May 28, 2024

Stemming the Citrix Bleed Vulnerability with Darktrace’s ActiveAI Security Platform

This blog delves into Darktrace’s investigation into the exploitation of the Citrix Bleed vulnerability on the network of a customer in late 2023. Darktrace’s Self-Learning AI ensured the customer was well equipped to track the post-compromise activity and identify affected devices.

Written by

Vivek Rajan

Cyber Analyst

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

Vivek Rajan

Cyber Analyst

May 2024

What is Citrix Bleed?

Since August 2023, cyber threat actors have been actively exploiting one of the most significant critical vulnerabilities disclosed in recent years: Citrix Bleed. Citrix Bleed, also known as CVE-2023-4966, remained undiscovered and even unpatched for several months, resulting in a wide range of security incidents across business and government sectors [1].

How does Citrix Bleed vulnerability work?

The vulnerability, which impacts the Citrix Netscaler Gateway and Netscaler ADC products, allows for outside parties to hijack legitimate user sessions, thereby bypassing password and multifactor authentication (MFA) requirements.

When used as a means of initial network access, the vulnerability has resulted in the exfiltration of sensitive data, as in the case of Xfinity, and even the deployment of ransomware variants including Lockbit [2]. Although Citrix has released a patch to address the vulnerability, slow patching procedures and the widespread use of these products has resulted in the continuing exploitation of Citrix Bleed into 2024 [3].

How Does Darktrace Handle Citrix Bleed?

Darktrace has demonstrated its proficiency in handling the exploitation of Citrix Bleed since it was disclosed back in 2023; its anomaly-based approach allows it to efficiently identify and inhibit post-exploitation activity as soon as it surfaces. Rather than relying upon traditional rules and signatures, Darktrace’s Self-Learning AI enables it to understand the subtle deviations in a device’s behavior that would indicate an emerging compromise, thus allowing it to detect anomalous activity related to the exploitation of Citrix Bleed.

In late 2023, Darktrace identified an instance of Citrix Bleed exploitation on a customer network. As this customer had subscribed to the Proactive Threat Notification (PTN) service, the suspicious network activity surrounding the compromise was escalated to Darktrace’s Security Operation Center (SOC) for triage and investigation by Darktrace Analysts, who then alerted the customer’s security team to the incident.

Darktrace’s Coverage

Initial Access and Beaconing of Citrix Bleed

Darktrace’s initial detection of indicators of compromise (IoCs) associated with the exploitation of Citrix Bleed actually came a few days prior to the SOC alert, with unusual external connectivity observed from a critical server. The suspicious connection in question, a SSH connection to the rare external IP 168.100.9[.]137, lasted several hours and utilized the Windows PuTTY client. Darktrace also identified an additional suspicious IP, namely 45.134.26[.]2, attempting to contact the server. Both rare endpoints had been linked with the exploitation of the Citrix Bleed vulnerability by multiple open-source intelligence (OSINT) vendors [4] [5].

Figure 1: Darktrace model alert highlighting an affected device making an unusual SSH connection to 168.100.9[.]137 via port 22.

As Darktrace is designed to identify network-level anomalies, rather than monitor edge infrastructure, the initial exploitation via the typical HTTP buffer overflow associated with this vulnerability fell outside the scope of Darktrace’s visibility. However, the aforementioned suspicious connectivity likely constituted initial access and beaconing activity following the successful exploitation of Citrix Bleed.

Command and Control (C2) and Payload Download

Around the same time, Darktrace also detected other devices on the customer’s network conducting external connectivity to various endpoints associated with remote management and IT services, including Action1, ScreenConnect and Fixme IT. Additionally, Darktrace observed devices downloading suspicious executable files, including “tniwinagent.exe”, which is associated with the tool Total Network Inventory. While this tool is typically used for auditing and inventory management purposes, it could also be leveraged by attackers for the purpose of lateral movement.

Defense Evasion

In the days surrounding this compromise, Darktrace observed multiple devices engaging in potential defense evasion tactics using the ScreenConnect and Fixme IT services. Although ScreenConnect is a legitimate remote management tool, it has also been used by threat actors to carry out C2 communication [6]. ScreenConnect itself was the subject of a separate critical vulnerability which Darktrace investigated in early 2024. Meanwhile, CISA observed that domains associated with Fixme It (“fixme[.]it”) have been used by threat actors attempting to exploit the Citrix Bleed vulnerability [7].

Reconnaissance and Lateral Movement

A few days after the detection of the initial beaconing communication, Darktrace identified several devices on the customer’s network carrying out reconnaissance and lateral movement activity. This included SMB writes of “PSEXESVC.exe”, network scanning, DCE-RPC binds of numerous internal devices to IPC$ shares and the transfer of compromise-related tools. It was at this point that Darktrace’s Self-Learning AI deemed the activity to be likely indicative of an ongoing compromise and several Enhanced Monitoring models alerted, triggering the aforementioned PTNs and investigation by Darktrace’s SOC.

