would be prompted to enter their Microsoft 365 credentials. 

Figure 5: The page displayed when the phishing link was clicked, viewed in a sandbox environment.
Figure 6: Example of a page shown when recipient clicks the second link, accessing “hulking-citrine-krypton[.]glitch[.]me”. 

This page contained the text “This document has been scanned for viruses by Norton Antivirus Security.” This is another example of threat actors’ employing social engineering techniques by impersonating well-known brands, such as established security vendors, to gain the trust of users and increase their likelihood of success.

It is highly probable that a real employee of the law firm had their account hijacked and that a malicious actor was exploiting it to send out these phishing emails en masse as part of a supply chain attack. In such cases, malicious actors rely on their targets’ trust of known contacts to not question departures from their normal conversations. 

Darktrace was able to instantly detect multiple anomalies in these emails, despite the fact that they were seemingly sent by known correspondents. The activity detected automatically triggered model breaches associated with unexpected and visually prominent links. As a result, Darktrace/Email responded by locking the link, stopping users from being able to click it.

Darktrace subsequently identified additional emails from this sender attempting to target other recipients within the company, triggering the model breaches associated with a surge in email sending indicative of a phishing campaign. In response, Darktrace/Email autonomously acted and filed these emails as junk. As more emails were detected across the customer’s environment, the anomaly score of the sender increased and Darktrace ultimately held back over 160 malicious emails, safeguarding recipients from potential account compromise.           

The following Darktrace/Email models were breached throughout the course of this phishing campaign:

  • Unusual/Sender Surge 
  • Unusual/Undisclosed Recipients 
  • Antigena Anomaly 
  • Association/Unlikely Recipient Association 
  • Link/Low Link Association 
  • Link/Visually Prominent Link 
  • Link/Visually Prominent Link Unexpected For Sender 
  • Unusual/New Sender Wide Distribution
  • Unusual/Undisclosed Recipients + New Address Known Domain

Conclusion

Social engineering plays a role in many of the major threats challenging current email cyber security, as attackers can use it to manipulate users into transferring money, revealing credentials, clicking malicious links, and more. 

The above threat stories happened before language generating AI became mainstream with the release of ChatGPT in December 2022. Now, it is even easier for malicious actors to generate sophisticated social engineering emails. By using social media posts as input, social engineering emails written by generative AI can be highly targeted and produced at scale. They often avoid the flags users are trained to look for, like poor grammar and spelling mistakes, and can hide payloads or forgo them entirely.

To mitigate the risk of possible social engineering attempts, it is recommended that organizations implement social media policies that advise employees to be cautious of what they post online and enact procedures to verify if fund transfer requests are legitimate.

Yet these policies are not enough on their own. Darktrace/Email can identify suspicious email traits, whether an email is sent from a known correspondent or an unknown sender. With Self-Learning AI, it knows an organization’s users better than any impersonator could. In this way, Darktrace/Email detects anomalies within emails and neutralizes malicious components at machine-speed, stopping attacks at their earliest stages, before employees fall victim. 

Appendices

List of Indicators of Compromise (IoCs)

Domain:

hxxps://ssvilvensstokes[.]app[.]box[.]com/notes/*?s=* - 1st external link (seen in email)

hxxps://hulking-citrine-krypton[.]glitch[.]me/flk.html - 2nd external link, masked behind “OPEN OR ACCESS DOCUMENT HERE”

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Isabelle Cheong
Cyber Security Analyst

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May 14, 2026

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

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Darktrace have identified activity consistent with Chinese-nexus operations, a Twill Typhoon-linked campaign targeting customer environments, primarily within the Asia-Pacific & Japan (APJ) region

Beginning in late September 2025, multiple affected hosts were observed making requests to domains impersonating content delivery networks (CDNs), including infrastructure masquerading as Yahoo- and Apple-affiliated services. Across these cases, Darktrace identified a consistent behavioral execution pattern: the retrieval of legitimate binaries alongside malicious Dynamic Link Libraries (DLLs), enabling sideloading and execution of a modular .NET-based Remote Access Trojan (RAT) framework.

The activity aligns with patterns described in Darktrace’s previous Chinese-nexus operations report, Crimson Echo. In this case, observed modular intrusion chains built on legitimate software, and staged payload delivery. Threat actors retrieve legitimate binaries alongside configuration files and malicious DLLs to enable sideloading of a .NET-based RAT.

