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April 12, 2022

Efficient Incident Reporting: Darktrace AI Analyst

Discover how Darktrace's Cyber AI Analyst accelerates incident reporting to the US federal government, enhancing cybersecurity response times.
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
Justin Fier
SVP, Red Team Operations
Written by
Sally Kenyon Grant
VP, Darktrace Federal
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12
Apr 2022

On March 15, 2022, President Biden signed the Cyber Incident Reporting for Critical Infrastructure Act into law, included as part of the Congressional Omnibus Appropriations bill. The law requires critical infrastructure owners and operators to quickly notify the Cyber and Infrastructure Security Agency (CISA) of ransomware payments and significant cyber-attacks.

The Cyber Incident Reporting for Critical Infrastructure Act creates two new reporting requirements:

  1. an obligation to report certain cyber incidents to DHS CISA within 72 hours
  2. an obligation to report ransomware payments within 24 hours

Supporting the new law, Darktrace AI accelerates the cyber incident reporting process. Specifically, Darktrace’s Cyber AI Analyst understands the connections among disparate security incidents with supervised machine learning and autonomously writes incident reports in human-readable language using natural language processing (NLP). These Darktrace incident reports allow human analysts to send reports to CISA quickly and efficiently.

In the below real-world attack case study, we demonstrate how Cyber AI Analyst facilitates seamless reporting for critical infrastructure organizations that fall victim to ransomware and malicious data exfiltration. The AI technology, trained on human analyst behavior, replicates investigations at machine speed and scale, surfacing relevant details in minutes and allowing security teams to understand what happened precisely and share this information with the relevant authorities.

The below threat investigation details a significant threat find on a step by step level in technical detail to demonstrate the power and speed of Cyber AI Analyst.

Cyber AI Analyst’s incident report

When ransomware struck this organization, Cyber AI Analyst was invaluable, autonomously investigating the full scope of the incident and generating a natural language summary that clearly showed the progression of the attack.

Figure 1: Cyber AI Analyst reveals the full scope of the attack

In the aftermath of this attack, Darktrace’s technology also offered analyst assistance in mapping out the timeline of the attack and identifying what files were compromised, helping the security team identify anomalous activity related to the ransomware attack.

Figure 2: Cyber AI Analyst showing the stages of the attack chain undergone by the compromised device

With Darktrace AI’s insights, the team easily identified the timeline of the attack, affected devices, credentials used, file shares accessed, files exfiltrated, and malicious endpoints contacted, enabling the customer to disclose the scale of the attack and notify necessary parties.

This example demonstrates how Cyber AI Analyst empowers critical infrastructure owners and operators to swiftly report major cyber-attacks to the federal government. Considering that 72 hours is the reporting period is for significant incidents — and 24 hours for ransomware payments — Cyber AI Analyst is no longer a nice-to-have but a must-have for critical infrastructure.

Attack breakdown: Ransomware and data exfiltration

Cyber AI Analyst delivered the most critical information in an easy-to-read report — with no human touch involved — as shown in the incident report above. We will now break down the attack further to demonstrate how Darktrace’s Self-Learning AI understood the unusual activity throughout the attack lifecycle.

In this double extortion ransomware, attackers exfiltrated data over 22 days. The detections made by Darktrace’s Self-Learning AI, and the parallel investigation by Cyber AI Analyst, were used to map the attack chain and identify how and what data had been exfiltrated and encrypted.

The attack consisted of three general groups of events:

  • Unencrypted FTP (File Transfer Protocol) data exfiltration to rare malicious external endpoint in Bulgaria (May 9 07:23:46 UTC – May 21 03:06:46 UTC)
  • Ransomware encryption of files in network file shares (May 25 01:00:27 UTC – May 30 07:09:53 UTC)
  • Encrypted SSH (Secure Shell) data exfiltration to rare malicious external endpoint (May 29 16:43:37 UTC – May 30 13:23:59 UTC)
Figure 3: Timeline of the attack alongside Darktrace model breaches

First, uploads of internal data to a rare external endpoint in Bulgaria were observed within the networks. The exfiltration was preceded by SMB reads of internal file shares before approximately 450GB of data was exfiltrated via FTP.

