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July 11, 2024

GuLoader: Evolving Tactics in Latest Campaign Targeting European Industry

Cado Security Labs identified a GuLoader campaign targeting European industrial companies via spearphishing emails with compressed batch files. This malware uses obfuscated PowerShell scripts and shellcode with anti-debugging techniques to establish persistence and inject into legitimate processes, to deliver Remote Access Trojans. GuLoader's ongoing evolution highlights the need for robust security.
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
Tara Gould
Malware Research Lead
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11
Jul 2024

Introduction: GuLoader

Researchers from Cado Security Labs (now part of Darktrace) recently discovered a  campaign targeting European industrial and engineering companies. GuLoader is an evasive shellcode downloader used to deliver Remote Access Trojans (RAT) that has been used by threat actors since 2019 and continues to advance. 

Figure 1

Initial access

Cado identified a number of spearphishing emails sent to electronic manufacturing, engineering and industrial companies in European countries including Romania, Poland, Germany and Kazakhstan. The emails typically include order inquiries and contain an archive file attachment (iso, 7z, gzip, rar). The emails are sent from various email addresses including from fake companies and compromised accounts. The emails typically hijack an existing email thread or request information about an order. 

PowerShell  

The first stage of GuLoader is a batch file that is compressed in the archive from the email attachment. As shown in Image 2, the batch file contains an obfuscated PowerShell script, which is done to evade detection.

Batch file
Figure 2: Obfuscated PowerShell

The obfuscated script contains strings that are deobfuscated through a function “Boendes” (in this sample) that contains a for loop that takes every fifth character, with the rest of the characters being junk. After deobfuscating, the functionality of the script is clearer. These values can be retrieved by debugging the script, however deobfuscating with Script 1 in the Scripts section, makes it easier to read for static analysis.

Deobfuscated Powershell
Figure 3 - Deobfuscated PowerShell

This Powershell script contains the function “Aromastofs” that is used to invoke the provided expressions. A secondary file is downloaded from careerfinder[.]ro and saved as “Knighting.Pro” in the user’s AppData/Roaming folder. The content retrieved from “Kighting.Pro” is decoded from Base64, converted to ASCII and selected from position 324537, with the length 29555. This is stored as “$Nongalactic” and contains more Powershell. 

Second Powershell script
Figure 4 - Second PowerShell script
Deobfuscated Secondary Powershell
Figure 5 - Deobfuscated Secondary PowerShell

As seen in Image 5, the secondary PowerShell is obfuscated in the same manner as before with the function “Boendes”. The script begins with checking which PowerShell is being used 32 or 64 bit. If 64 bit is in use, a 32 bit PowerShell process is spawned to execute the script, and to enable 32 bit processes later in the chain. 

The function named “Brevsprkkernes” is a secondary obfuscation function. The function takes the obfuscated hex string, converts to a byte array, applies XOR with a key of 173 and converts to ASCII. This obfuscation is used to evade detection and analysis more difficult. Again, these values can be retrieved with debugging; however for readability, using Script 2 in the Scripts section makes it easier to read. 

Obfuscated Hex Strings
Figure 6: Obfuscated Hex Strings
Deobfuscated PowersShell Strings
Figure 7 - Deobfuscated PowerShell Strings
Deobfuscated Process Injection
Figure 8: Deobfuscated Process Injection

The second PowerShell script contains functionality to allocate memory via VirtualAlloc and to execute shellcode. VirtualAlloc is a native Windows API function that allows programs to allocate, reserve, or commit memory in a specified process. Threat actors commonly use VirtualAlloc to allocate memory for malicious code execution, making it harder for security solutions to detect or prevent code injection. The variable “$Bakteriekulturs” contains the bytes that were stored in “AppData/Roaming/Knighting.Pro” and converted from Base64 in the first part of the PowerShell Script. Marshall::Copy is used to copy the first 657 bytes of that file, which is the first shellcode. Marshall.Copy is a method that enables the transfer of data between unmanaged memory and managed arrays, allowing data exchange between managed and unmanaged code. Marshal.Copy is typically abused to inject or manipulate malicious payloads in memory, bypassing traditional detection by directly accessing and modifying memory regions used by applications. Marshall::Copy is used again to copy bytes 657 to 323880 as a second shellcode. 