Darktrace observed a server on the network initiating a wide range of connections to more than 600 internal IPs across several critical ports, suggesting port scanning, as well as conducting unexpected DCE-RPC service control (svcctl) activity on multiple internal devices, amongst them domain controllers. Additionally, several binds to server service (srvsvc) and security account manager (samr) endpoints via IPC$ shares on destination devices were detected, indicating further reconnaissance activity. The querying of these endpoints was also observed through RPC commands to enumerate services running on the device, as well as Security Account Manager (SAM) accounts.

Darktrace also identified devices performing SMB writes of the WinRAR data compression tool, in what likely represented preparation for the compression of data prior to data exfiltration. Further SMB file writes were observed around this time including PSEXESVC.exe, which was ultimately used by attackers to conduct remote code execution, and one device was observed making widespread failed NTLM authentication attempts on the network, indicating NTLM brute-forcing. Darktrace observed several devices using administrative credentials to carry out the above activity.

In addition to the transfer of tools and executables via SMB, Darktrace also identified numerous devices deleting files through SMB around this time. In one example, an MSI file associated with the patch management and remediation service, Action1, was deleted by an attacker. This legitimate security tool, if leveraged by attackers, could be used to uncover additional vulnerabilities on target networks.

A server on the customer’s network was also observed writing the file “m.exe” to multiple internal devices. OSINT investigation into the executable indicated that it could be a malicious tool used to prevent antivirus programs from launching or running on a network [8].

Impact and Data Exfiltration

Following the initial steps of the breach chain, Darktrace observed numerous devices on the customer’s network engaging in data exfiltration and impact events, resulting in additional PTN alerts and a SOC investigation into data egress. Specifically, two servers on the network proceeded to read and download large volumes of data via SMB from multiple internal devices over the course of a few hours. These hosts sent large outbound volumes of data to MEGA file storage sites using TLS/SSL over port 443. Darktrace also identified the use of additional file storage services during this exfiltration event, including 4sync, file[.]io, and easyupload[.]io. In total the threat actor exfiltrated over 8.5 GB of data from the customer’s network.

Figure 2: Darktrace Cyber AI Analyst investigation highlighting the details of a data exfiltration attempt.

Finally, Darktrace detected a user account within the customer’s Software-as-a-Service (SaaS) environment conducting several suspicious Office365 and AzureAD actions from a rare IP for the network, including uncommon file reads, creations and the deletion of a large number of files.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled on the network and the post-exploitation activity was able to progress until the customer was made aware of the attack by Darktrace’s SOC team. Had RESPOND been active and configured in autonomous response mode at the time of the attack, it would have been able to promptly contain the post-exploitation activity by blocking external connections, shutting down any C2 activity and preventing the download of suspicious files, blocking incoming traffic, and enforcing a learned ‘pattern of life’ on offending devices.

Conclusion

Given the widespread use of Netscaler Gateway and Netscaler ADC, Citrix Bleed remains an impactful and potentially disruptive vulnerability that will likely continue to affect organizations who fail to address affected assets. In this instance, Darktrace demonstrated its ability to track and inhibit malicious activity stemming from Citrix Bleed exploitation, enabling the customer to identify affected devices and enact their own remediation.

Darktrace’s anomaly-based approach to threat detection allows it to identify such post-exploitation activity resulting from the exploitation of a vulnerability, regardless of whether it is a known CVE or a zero-day threat. Unlike traditional security tools that rely on existing threat intelligence and rules and signatures, Darktrace’s ability to identify the subtle deviations in a compromised device’s behavior gives it a unique advantage when it comes to identifying emerging threats.

Credit to Vivek Rajan, Cyber Analyst, Adam Potter, Cyber Analyst

Appendices

Darktrace Model Coverage

Device / Suspicious SMB Scanning Activity

Device / ICMP Address Scan

Device / Possible SMB/NTLM Reconnaissance

Device / Network Scan

Device / SMB Lateral Movement

Device / Possible SMB/NTLM Brute Force

Device / Suspicious Network Scan Activity

User / New Admin Credentials on Server

Anomalous File / Internal::Unusual Internal EXE File Transfer

Compliance / SMB Drive Write

Device / New or Unusual Remote Command Execution

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Rare WinRM Incoming

Anomalous Connection / Unusual Admin SMB Session

Device / Unauthorised Device

User / New Admin Credentials on Server

Anomalous Server Activity / Outgoing from Server

Device / Long Agent Connection to New Endpoint

Anomalous Connection / Multiple Connections to New External TCP Port

Device / New or Uncommon SMB Named Pipe

Device / Multiple Lateral Movement Model Breaches

Device / Large Number of Model Breaches

Compliance / Remote Management Tool On Server

Device / Anomalous RDP Followed By Multiple Model Breaches

Device / SMB Session Brute Force (Admin)

Device / New User Agent

Compromise / Large Number of Suspicious Failed Connections

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Enhanced Unusual External Data Transfer

Device / Increased External Connectivity

Unusual Activity / Unusual External Data to New Endpoints

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Uncommon 1 GiB Outbound

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Compliance / Possible Unencrypted Password File On Server

Anomalous Connection / Suspicious Read Write Ratio and Rare External

Device / Reverse DNS Sweep]