Darktrace’s AI identified this threatening activity on its own, and the organization was quickly able to pinpoint what data had been exfiltrated, including files camouflaged by markings such as ‘Talent Acquisition’ and ‘Engineering and Construction,’ and legal and financial documents — suggesting that these were documents of an extremely sensitive nature.

Figure 4: Screenshots showing two model breaches relating to external uploads over FTP
Figure 5: Screenshot showing SMB reads from a file share before FTP upload

Model breaches:

  • Anomalous Connection / Unusual Incoming Data Volume
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / SMB Reads then Writes with Additional Extensions
  • Unusual Activity / Anomalous SMB Move & Write
  • Unusual Activity / High Volume Server Data Transfer
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / SMB Lateral Movement

Four days following this observed activity, Darktrace’s AI detected the deployment of ransomware when multiple compromised devices began making anomalous SMB connections to file shares that they do not typically access, reading and writing similar volumes to the SMB file shares, as well as writing additional extensions to files over SMB. The file extension comprised a random string of letters and was likely to be unique to this target.

Using Darktrace, the customer obtained a full list of files that had been encrypted. The list included apparent financial records in an ‘Accounts’ file share.

Figure 6: Model breach showing additional extension written to file during ransomware encryption

Model breaches:

  • Anomalous Connection / Unusual Incoming Data Volume
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / SMB Reads then Writes with Additional Extensions
  • Unusual Activity / Anomalous SMB Move & Write
  • Unusual Activity / High Volume Server Data Transfer
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / SMB Lateral Movement

Simultaneously, uploads of internal data to a rare external endpoint were observed within the network. The uploads were all performed using encrypted SSH/SFTP. In total, approximately 3.5GB of data was exfiltrated this way.

Despite the attacker using an encrypted channel to exfiltrate this data, Darktrace detected anomalous SMB file transfers prior to the external upload, indicating which files were exfiltrated. Here, Darktrace’s ability to go ‘back in time’ proved invaluable in helping analysts determine which files had been exfiltrated, although they were exfiltrated via an encrypted means.

Figure 7: Model breaches showing anomalous SMB activity before upload over SSH

Model breaches:

  • Anomalous Server Activity / Outgoing from Server
  • Compliance / SSH to Rare External Destination
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Device / Anomalous SMB Followed By Multiple Model Breaches
  • Device / Large Number of Model Breaches
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Data Sent To New External Device

How did the attack bypass the rest of the security stack?

Existing administrative credentials were used to escalate privileges within the network and perform malicious activity.

Had Darktrace Antigena been active, it would have actioned a targeted, autonomous response to contain the activity in its early stages. Antigena would have enforced the ‘pattern of life’ on the devices involved in anomalous SMB activity — containing activity such as reading from file shares that are not normally connected, appending extensions to files and blocking outgoing connections to rare external endpoints.

However, in this case, Antigena was not set up to take action – it was configured in Human Confirmation mode. The incident was clearly alerted on by Darktrace, and appeared as a top priority item in the security team’s workflow. However, the security team was not monitoring Darktrace’s user interface, and in the absence of any action taken by other tools, the attack was allowed to progress, and the organization was obligated to disclose the details of the incident.

Streamlining the reporting process

In the modern threat landscape, leaning on AI to stop fast-moving and sophisticated attacks at machine speed and scale is critical. As this attack shows, the technology also helps organizations fulfill reporting requirements in the aftermath of an attack.

New legislation requires timely disclosure; with many traditional approaches to security, organizations do not have the capacity to surface the full details after an attack. On top of this, collating these details can take days or weeks. This is why Darktrace is no longer a nice-to-have but a must-have for critical infrastructure organizations, which are now required to report significant incidents swiftly.

Darktrace’s AI detects malicious activity as it happens and empowers customers to quickly understand the timeline of a compromise, as well as files accessed and exfiltrated by an attacker. This not only prepares organizations to resist the most sophisticated attacks, but also accelerates and radically simplifies the process of reporting the data breach.