First Shellcode
Figure 9: First Shellcode

The first shellcode includes multiple anti-debugging techniques that make static and dynamic analysis difficult. There have been multiple evolutions of GuLoader’s evasive techniques that have been documented [1]. The main functionality of the first shellcode is to load and decrypt the second shellcode. The second shellcode adds the original PowerShell script as a Registry Key “Mannas” in HKCU/Software/Procentagiveless for persistence, with the path to PowerShell 32 bit executable stored as “Frenetic” in HKCU\Environment; however, these values change per sample. 

Registry Key created for PowerShell Script
Figure 10 - Registry Key created for PowerShell Script
PowerShell bit added to Registry
Figure 11 - PowerShell 32 bit added to Registry

The second shellcode is injected into the legitimate “msiexec.exe” process and appears to be reaching out to a domain to retrieve an additional payload, however at the time of analysis this request returns a 404. Based on previous research of GuLoader, the final payload is usually a RAT including Remcos, NetWire, and AgentTesla.[2]

msiexec abused to retrieve additional payload
Figure 12  - msiexec abused to retrieve additional payload

Key Takeaway

Guloader malware continues to adapt its techniques to evade detection to deliver RATs. Threat actors are continually targeting specific industries in certain countries. Its resilience highlights the need for proactive security measures. To counter Guloader and other threats, organizations must stay vigilant and employ a robust security plan.

Scripts

Script 1 to deobfuscate junk characters 

import re 
import argparse 
import os 
 
def deobfuscate_powershell(input_file, output_file): 
  try: 
      with open(input_file, 'r', encoding='utf-8') as f: 
          text = f.read() 
 
      function_name_match = re.search(r"function\s+(\w+)\s*\(", text) 
      if not function_name_match: 
          print("Could not find the obfuscation function name in the file.") 
          return 
      
      function_name = function_name_match.group(1) 
      print(f"Detected obfuscation function name: {function_name}") 
 
      obfuscated_pattern = rf"(?<={function_name} ')(.*?)(?=')" 
      matches = re.findall(obfuscated_pattern, text) 
 
      for match in matches: 
          deobfuscated = match[4::5] 
          full_obfuscated_call = f"{function_name} '{match}'" 
          text = text.replace(full_obfuscated_call, deobfuscated) 
 
      with open(output_file, 'w', encoding='utf-8') as f: 
          f.write(text) 
 
      print(f"Deobfuscation complete. Output saved to {output_file}") 
 
  except Exception as e: 
      print(f"An error occurred!: {e}") 
 
if __name__ == "__main__": 
  parser = argparse.ArgumentParser(description="Deobfuscate an obfuscated PowerShell file.") 
  parser.add_argument("input_file", help="Path to the obfuscated PowerShell file.") 
  parser.add_argument("output_file", nargs='?', help="Path to save the deobfuscated file. Default is 'deobfuscated_powershell.ps1' in the same directory.", default=None) 
 
  args = parser.parse_args() 
 
  if args.output_file is None: 
      output_file = os.path.splitext(args.input_file)[0] + "_deobfuscated.ps1" 
  else: 
      output_file = args.output_file 
 
  deobfuscate_powershell(args.input_file, output_file) 

Script 2 to deobfuscate hex strings obfuscation (note this will need values changed based on sample)

import re 
import argparse 
 
def brevsprkkernes(spackle): 
  if not all(c in'0123456789abcdefABCDEF'for c in spackle): 
      return f"Invalid hex: {spackle}" 
  paronomasian = 2 
  polyurethane = bytearray(len(spackle) // 2) 
 
  for forstyrrets in range(0, len(spackle), paronomasian): 
      try: 
          polyurethane[forstyrrets // 2] = int(spackle[forstyrrets:forstyrrets + 2], 16) 
          polyurethane[forstyrrets // paronomasian] ^= 173 
      except ValueError: 
          return f"Error processing hex: {spackle}" 
 
  return polyurethane.decode('ascii', errors='ignore') 
 
def process_file(input_file, output_file): 
  with open(input_file, 'r') as infile: 
      content = infile.read() 
 
  def replace_function(match): 
      hex_string = match.group(1).strip() 
      result = brevsprkkernes(hex_string) 
      return f"Brevsprkkernes '{result}'" 
 
  updated_content = re.sub(r"Brevsprkkernes\s*['\"]?([0-9A-Fa-f]+)['\"]?", replace_function, content) 
 
  with open(output_file, 'w') as outfile: 
      outfile.write(updated_content) 
 
if __name__ == "__main__": 
  parser = argparse.ArgumentParser(description="Process a PowerShell file and replace hex strings.") 
  parser.add_argument("input_file", help="Path to the input file.") 
  parser.add_argument("output_file", help="Path to save the deobufuscated file.") 
  args = parser.parse_args() 
 
  process_file(args.input_file, args.output_file) 