Observed Campaign

Across cases, the same ordered sequence appears: retrieval of a legitimate executable, (2) retrieval of a matching .config file, (3) retrieval of the malicious

DLL, (4) repeated DLL downloads over time, and (5) command-and-control (C2) communication. The .config file retrieves a malicious binary, while the legitimate binary provides a legitimate process to run it in.

Darktrace assesses with moderate confidence that this activity aligns with publicly reported Twill Typhoon tradecraft. The observed use of FDMTP, DLL sideloading, and overlapping infrastructure is consistent with previously observed operations, though not unique to a single actor. While initial access was not directly observed, previous Twill Typhoon campaigns have typically involved spear-phishing.

What Darktrace Observed

Since late September 2025, Darktrace has observed multiple customer environments making HTTP GET requests to infrastructure presenting as “CDN” endpoints for well-known platforms (including Yahoo and Apple lookalikes). Across cases, the affected hosts retrieved legitimate executables, then matching .config files (same base filename), then DLLs intended for sideloading. The sequencing of a legitimate binary + configuration + DLL  has been previously observed in campaigns linked to China-nexus threat actors.

In several cases, affected hosts also issued outbound requests to a /GetCluster endpoint, including the protocol=Dotnet-Tcpdmtp parameter. This activity was repeatedly followed by retrieval of DLL content that was subsequently used for search-order hijacking within legitimate processes.

In the September–October 2025 cases, Darktrace alerting commonly surfaced early-stage registration and C2 setup behaviors, followed by retrieval of a DLL (e.g., Client.dll) from the same external host, sometimes repeatedly over multiple days, consistent with establishing and maintaining the execution chain.

In April 2026, a finance-sector endpoint initiated a series of GET requests to yahoo-cdn[.]it[.]com, first fetching legitimate binaries (including vshost.exe and dfsvc.exe), then repeatedly retrieving associated configuration and DLL components (including dfsvc.exe.config and dnscfg.dll) over an 11-day window. The use of both Visual Studio hosting and OneClick (dfsvc.exe) paths are used to ensure the malware can run in the targeted environment.

Technical Analysis

Initial staging and execution

While the initial access method is unknown, Darktrace security researchers identified multiple archives containing the malware.

A representative example includes a ZIP archive (“test.zip”) containing:

  • A legitimate executable: biz_render.exe (Sogou Pinyin IME)
  • A malicious DLL: browser_host.dll

Contained within the zip archive named “test.zip” is the legitimate binary “biz_render.exe”, a popular Chinese Input Method Editor (IME) Sogou Pinyin.

Alongside the legitimate binary is a malicious DLL named “browser_host.dll”. As the legitimate binary loads a legitimate DLL named “browser_host.dll” via LoadLibraryExW, the malicious DLL has been named the same to sideload the malicious DLL into biz_render.exe. By supplying a malicious DLL with an identical name, the actor hijacks execution flow, enabling the payload to execute within a trusted process.

Figure 1: Biz_render.exe loading browser_host.dll.

The legitimate binary invokes the function GetBrowserManagerInstance from the sideloaded “browser_host.dll”, which then performs XOR-based decryption of embedded strings (key 0x90) to resolve and dynamically load mscoree.dll.

The DLL uses the Windows Common Language Runtime (CLR) to execute managed .NET code inside the process rather than relying solely on native binaries. During execution, the loader loads a payload directly into memory as .NET assemblies, enabling an in-memory execution.

C2 Registration

A GET request is made to:

GET /GetCluster?protocol=DotNet-TcpDmtp&tag={0}&uid={1}

with the custom header:

Verify_Token: Dmtp

This returns Base64-encoded and gzip-compressed IP addresses used for subsequent communication.

Figure 2: Decoded IPs.

Staged payload retrieval

Subsequent activity includes retrieval of multiple components from yahoo-cdn.it[.]com. The following GET requests are made:

/dfsvc.exe

/dnscfg.dll

/dfsvc.exe.config

/vhost.exe

/Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll

/config.etl

ClickOnce and AppDomain hijacking

Dfsvc.exe is the legitimate Windows ClickOnce Engine, part of the .NET framework used for updating ClickOnce Applications. Accompanying dfsvc.exe is a legitimate dfsvc.exe.config file that is used to store configuration data for the application. However, in this instance the malware has replaced the legitimate dfsvc.exe.config with the one retrieved from the server in: C:\Windows\Microsoft.NET\Framework64\v4.0.30319.