Security teams should not have to confront disclosure processes on their own. Attacks happen fast, and their aftermaths are messy – retrospective investigation of lost data can be a futile effort with traditional approaches. With Darktrace, security teams can meet disruptive and sudden attacks with precise and nimble means of uncovering data, as well as detection and mitigation of risk. And, should the need arise, rapid and accurate reporting of events is laid out on a silver platter by the AI.

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
Justin Fier
SVP, Red Team Operations
Written by
Sally Kenyon Grant
VP, Darktrace Federal

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July 8, 2025

Defending the Cloud: Stopping Cyber Threats in Azure and AWS with Darktrace

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Real-world intrusions across Azure and AWS

As organizations pursue greater scalability and flexibility, cloud platforms like Microsoft Azure and Amazon Web Services (AWS) have become essential for enabling remote operations and digitalizing corporate environments. However, this shift introduces a new set of security risks, including expanding attack surfaces, misconfigurations, and compromised credentials frequently exploited by threat actors.

This blog dives into three instances of compromise within a Darktrace customer’s Azure and AWS environment which Darktrace.

  1. The first incident took place in early 2024 and involved an attacker compromising a legitimate user account to gain unauthorized access to a customer’s Azure environment.
  2. The other two incidents, taking place in February and March 2025, targeted AWS environments. In these cases, threat actors exfiltrated corporate data, and in one instance, was able to detonate ransomware in a customer’s environment.

Case 1 - Microsoft Azure

Simplified timeline of the attack on a customer’s Azure environment.
Figure 1: Simplified timeline of the attack on a customer’s Azure environment.

In early 2024, Darktrace identified a cloud compromise on the Azure cloud environment of a customer in the Europe, the Middle East and Africa (EMEA) region.

Initial access

In this case, a threat actor gained access to the customer’s cloud environment after stealing access tokens and creating a rogue virtual machine (VM). The malicious actor was found to have stolen access tokens belonging to a third-party external consultant’s account after downloading cracked software.

With these stolen tokens, the attacker was able to authenticate to the customer’s Azure environment and successfully modified a security rule to allow inbound SSH traffic from a specific IP range (i.e., securityRules/AllowCidrBlockSSHInbound). This was likely performed to ensure persistent access to internal cloud resources.

Detection and investigation of the threat

Darktrace / IDENTITY recognized that this activity was highly unusual, triggering the “Repeated Unusual SaaS Resource Creation” alert.

Cyber AI Analyst launched an autonomous investigation into additional suspicious cloud activities occurring around the same time from the same unusual location, correlating the individual events into a broader account hijack incident.

Cyber AI Analyst’s investigation into unusual cloud activity performed by the compromised account.
Figure 2: Cyber AI Analyst’s investigation into unusual cloud activity performed by the compromised account.
Figure 2: Surrounding resource creation events highlighted by Cyber AI Analyst.
Figure 3: Surrounding resource creation events highlighted by Cyber AI Analyst.
Figure 4: Surrounding resource creation events highlighted by Cyber AI Analyst.

“Create resource service limit” events typically indicate the creation or modification of service limits (i.e., quotas) for a specific Azure resource type within a region. Meanwhile, “Registers the Capacity Resource Provider” events refer to the registration of the Microsoft Capacity resource provider within an Azure subscription, responsible for managing capacity-related resources, particularly those related to reservations and service limits. These events suggest that the threat actor was looking to create new cloud resources within the environment.

Around ten minutes later, Darktrace detected the threat actor creating or modifying an Azure disk associated with a virtual machine (VM), suggesting an attempt to create a rogue VM within the environment.

Threat actors can leverage such rogue VMs to hijack computing resources (e.g., by running cryptomining malware), maintain persistent access, move laterally within the cloud environment, communicate with command-and-control (C2) infrastructure, and stealthily deliver and deploy malware.

Persistence

Several weeks later, the compromised account was observed sending an invitation to collaborate to an external free mail (Google Mail) address.

Darktrace deemed this activity as highly anomalous, triggering a compliance alert for the customer to review and investigate further.

The next day, the threat actor further registered new multi-factor authentication (MFA) information. These actions were likely intended to maintain access to the compromised user account. The customer later confirmed this activity by reviewing the corresponding event logs within Darktrace.