Indicators of compromise (IoCs)

GuLoader scripts

ZW_PCCE-010023024001.bat  36a9a24404963678edab15248ca95a4065bdc6a84e32fcb7a2387c3198641374  

ORDER_1ST.bat  26500af5772702324f07c58b04ff703958e7e0b57493276ba91c8fa87b7794ff  

IMG465244247443 GULF ORDER Opmagasinering.cmd  40b46bae5cca53c55f7b7f941b0a02aeb5ef5150d9eff7258c48f92de5435216  

EXSP 5634 HISP9005 ST MSDS DOKUME74247linierelet.bat  e0d9ebe414aca4f6d28b0f1631a969f9190b6fb2cf5599b99ccfc6b7916ed8b3  

LTEXSP 5634 HISP9005 ST MSDS DOKUME74247liniereletbrunkagerne.bat 4c697bdcbe64036ba8a79e587462960e856a37e3b8c94f9b3e7875aeb2f91959  

Quotation_final_buy_order_list_2024_po_nos_ART125673211020240000000000024.bat661f5870a5d8675719b95f123fa27c46bfcedd45001ce3479a9252b653940540  

MEC20241022001.bat  33ed102236533c8b01a224bd5ffb220cecc32900285d2984d4e41803f1b2b58d  

nMEC20241022001.iso  9617fa7894af55085e09a06b1b91488af37b8159b22616dfd5c74e6b9a081739  

Gescanneerde lijst met artikelen nr. 654398.bat  f5feabf1c367774dc162c3e29b88bf32e48b997a318e8dd03a081d7bfe6d3eb5  

DHL_Shipping_Invoices_Awb_BL_000000000102220242247820020031808174Global180030010222024.cmd f78319fcb16312d69c6d2e42689254dff3cb875315f7b2111f5c3d2b4947ab50  

Order Confirmation.bat  949cdd89ed5fb2da03c53b0e724a4d97c898c62995e03c48cbd8456502e39e57  

SKM_0001810-01-2024-GL-3762.bat  9493ad437ea4b55629ee0a8d18141977c2632de42349a995730112727549f40e  

21102024_0029_18102024_SKM_0001810-01-2024-GL-3762.iso  535dd8d9554487f66050e2f751c9f9681dadae795120bb33c3db9f71aafb472c  

\Device\CdRom1\MARSS-FILTRY_ZW015010024.BAT  e5ebe4d8925853fc1f233a5a6f7aa29fd8a7fa3a8ad27471c7d525a70f4461b6  

Myologist.cmd  51244e77587847280079e7db8cfdff143a16772fb465285b9098558b266c6b3f  

SKU_0001710-1-2024-SX-3762.bat  643cd5ba1ac50f5aa2a4c852b902152ffc61916dc39bd162f20283a0ecef39fe  

Stamcafeernes.cmd  54b8b9c01ce6f58eb6314c67f3acb32d7c3c96e70c10b9d35effabb7e227952e  

C:\Users\user\AppData\Local\Temp\j4phhdbc.lti\Bank details Form.bat  c1f810194395ff53044e3ef87829f6dff63a283c568be4a83088483b6c043ec8  

SKGCRO COMANDA FAB SRL M60_647746748846748347474.bat  8dd5fd174ee703a43ab5084fdaba84d074152e46b84d588bf63f9d5cd2f673d1  

DHL_Shipping_Invoices_Awb_BL_000000000101620242247820020031808174Global180030010162024.bat bde5f995304e327d522291bf9886c987223a51a299b80ab62229fcc5e9d09f62  

Ciwies.cmd  b1be65efa06eb610ae0426ba7ac7f534dcb3090cd763dc8642ca0ede7a339ce7  

Zamówienie Agotech Begyndelsesord.cmd  18c0a772f0142bc8e5fb0c8931c0ba4c9e680ff97d7ceb8c496f68dea376f9da  

SKM_0001810-01-2024-GL-3762.iso  4a4c0918bdacd60e792a814ddacc5dc7edb83644268611313cb9b453991ac628  

C:\Users\user\AppData\Local\Temp\Stemmeslugerens.bat  8bedbdaa09eefac7845278d83a08b17249913e484575be3a9c61cf6c70837fd2  

Agotech Zamówienie Fjeldkammes325545235562377.bat  ff6c4c8d899df66b551c84124e73c1f3ffa04a4d348940f983cf73b2709895d3  