Additionally, vhost.exe the legitimate Visual Studio hosting process is retrieved from the server, along with “Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll” and “config.etl”. The DLL is used to decrypt the AES encrypted payload in config.etl and load it. The encrypted payload is dnscfg.dll, which can be loaded into vshost instead of dfsvc, and may be used if the environment does not support .NET.

Figure 3: ClickOnce configuration.

The malicious configuration disables logging, forces the application to load dnscfg.dll from the remote server, and uses a custom AppDomainManager to ensure the DLL is executed during initialization of dfsvc.exe. To ensure persistence, a scheduled task is added for %APPDATA%\Local\Microsoft\WindowsApps\dfsvc.exe.

Core payload

The DLL dnscfg.dll is a .NET binary named Client.TcpDmtp.dll. The payload is a heavily obfuscated backdoor that generates its logic at runtime and communicates with the command and control (C2) over custom TCP, DMTP (Duplex Message Transport Protocol) and appears to be an updated version of FDMTP to version 3.2.5.1

Figure 4: InitializeNewDomain.

The payload:

  • Uses cluster-based resolution (GetHostFromCluster)
  • Implements token validation
  • Enters a persistent execution loop (LoopMessage)
  • Supports structured remote tasking over DMTP

Once connected, the malware enters a persistent loop (LoopMessage), enabling it to receive commands from the remote server.

Figure 5: DMTP Connect function.

Rather than referencing values directly, they are retrieved through containers that are resolved at runtime. String values are stored in an encrypted byte array (_0) and decrypted by a custom XOR-based string decryption routine (dcsoft). The lower 16 bits of the provided key are XORed with 0xA61D (42525) to derive the initial XOR key, while subsequent bits define the string length and offset into the encrypted byte array. Each character is reconstructed from two encrypted bytes and XORed with the incrementing key value, producing the plaintext string used by the payload.

Figure 6: Decrypted strings.

Embedded in the resources section are multiple compressed binaries, the majority of which are library files. The only exceptions are client.core.dll and client.dmtpframe.dll.

Figure 7: Resources.

Modular framework and plugins

The payload embeds multiple compressed libraries, notably:

  • client.core.dll
  • client.dmtpframe.dll

Client.core.dll is a core library used for system profiling, C2 communication and plugin execution. The implant has the functionality to retrieve information including antivirus products, domain name, HWID, CLR version, administrator status, hardware details, network details, operating system, and user.

Figure 8: Client.Core.Info functions.

Additionally, the component is responsible for loading plugins, with support for both binary and JSON-based plugin execution. This allows plugins to receive commands and parameters in different formats depending on the task being performed.

The framework handles details such as plugin hashes, method names, task identifiers, caller tracking, and argument processing, allowing plugins to be executed consistently within the environment. In addition to execution management, the library also provides plugins with access to common runtime functionality such as logging, communication, and process handling.

Figure 9: Client.core functions.

client.dmtpframe.dll handles:

  • DMTP communication
  • Heartbeats and reconnection
  • Plugin persistence via registry:

HKCU\Software\Microsoft\IME\{id}

Client.dmtpframe.dll is built on the TouchSocket DMTP networking library and continues to manage the remote plugins. The DLL implements remote communication features including heartbeat maintenance, reconnection handling, RPC-style messaging, SSL support, and token-based verification. The DLL also has the ability to add plugins to the registry under HKCU/Software/Microsoft/IME/{id} for persistence.

Plugins observed

While the full set of plugins remains unknown, researchers were able to identify four plugins, including:

  • Persist.WpTask.dll - used to create, remove and trigger scheduled Windows tasks remotely.
  • Persist.registry.dll - used to manage registry persistence with the ability to create, and delete registry values, along with hidden persistence keys.
  • Persist.extra.dll - used to load and persist the main framework.
  • Assist.dll - used to remotely retrieve files or commands, as well as manipulate system processes.
Figure 10: Plugins stored in IME registry.
Figure 11: Obfuscated script in plugin resources.