Case 2 – Amazon Web Services

Simplified timeline of the attack on a customer’s AWS environment
Figure 5: Simplified timeline of the attack on a customer’s AWS environment

In February 2025, another cloud-based compromised was observed on a UK-based customer subscribed to Darktrace’s Managed Detection and Response (MDR) service.

How the attacker gained access

The threat actor was observed leveraging likely previously compromised credential to access several AWS instances within customer’s Private Cloud environment and collecting and exfiltrating data, likely with the intention of deploying ransomware and holding the data for ransom.

Darktrace alerting to malicious activity

This observed activity triggered a number of alerts in Darktrace, including several high-priority Enhanced Monitoring alerts, which were promptly investigated by Darktrace’s Security Operations Centre (SOC) and raised to the customer’s security team.

The earliest signs of attack observed by Darktrace involved the use of two likely compromised credentials to connect to the customer’s Virtual Private Network (VPN) environment.

Internal reconnaissance

Once inside, the threat actor performed internal reconnaissance activities and staged the Rclone tool “ProgramData\rclone-v1.69.0-windows-amd64.zip”, a command-line program to sync files and directories to and from different cloud storage providers, to an AWS instance whose hostname is associated with a public key infrastructure (PKI) service.

The threat actor was further observed accessing and downloading multiple files hosted on an AWS file server instance, notably finance and investment-related files. This likely represented data gathering prior to exfiltration.

Shortly after, the PKI-related EC2 instance started making SSH connections with the Rclone SSH client “SSH-2.0-rclone/v1.69.0” to a RockHoster Virtual Private Server (VPS) endpoint (193.242.184[.]178), suggesting the threat actor was exfiltrating the gathered data using the Rclone utility they had previously installed. The PKI instance continued to make repeated SSH connections attempts to transfer data to this external destination.

Darktrace’s Autonomous Response

In response to this activity, Darktrace’s Autonomous Response capability intervened, blocking unusual external connectivity to the C2 server via SSH, effectively stopping the exfiltration of data.

This activity was further investigated by Darktrace’s SOC analysts as part of the MDR service. The team elected to extend the autonomously applied actions to ensure the compromise remained contained until the customer could fully remediate the incident.

Continued reconissance

Around the same time, the threat actor continued to conduct network scans using the Nmap tool, operating from both a separate AWS domain controller instance and a newly joined device on the network. These actions were accompanied by further internal data gathering activities, with around 5 GB of data downloaded from an AWS file server.

The two devices involved in reconnaissance activities were investigated and actioned by Darktrace SOC analysts after additional Enhanced Monitoring alerts had triggered.

Lateral movement attempts via RDP connections

Unusual internal RDP connections to a likely AWS printer instance indicated that the threat actor was looking to strengthen their foothold within the environment and/or attempting to pivot to other devices, likely in response to being hindered by Autonomous Response actions.

This triggered multiple scanning, internal data transfer and unusual RDP alerts in Darktrace, as well as additional Autonomous Response actions to block the suspicious activity.

Suspicious outbound SSH communication to known threat infrastructure

Darktrace subsequently observed the AWS printer instance initiating SSH communication with a rare external endpoint associated with the web hosting and VPS provider Host Department (67.217.57[.]252), suggesting that the threat actor was attempting to exfiltrate data to an alternative endpoint after connections to the original destination had been blocked.

Further investigation using open-source intelligence (OSINT) revealed that this IP address had previously been observed in connection with SSH-based data exfiltration activity during an Akira ransomware intrusion [1].

Once again, connections to this IP were blocked by Darktrace’s Autonomous Response and subsequently these blocks were extended by Darktrace’s SOC team.

The above behavior generated multiple Enhanced Monitoring alerts that were investigated by Darktrace SOC analysts as part of the Managed Threat Detection service.

Enhanced Monitoring alerts investigated by SOC analysts as part of the Managed Detection and Response service.
Figure 5: Enhanced Monitoring alerts investigated by SOC analysts as part of the Managed Detection and Response service.

Final containment and collaborative response

Upon investigating the unusual scanning activity, outbound SSH connections, and internal data transfers, Darktrace analysts extended the Autonomous Response actions previously triggered on the compromised devices.