Agotech Zamówienie Fjeldkammes3255452355623.bat  f3e046a7769b9c977053dd32ebc1b0e1bbfe3c61789d2b8d54e51083c3d0bed5  

SKU_0001710-1-2024-SX-3762.iso  0546b035a94953d33a5c6d04bdc9521b49b2a98a51d38481b1f35667f5449326  

SKU_0001710-1-2024-SX-3762.bat  4f1b5d4bb6d0a7227948fb7ebb7765f3eb4b26288b52356453b74ea530111520  

DOKUMENTEN_TOBIAS.bat  038113f802ef095d8036e86e5c6b2cb8bc1529e18f34828bcf5f99b4cc012d6a  

IMEG238668289485293885823085802835025Urfjeld.bat  6977043d30d8c1c5024669115590b8fd154905e01ab1f2832b2408d1dc811164  

SKM_C250i24100408500.iso  6370cbcb1ac3941321f93dd0939d5daba0658fb8c85c732a6022cc0ec8f0f082  

SKU_0001710-1-2024-SX-3762.iso  7f06382b781a8ba0d3f46614f8463f8857f0ade67e0f77606b8d918909ad37c2  

\Device\CdRom1\ORDINE ELECTRICAS BC CORP PO EDC0969388.BAT  e98fa3828fa02209415640c41194875c1496bc6f0ca15902479b012243d37c47  

Quote Request #2359 Bogota.msg  0f0dfe8c5085924e5ab722fa01ea182569872532a6162547a2e87a1d2780f902  

ORDER.1ST.bat  48dca5f3a12d3952531b05b556c30accafbf9a3c6cda3ec517e4700d5845ab61  

Fortryl105.cmd  f43b78e4dc3cba2ee9c6f0f764f97841c43419059691d670ca930ce84fb7143b  

SMX-0002607-1-2024-UP-3762.iso  a60dbbe88a1c4857f009a3c06a2641332d41dfd89726dd5f2c6e500f7b25b751

Quotation_final_buy_order_list_2024_po_nos_ART1256731610202400000000000.cmd efd80337104f2acde5c8f3820549110ad40f1aa9b494da9a356938103bda82e7

a60dbbe88a1c4857f009a3c06a2641332d41dfd89726dd5f2c6e500f7b25b751.iso 0327db7b754a16a7ae29265e7d8daed7a1caa4920d5151d779e96cd1536f2fbe  

MARSS-FILTRY_ZW015010024.iso c415127bde80302a851240a169fff0592e864d2f93e9a21c7fd775fdb4788145

SKM_C250i24100408500.bat 36c464519a4cce8d0fcdb22a8974923fd51d915075eba9e62ade54a9c396844d  

UPM-0002607-1-2024-UP-3762.iso  e9fc754844df1a7196a001ac3dfbcf28b80397a718a3ceb8d397378a6375ff62  

Comanda KOMARON TRADE SRL 435635Lukketid.bat 1bf09bcb5bfa440fc6ce5c1d3f310fb274737248bf9acdd28bea98c9163a745a  

311861751714730477170144.bat f87448d722e160584e40feaad0769e170056a21588679094f7d58879cdb23623  

Estimate_buy_product_purchase_order_import_list_10_10_2024_000000101024.cmd f20670ed0cdc2d9a2a75884548e6e6a3857bbf66cfbfb4afe04a3354da9067c9  

PAYMENT TERM.bat 4c90504c86f1e77b0a75a1c7408adf1144f2a0e3661c20f2bf28d168e3408429  

Arbitrre.cmd  8ef4cb5ad7d5053c031690b9d04d64ba5d0d90f7bf8ba5e74cb169b5388e92c5  

KZЗапрос продукта SKM_32532667622352352Arvehygiejnikernes.bat 4ddd3369a51621b0009b6d993126fcb74b52e72f8cacd71fcbc401cda03108cb  

Order_AP568.bat fda4e04894089be87f520144d8a6141074d63d33b29beb28fd042b0ecc06fbbc  

C:\Users\user\Documents\ConnectWiseControl\Temp\Blodprocenternes.cmd e5f5d9855be34b44ad4c9b1c5722d1a6dff2f4a6878a874df1209d813aea7094  

Productivenesses.cmd a7268e906b86f7c1bb926278bf88811cb12189de0db42616e5bbb3dc426a4ef5  

Doktriner.cmd 74d468acd0493a6c5d72387c8e225cc0243ae1a331cd1e2d38f75ed8812347dd  

final_buy_product_purchase_order_import_list_11_10_2024_000000111024.cmd a2127d63bc0204c17d4657e5ae6930cab6ab33ae3e65b82e285a8757f39c4da9  

