December 15, 2023

How Darktrace Halted A DarkGate in MS Teams

Discover how Darktrace thwarted DarkGate malware in Microsoft Teams. Stay informed on the latest cybersecurity measures and protect your business.

Written by

Natalia Sánchez Rocafort

Cyber Security 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

Natalia Sánchez Rocafort

Cyber Security Analyst

Dec 2023

Securing Microsoft Teams and SharePoint

Given the prevalence of the Microsoft Teams and Microsoft SharePoint platforms in the workplace in recent years, it is essential that organizations stay vigilant to the threat posed by applications vital to hybrid and remote work and prioritize the security and cyber hygiene of these services. For just as the use of these platforms has increased exponentially with the rise of remote and hybrid working, so too has the malicious use of them to deliver malware to unassuming users.

Researchers across the threat landscape have begun to observe these legitimate services being leveraged by malicious actors as an initial access method. Microsoft Teams can easily be exploited to send targeted phishing messages to individuals within an organization, while appearing legitimate and safe. Although the exact contents of these messages may vary, the messages frequently use social engineering techniques to lure users to click on a SharePoint link embedded into the message. Interacting with the malicious link will then download a payload [1].

Darktrace observed one such malicious attempt to use Microsoft Teams and SharePoint in September 2023, when a device was observed downloading DarkGate, a commercial trojan that is known to deploy other strains of malware, also referred to as a commodity loader [2], after clicking on SharePoint link. Fortunately for the customer, Darktrace’s suite of products was perfectly poised to identify the initial signs of suspicious activity and Darktrace RESPOND™ was able to immediately halt the advancement of the attack.

DarkGate Attack Overview

On September 8, 2023, Darktrace DETECT™ observed around 30 internal devices on a customer network making unusual SSL connections to an external SharePoint site which contained the name of a person, 'XXXXXXXX-my.sharepoint[.]com' (107.136[.]8, 13.107.138[.]8). The organization did not have any employees who went by this name and prior to this activity, no internal devices had been seen contacting the endpoint.

At first glance, this initial attack vector would have appeared subtle and seemingly trustworthy to users. Malicious actors likely sent various users a phishing message via Microsoft Teams that contained the spoofed SharePoint link to the personalized SharePoint link ''XXXXXXXX-my.sharepoint[.]com'.

Figure 1: Advanced Search query showing a sudden spike in connections to ''XXXXXXXX -my.sharepoint[.]com'.

Darktrace observed around 10 devices downloading approximately 1 MB of data during their connections to the Sharepoint endpoint. Darktrace DETECT observed some of the devices making subsequent HTTP GET requests to a range of anomalous URIs. The devices utilized multiple user-agents for these connections, including ‘curl’, a command line tool that allows individuals to request and transfer data from a specific URL. The connections were made to the IP 5.188.87[.]58, an endpoint that has been flagged as an indicator of compromise (IoC) for DarkGate malware by multiple open-source intelligence (OSINT) sources [3], commonly associated with HTTP GET requests:

GET request over port 2351 with the User-Agent header 'Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5)' and the target URI '/bfyxraav' to 5.188.87[.]58
GET request over port 2351 with the user-agent header 'curl' and the target URI '/' to 5.188.87[.]58
GET request over port 2351 with the user-agent header 'curl/8.0.1' and the target URI '/msibfyxraav' to 5.188.87[.]58

The HTTP GET requests made with the user-agent header 'curl' and the target URI '/' to 5.188.87[.]58 were responded to with a filename called 'Autoit3.exe'. The other requests received script files with names ending in '.au3, such as 'xkwtvq.au3', 'otxynh.au3', and 'dcthbq.au3'. DarkGate malware has been known to make use of legitimate AutoIt files, and typically runs multiple AutoIt scripts (‘.au3’) [4].

Following these unusual file downloads, the devices proceeded to make hundreds of HTTP POST requests to the target URI '/' using the user-agent header 'Mozilla/4.0 (compatible; Synapse)' to 5.188.87[.]58. The contents of these requests, along with the contents of the responses, appear to be heavily obfuscated.

Figure 2: Example of obfuscated response, as shown in a packet capture downloaded from Darktrace.

While Microsoft’s Safe Attachments and Safe Links settings were unable to detect this camouflaged malicious activity, Darktrace DETECT observed the unusual over-the-network connectivity that occurred. While Darktrace DETECT identified multiple internal devices engaging in this anomalous behavior throughout the course of the compromise, the activity observed on one device in particular best showcases the overall kill chain of this attack.