Persist.extra.dll is a module that is used to load a script “setup.log” to load and persist the main framework. Stored within the resources section of the binary is an obfuscated script that creates a .NET COM object that is added to the registry key HKCU\Software\Classes\TypeLib\ {9E175B61-F52A-11D8-B9A5-505054503030} \1.0\1\Win64 for persistence. After deobfuscating this script, another DLL is revealed named “WindowsBase.dll”.

Figure 12: Registry entry for script.

The binary checks in with icloud-cdn[.]net every five minutes, retrieves a version string, downloads an encrypted payload named checksum.bin, saves it locally as C:\ProgramData\USOShared\Logs\checksum.etl, decrypts it with AES using the hardcoded key POt_L[Bsh0=+@0a., and loads the decrypted assembly directly from memory via Assembly.Load(byte[]). The version.txt file acts as an update marker so it only re-downloads when the remote version changes, while the mutex prevents duplicate instances.

Figure 13: USOShared/Logs.

Checksum.etl is decrypted with AES and loaded into memory, loading another .NET DLL named “Client.dll”. This binary is the same as “dnscfg.dll” mentioned at the start and allows the threat actors to update the main framework based on the version.

Conclusion

Across cases, Darktrace consistently observed the following sequence:

  • Retrieval of legitimate executables
  • Retrieval of DLLs for sideloading
  • C2 registration via /GetCluster

This approach is consistent with broader China-nexus tradecraft. As outlined in Darktrace’s Crimson Echo report, the stable feature of this activity is behavioral. Infrastructure rotates and payloads can change, but the execution model persists. For defenders, the implication is straightforward: detection anchored to individual indicators will degrade quickly. Detection anchored to a behavioral sequence offer a far more durable approach.

Credit to Tara Gould (Malware Research Lead), Adam Potter (Senior Cyber Analyst), Emma Foulger (Global Threat Research Operations Lead), Nathaniel Jones (VP, Security & AI Strategy)

Edited by Ryan Traill (Content Manager)


Appendices

A detailed list of detection models and triggered indicators is provided alongside IoCs.

Indicators of Compromise (IoCs)

Test.zip - fc3959ebd35286a82c662dc81ca658cb

Dnscfg.dll - b2c8f1402d336963478f4c5bc36c961a

Client.TcpDmtp.dll - c52b4a16d93a44376f0407f1c06e0b

Browser_host.dll - c17f39d25def01d5c87615388925f45a

Client.DmtpFrame.dll - 482cc72e01dfa54f30efe4fefde5422d

Persist.Extra - 162F69FE29EB7DE12B684E979A446131

Persist.Registry - 067FBAD4D6905D6E13FDC19964C1EA52

Assist - 2CD781AB63A00CE5302ED844CFBECC27

Persist.WpTask - DF3437C88866C060B00468055E6FA146

Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll - c650a624455c5222906b60aac7e57d48

www.icloud-cdn[.]net

www.yahoo-cdn.it[.]com

154.223.58[.]142[AP8] [EF9]

MITRE ATT&CK Techniques

T1106 – Native API

T1053.005 - Scheduled Task

T1546.16 - Component Object Model Hijacking

T1547.001 - Registry Run Keys

T1511.001 - Dynamic Link Library Injection

T1622 – Debugger Evasion

T1140 – Deobfuscate/Decode Files or Information

T1574.001 - Hijack Execution Flow: DLL

T1620 – Reflective Code Loading

T1082 – System Information Discovery

T1007 – System Service Discovery

T1030 – System Owner/User Discovery

T1071.001 - Web Protocols

T1027.007 - Dynamic API Resolution

T1095 – Non-Application Layer Protocol

Darktrace Model Alerts

·      Compromise / Beaconing Activity To External Rare

·      Compromise / HTTP Beaconing to Rare Destination

·      Anomalous File / Script from Rare External Location

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / Agent Beacon to New Endpoint

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Quick and Regular Windows HTTP Beaconing

·      Compromise / High Volume of Connections with Beacon Score

·      Anomalous File / Anomalous Octet Stream (No User Agent)

·      Compromise / Repeating Connections Over 4 Days

·      Device / Large Number of Model Alerts

·      Anomalous Connection / Multiple Connections to New External TCP Port

·      Compromise / Large Number of Suspicious Failed Connections

·      Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·      Device / Increased External Connectivity

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About the author
Tara Gould
Malware Research Lead

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May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

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Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

---

Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

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About the author
Irving Bruckstein
CEO CyberAIgroup
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