Unusual Activity / Possible RPC Recon Activity

Anomalous File / Internal::Executable Uploaded to DC

Compliance / SMB Version 1 Usage

Darktrace AI Analyst Incidents

Scanning of Multiple Devices

Suspicious Remote Service Control Activity

SMB Writes of Suspicious Files to Multiple Devices

Possible SSL Command and Control to Multiple Devices

Extensive Suspicious DCE-RPC Activity

Suspicious DCE-RPC Activity

Internal Downloads and External Uploads

Unusual External Data Transfer

Unusual External Data Transfer to Multiple Related Endpoints

MITRE ATT&CK Mapping

Technique – Tactic – ID – Sub technique of

Network Scanning – Reconnaissance - T1595 - T1595.002

Valid Accounts – Defense Evasion, Persistence, Privilege Escalation, Initial Access – T1078 – N/A

Remote Access Software – Command and Control – T1219 – N/A

Lateral Tool Transfer – Lateral Movement – T1570 – N/A

Data Transfers – Exfiltration – T1567 – T1567.002

Compressed Data – Exfiltration – T1030 – N/A

NTLM Brute Force – Brute Force – T1110 - T1110.001

AntiVirus Deflection – T1553 - NA

Ingress Tool Transfer - COMMAND AND CONTROL - T1105 - NA

Indicators of Compromise (IoCs)

204.155.149[.]37 – IP – Possible Malicious Endpoint

199.80.53[.]177 – IP – Possible Malicious Endpoint

168.100.9[.]137 – IP – Malicious Endpoint

45.134.26[.]2 – IP – Malicious Endpoint

13.35.147[.]18 – IP – Likely Malicious Endpoint

13.248.193[.]251 – IP – Possible Malicious Endpoint

76.223.1[.]166 – IP – Possible Malicious Endpoint

179.60.147[.]10 – IP – Likely Malicious Endpoint

185.220.101[.]25 – IP – Likely Malicious Endpoint

141.255.167[.]250 – IP – Malicious Endpoint

106.71.177[.]68 – IP – Possible Malicious Endpoint

cat2.hbwrapper[.]com – Hostname – Likely Malicious Endpoint

aj1090[.]online – Hostname – Likely Malicious Endpoint

dc535[.]4sync[.]com – Hostname – Likely Malicious Endpoint

204.155.149[.]140 – IP - Likely Malicious Endpoint

204.155.149[.]132 – IP - Likely Malicious Endpoint

204.155.145[.]52 – IP - Likely Malicious Endpoint

204.155.145[.]49 – IP - Likely Malicious Endpoint

References

Written by

Vivek Rajan

Cyber Analyst

Inside the SOC

Written by

Vivek Rajan

Cyber Analyst

Senior Product Marketing Manager, Email

Watch the NIS2 Webinar

Blog

Email

December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

No labeling burden; no regex/rule maintenance
Works when tags are missing, wrong, or stale
Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

Flags risk without predefined rules or IOCs
Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

Low-latency, on-send enforcement
Non-generative for predictable, explainable outcomes
Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

Can it operate label free when tags are missing or wrong? 
Does it truly learn per user behavior (no shortcuts)? 
Is there a domain specific model behind the content understanding (not a generic LLM)? 
Does it explain decisions to both analysts and end users? 
Will it integrate with your label program and SOC workflows rather than duplicate them?

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

[related-resource]

About the author

Carlos Gray

Senior Product Marketing Manager, Email

Blog

Email

December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAIL^TM and Darktrace / IDENTITY^TM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services. This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.

Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress, as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique

Phishing - T1566

Adversary-in-the-Middle - T1557

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Check out this article by Darktrace: Stemming the Citrix Bleed Vulnerability with Darktrace’s ActiveAI Security Platform

Stemming the Citrix Bleed Vulnerability with Darktrace’s ActiveAI Security Platform

What is Citrix Bleed?

How does Citrix Bleed vulnerability work?

How Does Darktrace Handle Citrix Bleed?

Darktrace’s Coverage

Initial Access and Beaconing of Citrix Bleed

Command and Control (C2) and Payload Download

Defense Evasion

Reconnaissance and Lateral Movement

Impact and Data Exfiltration

Conclusion

Appendices

Darktrace Model Coverage

Darktrace AI Analyst Incidents

MITRE ATT&CK Mapping

Indicators of Compromise (IoCs)

References

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

Why organizations are moving to label-free, behavioral DLP for outbound email

Enjoying the blog?

More in this series

Blog

Email

December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

Why outbound email DLP needs reinventing

How data loss actually happens

What traditional DLP approaches offer (and where gaps remain)

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

2) Behavioral (vs. rules, signatures, threat intelligence)

3) Proprietary DSLM (vs. generic LLM)

The Darktrace approach to DLP

A buyer’s checklist for DLP solutions

Blog

Email

December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

What is an Adversary-in-the-middle (AiTM) attack?

What does an AiTM attack look like?

Inbound phishing email

Proxy-based credential theft and session hijacking

Follow-on attacks

Darktrace’s detection of an AiTM attack

Internal and external phishing

Darktrace’s Response

Conclusion

Appendices

Models