As the threat actor was leveraging these systems for data exfiltration, all outgoing traffic from the affected devices was blocked for an additional 24 hours to provide the customer’s security team with time to investigate and remediate the compromise.

Additional investigative support was provided by Darktrace analysts through the Security Operations Service, after the customer's opened of a ticket related to the unfolding incident.

Simplified timeline of the attack
Figure 8: Simplified timeline of the attack

Around the same time of the compromise in Case 2, Darktrace observed a similar incident on the cloud environment of a different customer.

Initial access

On this occasion, the threat actor appeared to have gained entry into the AWS-based Virtual Private Cloud (VPC) network via a SonicWall SMA 500v EC2 instance allowing inbound traffic on any port.

The instance received HTTPS connections from three rare Vultr VPS endpoints (i.e., 45.32.205[.]52, 207.246.74[.]166, 45.32.90[.]176).

Lateral movement and exfiltration

Around the same time, the EC2 instance started scanning the environment and attempted to pivot to other internal systems via RDP, notably a DC EC2 instance, which also started scanning the network, and another EC2 instance.  

The latter then proceeded to transfer more than 230 GB of data to the rare external GTHost VPS endpoint 23.150.248[.]189, while downloading hundreds of GBs of data over SMB from another EC2 instance.

Cyber AI Analyst incident generated following the unusual scanning and RDP connections from the initial compromised device.
Figure 7: Cyber AI Analyst incident generated following the unusual scanning and RDP connections from the initial compromised device.

The same behavior was replicated across multiple EC2 instances, whereby compromised instances uploaded data over internal RDP connections to other instances, which then started transferring data to the same GTHost VPS endpoint over port 5000, which is typically used for Universal Plug and Play (UPnP).

What Darktrace detected

Darktrace observed the threat actor uploading a total of 718 GB to the external endpoint, after which they detonated ransomware within the compromised VPC networks.

This activity generated nine Enhanced Monitoring alerts in Darktrace, focusing on the scanning and external data activity, with the earliest of those alerts triggering around one hour after the initial intrusion.

Darktrace’s Autonomous Response capability was not configured to act on these devices. Therefore, the malicious activity was not autonomously blocked and escalated to the point of ransomware detonation.

Conclusion

This blog examined three real-world compromises in customer cloud environments each illustrating different stages in the attack lifecycle.

The first case showcased a notable progression from a SaaS compromise to a full cloud intrusion, emphasizing the critical role of anomaly detection when legitimate credentials are abused.

The latter two incidents demonstrated that while early detection is vital, the ability to autonomously block malicious activity at machine speed is often the most effective way to contain threats before they escalate.

Together, these incidents underscore the need for continuous visibility, behavioral analysis, and machine-speed intervention across hybrid environments. Darktrace's AI-driven detection and Autonomous Response capabilities, combined with expert oversight from its Security Operations Center, give defenders the speed and clarity they need to contain threats and reduce operational disruption, before the situation spirals.

Credit to Alexandra Sentenac (Senior Cyber Analyst) and Dylan Evans (Security Research Lead)

References

[1] https://www.virustotal.com/gui/ip-address/67.217.57.252/community

Case 1

Darktrace / IDENTITY model alerts

IaaS / Compliance / Uncommon Azure External User Invite

SaaS / Resource / Repeated Unusual SaaS Resource Creation

IaaS / Compute / Azure Compute Resource Update

Cyber AI Analyst incidents

Possible Unsecured AzureActiveDirectory Resource

Possible Hijack of Office365 Account

Case 2

Darktrace / NETWORK model alerts

Compromise / SSH Beacon

Device / Multiple Lateral Movement Model Alerts

Device / Suspicious SMB Scanning Activity

Device / SMB Lateral Movement

Compliance / SSH to Rare External Destination

Device / Anomalous SMB Followed By Multiple Model Alerts

Device / Anonymous NTLM Logins

Anomalous Connection / SMB Enumeration

Device / New or Uncommon SMB Named Pipe Device / Network Scan

Device / Suspicious Network Scan Activity

Device / New Device with Attack Tools

Device / RDP Scan Device / Attack and Recon Tools

Compliance / High Priority Compliance Model Alert

Compliance / Outgoing NTLM Request from DC

Compromise / Large Number of Suspicious Successful Connections

Device / Large Number of Model Alerts

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Unusual Activity / Internal Data Transfer