ORDER_U769.bat b45d9b5dbe09b2ca45d66432925842b0f698c9d269d3c7b5148cc26bdc2a92d0  

Beschwerde-Rechtsanwalt.bat 229c4ce294708561801b16eed5a155c8cfe8c965ea99ac3cfb4717a35a1492f3  

upit nr5634 10_08_2024.cmd 5854d9536371389fb0f1152ebc1479266d36ec4e06b174619502a6db1b593d71  

C:\Users\user\AppData\Local\Temp\Doktriner.cmd 140dcb39308d044e3e90610c65a08e0abc6a3ac22f0c9797971f0c652bb29add  

Fedtsyresammenstning.cmd 0b1c44b202ede2e731b2d9ee64c2ce333764fbff17273af831576a09fc9debfa  

HENIKENPLANT PROJECT PROPOSAL BID_24-0976·pdf.cmd 31a72d94b14bf63b07d66d023ced28092b9253c92b6e68397469d092c2ffb4a6  

MAIN ORDER.bat 85d1877ceda7c04125ca6383228ee158062301ae2b4e4a4a698ef8ed94165c7c  

Narudzba ACH0036173.bat 8d7324d66484383eba389bc2a8a6d4e9c4cb68bfec45d887b7766573a306af68  

Sludger.cmd 45b7b8772d9fe59d7df359468e3510df1c914af41bd122eeb5a408d045399a14  

Glasmester.bat b0e69f895f7b0bc859df7536d78c2983d7ed0ac1d66c243f44793e57d346049d  

PERMINTAAN ANGGARAN (Universitas IPB) ID177888·pdf.cmd 09a3bb4be0a502684bd37135a9e2cbaa3ea0140a208af680f7019811b37d28d6  

C:\Users\user\Documents\ConnectWiseControl\Temp\Bidcock.cmd 0996e7b37e8b41ff0799996dd96b5a72e8237d746c81e02278d84aa4e7e8534e  

PO++380.101483.bat a9af33c8a9050ee6d9fe8ce79d734d7f28ebf36f31ad8ee109f9e3f992a8d110  

Network IOCs

91[.]109.20.161

137[.]184.191.215

185[.]248.196.6

hxxps://filedn[.]com/lK8iuOs2ybqy4Dz6sat9kSz/Frihandelsaftalen40.fla

hxxps://careerfinder[.]ro/vn/Traurigheder[.]sea

hxxp://inversionesevza[.]com/wp-includes/blocks_/Dekupere.pcz

hxxps://rareseeds[.]zendesk[.]com/attachments/token/G9SQnykXWFAnrmBcy8MzhciEs/?name=PO++380.101483.bat

Detection

Yara rule

rule GuLoader_Obfuscated_Powershell 
{ 
   meta: 
       description = "Detects Obfuscated GuLoader Powershell Scripts" 
       author = "[email protected]" 
       date = "2024-10-14" 
   strings: 
      $hidden_window = { 7374617274202f6d696e20706f7765727368656c6c2e657865202d77696e646f777374796c652068696464656e2022 } 
      $for_loop = /for\s*\(\s*\$[a-zA-Z0-9_]+\s*=\s*\d+;\s*\$[a-zA-Z0-9_]+\s*-lt\s*\$[a-zA-Z0-9_]+\s*;\s*\$[a-zA-Z0-9_]+\s*\+=\s*\d+\s*\)/ 
   condition: 
      $for_loop and $hidden_window 

MITRE ATT&CK

T1566.001  Phishing: Malicious Attachment  

T1055 Process Injection  

T1204.002  User Execution: Malicious File  

T1547.001  Boot or Logon Autostart Execution: Registry Run Keys / Startup Folder  

T1140  Deobfuscate/Decode Files or Information  

T1622  Debugger Evasion  

T1001.001  Junk Code  

T1105  Ingress Tool Transfer  

T1059.001  Command and Scripting Interpreter: Powershell  

T1497.003  Virtualization/Sandbox Evasion: Time Based Evasion  

T1071.001  Application Layer Protocol: Web Protocols

References:

[1] https://www.crowdstrike.com/en-us/blog/guloader-dissection-reveals-new-anti-analysis-techniques-and-code-injection-redundancy/  

[2] https://www.checkpoint.com/cyber-hub/threat-prevention/what-is-malware/guloader-malware/

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
Tara Gould
Malware Research Lead

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

behavioral detectionDefault blog imageDefault blog image

Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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