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February 11, 2025

Defending Against Living-off-the-Land Attacks: Anomaly Detection in Action

Discover how Darktrace detected and responded to cyberattacks using Living-off-the-Land (LOTL) tactics to exploit trusted services and tools on customer networks.
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
Alexandra Sentenac
Cyber Analyst
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11
Feb 2025

What is living-off-the-land?

Threat actors employ a variety of techniques to compromise target networks, including exploiting unpatched vulnerabilities, abusing misconfigurations, deploying backdoors, and creating custom malware. However, these methods generate a lot of noise and are relatively easy for network and host-based monitoring tools to detect, especially once indicators of compromise (IoCs) and tactics, techniques, and procedures (TTPs) are published by the cybersecurity community.

Living-off-the-Land (LOTL) techniques, however, allow attacks to remain nearly invisible to Endpoint Detection and Response (EDR) tools – leveraging trusted protocols, applications and native systems to carry out malicious activity. While mitigations exist, they are often poorly implemented. The Cybersecurity and Infrastructure Security Agency (CISA) found that some organizations “lacked security baselines, allowing [Living-off-the-Land binaries (LOLBins)] to execute and leaving analysts unable to identify anomalous activity” and “organizations did not appropriately tune their detection tools to reduce alert noise, leading to an unmanageable level of alerts to sift through and action" [1].

Darktrace / NETWORK addresses this challenge across Information Technology (IT), Operational Technology (OT), and cloud environments by continuously analyzing network traffic and identifying deviations from normal behavior with its multi-layered AI – helping organizations detect and respond to LOTL attacks in real time.

Darktrace’s detection of LOTL attacks

This blog will review two separate attacks detected by Darktrace that leveraged LOTL techniques at several stages of the intrusion.

Case A

Reconnaissance

In September 2024, a malicious actor gained access to a customer network via their Virtual Private Network (VPN) from two desktop devices that had no prior connection history. Over two days, the attacker conducted multiple network scans, targeting ports associated with Remote Desktop Protocol (RDP) and NTLM authentication. Darktrace detected this unusual activity, triggering multiple alerts for scanning and enumeration activity.

Unusual NTLM authentication attempts using default accounts like “Guest” and “Administrator” were detected. Two days after the initial intrusion, suspicious DRSGetNCChanges requests were observed on multiple domain controllers (DCs), targeting the Directory Replication Service RPC interface (i.e., drsuapi) – a technique used to extract account hashes from DCs. This process can be automated using tools like Mimikatz's DcSync and DCShadow

Around the same time, attacker-controlled devices were seen presenting an admin credential and another credential potentially granting access to Cisco Firewall systems, suggesting successful privilege escalation. Due to the severity of this activity, Darktrace’s Autonomous Response was triggered to prevent the device from further deviation from its normal behavior. However, because Autonomous Response was configured in Human Confirmation mode, the response actions had to be manually applied by the customer.

Cyber AI Analyst Critical Incident showing the unusual DRSGetNCChanges requests following unusual scanning activity.
Figure 1: Cyber AI Analyst Critical Incident showing the unusual DRSGetNCChanges requests following unusual scanning activity.

Lateral movement

Darktrace also detected anomalous RDP connections to domain controllers, originating from an attacker-controlled device using admin and service credentials. The attacker then successfully pivoted to a likely RDP server, leveraging the RDP protocol – one of the most commonly used for lateral movement in network compromises observed by Darktrace.

Cyber Analyst Incident displaying unusual RDP lateral movement connections
Figure 2: Cyber Analyst Incident displaying unusual RDP lateral movement connections.

Tooling

Following an incoming RDP connection, one of the DCs made a successful GET request to the URI '/download/122.dll' on the 100% rare IP, 146.70.145[.]189. The request returned an executable file, which open-source intelligence (OSINT) suggests is likely a CobaltStrike C2 sever payload [2] [3]. Had Autonomous Response been enabled here, it would have blocked all outgoing traffic from the DC allowing the customer to investigate and remediate.

Additionally, Darktrace detected a suspicious CreateServiceW request to the Service Control (SVCCTL) RPC interface on a server. The request executed commands using ‘cmd.exe’ to perform the following actions

  1. Used ‘tasklist’ to filter processes named ”lsass.exe” (Local Security Authority Subsystem Service) to find its specific process ID.
  2. Used “rundll32.exe” to execute the MiniDump function from the “comsvcs.dll” library, creating a memory dump of the “lsass.exe” process.
  3. Saved the output to a PNG file in a temporary folder,

Notably, “cmd.exe” was referenced as “CMd.EXE” within the script, likely an attempt to evade detection by security tools monitoring for specific keywords and patterns.