Anomalous Connection / Unusual Internal Connections

Device / Anomalous RDP Followed By Multiple Model Alerts

Unusual Activity / Unusual External Activity

Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Unusual External Data to New Endpoint

Anomalous Connection / Multiple Connections to New External TCP Port

Darktrace / Autonomous Response model alerts

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / Manual / Quarantine Device

Antigena / MDR / MDR-Quarantined Device

Antigena / MDR / Model Alert on MDR-Actioned Device

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

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

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / Insider Threat / Antigena Internal Data Transfer Block

Cyber AI Analyst incidents

Possible Application Layer Reconnaissance Activity

Scanning of Multiple Devices

Unusual Repeated Connections

Unusual External Data Transfer

Case 3

Darktrace / NETWORK model alerts

Unusual Activity / Unusual Large Internal Transfer

Compliance / Incoming Remote Desktop

Unusual Activity / High Volume Server Data Transfer

Unusual Activity / Internal Data Transfer

Anomalous Connection / Unusual Internal Remote Desktop

Anomalous Connection / Unusual Incoming Data Volume

Anomalous Server Activity / Domain Controller Initiated to Client

Device / Large Number of Model Alerts

Anomalous Connection / Possible Flow Device Brute Force

Device / RDP Scan

Device / Suspicious Network Scan Activity

Device / Network Scan

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Connection / Download and Upload

Unusual Activity / Unusual External Data Transfer

Unusual Activity / High Volume Client Data Transfer

Unusual Activity / Unusual External Activity

Anomalous Connection / Uncommon 1 GiB Outbound

Device / Increased External Connectivity

Compromise / Large Number of Suspicious Successful Connections

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Low and Slow Exfiltration to IP

Unusual Activity / Enhanced Unusual External Data Transfer

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / Multiple Connections to New External UDP Port

Anomalous Connection / Possible Data Staging and External Upload

Unusual Activity / Unusual External Data to New Endpoint

Device / Large Number of Model Alerts from Critical Network Device

Compliance / External Windows Communications

Anomalous Connection / Unusual Internal Connections

Cyber AI Analyst incidents

Scanning of Multiple Devices

Extensive Unusual RDP Connections

MITRE ATT&CK mapping

(Technique name – Tactic ID)

Case 1

Defense Evasion - Modify Cloud Compute Infrastructure: Create Cloud Instance

Persistence – Account Manipulation

Case 2

Initial Access - External Remote Services

Execution - Inter-Process Communication

Persistence - External Remote Services

Discovery - System Network Connections Discovery

Discovery - Network Service Discovery

Discovery - Network Share Discovery

Lateral Movement - Remote Desktop Protocol

Lateral Movement - Remote Services: SMB/Windows Admin Shares

Collection - Data from Network Shared Drive

Command and Control - Protocol Tunneling

Exfiltration - Exfiltration Over Asymmetric Encrypted Non-C2 Protocol

Case 3

Initial Access - Exploit Public-Facing Application

Discovery - Remote System Discovery

Discovery - Network Service Discovery

Lateral Movement - Remote Services

Lateral Movement - Remote Desktop Protocol  

Collection - Data from Network Shared Drive

Collection - Data Staged: Remote Data Staging

Exfiltration - Exfiltration Over C2 Channel

Command and Control - Non-Standard Port

Command and Control – Web Service

Impact - Data Encrypted for Impact

List of IoCs

IoC         Type      Description + Probability

193.242.184[.]178 - IP Address - Possible Exfiltration Server  

45.32.205[.]52  - IP Address  - Possible C2 Infrastructure

45.32.90[.]176 - IP Address - Possible C2 Infrastructure

207.246.74[.]166 - IP Address - Likely C2 Infrastructure

67.217.57[.]252 - IP Address - Likely C2 Infrastructure

23.150.248[.]189 - IP Address - Possible Exfiltration Server

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About the author
Alexandra Sentenac
Cyber Analyst

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July 7, 2025

Top Eight Threats to SaaS Security and How to Combat Them

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The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1.  Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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