Model Alert Log showing the unusual SVCCTL create request.
Figure 3: Model Alert Log showing the unusual SVCCTL create request.

Over the course of three days, this activity triggered around 125 Darktrace / NETWORK alerts across 11 internal devices. In addition, Cyber AI Analyst launched an autonomous investigation into the activity, analyzing and connecting 16 separate events spanning multiple stages of the cyber kill chain - from initial reconnaissance to payload retrieval and lateral movement.

Darktrace’s comprehensive detection enabled the customer’s security team to remediate the compromise before any further escalation was observed.

Case B

Between late 2023 and early 2024, Darktrace identified a widespread attack that combined insider and external threats, leveraging multiple LOTL tools for reconnaissance and lateral movement within a customer's network.

Reconnaissance

Initially, Darktrace detected the use of a new administrative credential by a device, which then made unusual RDP connections to multiple internal systems, including a 30-minute connection to a DC. Throughout the attack, multiple unusual RDP connections using the new administrative credential “%admin!!!” were observed, indicating that this protocol was leveraged for lateral movement.

The next day, a Microsoft Defender Security Integration alert was triggered on the device due to suspicious Windows Local Security Authority Subsystem Service (LSASS) credential dump behavior. Since the LSASS process memory can store operating system and domain admin credentials, obtaining this sensitive information can greatly facilitate lateral movement within a network using legitimate tools such as PsExec or Windows Management Instrumentation (WMI) [4]. Security integrations with other security vendors like this one can provide insights into host-based processes, which are typically outside of Darktrace’s coverage. Darktrace’s anomaly detection and network activity monitoring help prioritize the investigation of these alerts.

Three days later, the attacker was observed logging into the DC and querying tickets for the Lightweight Directory Access Protocol (LDAP) service using the default credential “Administrator.” This activity, considered new by Darktrace, triggered an Autonomous Response action that blocked further connections on Kerberos port 88 to the DC. LDAP provides a central location to access and manage data about computers, servers, users, groups, and policies within a network. LDAP enumeration can provide valuable Active Directory (AD) object information to an attacker, which can be used to identify critical attack paths or accounts with high privileges.

Lateral movement

Following the incoming RDP connection, the DC began scanning activities, including RDP and Server Block Message (SMB) services, suggesting the attacker was using remote access for additional reconnaissance. Outgoing RDP connection attempts to over 100 internal devices were observed, with around 5% being successful, highlighting the importance of this protocol for the threat actor’s lateral movement.

Around the same time, the DC made WMI, PsExec, and service control connections to two other DCs, indicating further lateral movement using native administrative protocols and tools. These functions can be leveraged by attackers to query system information, run malicious code, and maintain persistent access to compromised devices while avoiding traditional security tool alarms. In this case, requested services included the IWbemServices (used to access WMI services) and IWbemFetchSmartEnum (used to retrieve a network-optimized enumerator interface) interfaces, with ExecQuery operations detected for the former. This method returns an enumerable collection of IWbemClassObject interface objects based on a query.

Additionally, unusual Windows Remote Management (WinRM) connections to another domain controller were observed. WinRM is a Microsoft protocol that allows systems to exchange and access management information over HTTP(S) across a network, such as running executables or modifying the registry and services.

Cyber AI Analyst Incident showing unusual WMI activity between the two DCs.
Figure 4: Cyber AI Analyst Incident showing unusual WMI activity between the two DCs.

The DC was also detected writing the file “PSEXESVC.exe” to the “ADMIN$” share of another internal device over the SMB file transfer network protocol. This activity was flagged as highly unusual by Darktrace, as these two devices had not previously engaged in this type of SMB connectivity.

It is rare for an attacker to immediately find the information or systems they are after, making it likely they will need to move around the network before achieving their objectives. Tools such as PsExec enable attackers to do this while largely remaining under the radar. With PsExec, attackers who gain access to a single system can connect to and execute commands remotely on other internal systems, access sensitive information, and spread their attack further into the environment.

Model Alert Event Log showing the new write of the file “PSEXESVC.exe” by one of the compromised devices over an SMB connection initiated at an unusual time.
Figure 5. Model Alert Event Log showing the new write of the file “PSEXESVC.exe” by one of the compromised devices over an SMB connection initiated at an unusual time.

Darktrace further observed the DC connecting to the SVCCTL endpoint on a remote device and performing the CreateServiceW operation, which was flagged as highly unusual based on previous behavior patterns between the two devices. Additionally, new ChangeServiceConfigW operations were observed from another device.

Aside from IWbemServices requests seen on multiple devices, Darktrace also detected multiple internal devices connecting to the ITaskSchedulerService interface over DCE-RPC and performing new SchRpcRegisterTask operations, which register a task on the destination system. Attackers can exploit the task scheduler to facilitate the initial or recurring execution of malicious code by a trusted system process, often with elevated permissions. The creation of these tasks was considered new or highly unusual and triggered several anomalous ITaskScheduler activity alerts.

Conclusion

As pointed out by CISA, threat actors frequently exploit the lack of implemented controls on their target networks, as demonstrated in the incidents discussed here. In the first case, VPN access was granted to all domain users, providing the attacker with a point of entry. In the second case, there were no restrictions on the use of RDP within the targeted network segment, allowing the attackers to pivot from device to device.

Darktrace assists security teams in monitoring for unusual use of LOTL tools and protocols that can be leveraged by threat actors to achieve a wide range of objectives. Darktrace’s Self-Learning AI sifts through the network traffic noise generated by these trusted tools, which are essential to administrators and developers in their daily tasks, and highlights any anomalous and potentially unexpected use.

Credit to Alexandra Sentenac (Senior Cyber Analyst) and Ryan Traill (Analyst Content Lead)

References

[1] https://www.cisa.gov/sites/default/files/2024-02/Joint-Guidance-Identifying-and-Mitigating-LOTL_V3508c.pdf

[2] https://www.virustotal.com/gui/ip-address/146.70.145.189/community

[3] https://www.virustotal.com/gui/file/cc9a670b549d84084618267fdeea13f196e43ae5df0d88e2e18bf5aa91b97318

[4]https://www.microsoft.com/en-us/security/blog/2022/10/05/detecting-and-preventing-lsass-credential-dumping-attacks

MITRE Mapping

INITIAL ACCESS - External Remote Services

DISCOVERY - Remote System Discovery

DISCOVERY - Network Service Discovery

DISCOVERY - File and Directory Discovery

CREDENTIAL ACCESS – OS Credential Dumping: LSASS Memory

LATERAL MOVEMENT - Remote Services: Remote Desktop Protocol

LATERAL MOVEMENT - Remote Services: SMB/Windows Admin Shares

EXECUTION - System Services: Service Execution

PERSISTENCE - Scheduled Task

COMMAND AND CONTROL - Ingress Tool Transfer

Darktrace Model Detections

Case A

Device / Suspicious Network Scan Activity

Device / Network Scan

Device / ICMP Address Scan

Device / Reverse DNS Sweep

Device / Suspicious SMB Scanning Activity

Device / Possible SMB/NTLM Reconnaissance

Anomalous Connection / Unusual Admin SMB Session

Device / SMB Session Brute Force (Admin)

Device / Possible SMB/NTLM Brute Force

Device / SMB Lateral Movement

Device / Anomalous NTLM Brute Force

Anomalous Connection / SMB Enumeration

Device / SMB Session Brute Force (Non-Admin)

Device / Anomalous SMB Followed By Multiple Model Breaches

Anomalous Connection / Possible Share Enumeration Activity

Device / RDP Scan

Device / Anomalous RDP Followed By Multiple Model Breaches

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Anomalous Connection / High Priority DRSGetNCChanges

Compliance / Default Credential Usage

User / New Admin Credentials on Client

User / New Admin Credentials on Server

Device / Large Number of Model Breaches from Critical Network Device

User / New Admin Credential Ticket Request

Compromise / Unusual SVCCTL Activity

Anomalous Connection / New or Uncommon Service Control

Anomalous File / Script from Rare External Location

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous File / EXE from Rare External Location

Anomalous File / Numeric File Download

Device / Initial Breach Chain Compromise

Device / Multiple Lateral Movement Model Breaches

Device / Large Number of Model Breaches

Compromise / Multiple Kill Chain Indicators

Case B

User / New Admin Credentials on Client

Compliance / Default Credential Usage

Anomalous Connection / SMB Enumeration

Device / Suspicious SMB Scanning Activity

Device / RDP Scan

Device / New or Uncommon WMI Activity

Device / Anomaly Indicators / New or Uncommon WMI Activity Indicator

Device / New or Unusual Remote Command Execution

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Active Remote Desktop Tunnel

Compliance / SMB Drive Write

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Device / Multiple Lateral Movement Model Breaches

Device / Anomalous ITaskScheduler Activity

Anomalous Connection / Unusual Admin RDP Session

Device / Large Number of Model Breaches from Critical Network Device

Compliance / Default Credential Usage

IOC - Type - Description/Probability

146.70.145[.]189 - IP Address - Likely C2 Infrastructure

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
Alexandra Sentenac
Cyber Analyst

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December 18, 2025

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

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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]

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Carlos Gray
Senior Product Marketing Manager, Email

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December 17, 2025

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

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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 / EMAILTM and Darktrace / IDENTITYTM 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.

Darktrace’s detection of the anomalous email rule creation.
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”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
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.
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.

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