The device in question was observed using two different user agents (curl/8.0.1 and Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5)) when connecting to the endpoint 5.188.87[.]58 and target URI ‘/bfyxraav’. Additionally, Darktrace DETECT recognized that it was unusual for this device to be making these HTTP connections via destination port 2351.

As a result, Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the suspicious activity and was able to connect the unusual external connections together, viewing them as one beaconing incident as opposed to isolated series of connections.

Figure 3: Cyber AI Analyst investigation summarizing the unusual repeated connections made to 5.188.87[.]58 via destination port 2351.

Darktrace then observed the device downloading the ‘Autoit3.exe’ file. Darktrace RESPOND took swift mitigative action by blocking similar connections to this endpoint, preventing the device from downloading any additional suspicious files.

Figure 4: Suspicious ‘Autoit3.exe’ downloaded by the source device from the malicious external endpoint.

Just one millisecond later, Darktrace observed the device making suspicious HTTP GET requests to URIs including ‘/msibfyxraav’. Darktrace recognized that the device had carried out several suspicious actions within a relatively short period of time, breaching multiple DETECT models, indicating that it may have been compromised. As a result, RESPOND took action against the offending device by preventing it from communicating externally [blocking all outbound connections] for a period of one hour, allowing the customer’s security team precious time to address the issue.

It should be noted that, at this point, had the customer subscribed to Darktrace’s Proactive Threat Notification (PTN) service, the Darktrace Security Operations Center (SOC) would have investigated these incidents in greater detail, and likely would have sent a notification directly to the customer to inform them of the suspicious activity.

Additionally, AI Analyst collated various distinct events and suggested that these stages were linked as part of an attack. This type of augmented understanding of events calculated at machine speed is extremely valuable since it likely would have taken a human analyst hours to link all the facets of the incident together.

Figure 5: AI Analyst investigation showcasing the use of the ‘curl’ user agent to connect to the target URI ‘/msibfyxraav’.

Figure 6: Darktrace RESPOND moved to mitigate any following connections by blocking all outgoing traffic for 1 hour.

Following this, an automated investigation was launched by Microsoft Defender for Endpoint. Darktrace is designed to coordinate with multiple third-party security tools, allowing for information on ongoing incidents to be seamlessly exchanged between Darktrace and other security tools. In this instance, Microsoft Defender identified a ‘low severity’ incident on the device, this automatically triggered a corresponding alert within DETECT, presented on the Darktrace Threat Visuallizer.

The described activity occurred within milliseconds. At each step of the attack, Darktrace RESPOND took action either by enforcing expected patterns of life [normality] on the affected device, blocking connections to suspicious endpoints for a specified amount of time, and/or blocking all outgoing traffic from the device. All the relevant activity was detected and promptly stopped for this device, and other compromised devices, thus containing the compromise and providing the security team invaluable remediation time.

Figure 7: Overview of the compromise activity, all of which took place within a matter of miliseconds.

Darktrace identified similar activity on other devices in this customer’s network, as well as across Darktrace’s fleet around the same time in early September.

On a different customer environment, Darktrace DETECT observed more than 25 ‘.au3’ files being downloaded; this activity can be seen in Figure 9.

Figure 8: High volume of file downloads following GET request and 'curl' commands.

Figure 9 provides more details of this activity, including the source and destination IP addresses (5.188.87[.]58), the destination port, the HTTP method used and the MIME/content-type of the file

Figure 9: Additional information of the anomalous connections.

A compromised server in another customer deployment was seen establishing unusual connections to the external IP address 80.66.88[.]145 – an endpoint that has been associated with DarkGate by OSINT sources [5]. This activity was identified by Darktrace/DETECT as a new connection for the device via an unusual destination port, 2840. As the device in question was a critical server, Darktrace DETECT treated it with suspicion and generated an ‘Anomalous External Activity from Critical Network Device’ model breach.

Figure 10: Model breach and model breach event log for suspicious connections to additional endpoint.

Conclusion

While Microsoft Teams and SharePoint are extremely prominent tools that are essential to the business operations of many organizations, they can also be used to compromise via living off the land, even at initial intrusion. Any Microsoft Teams user within a corporate setting could be targeted by a malicious actor, as such SharePoint links from unknown senders should always be treated with caution and should not automatically be considered as secure or legitimate, even when operating within legitimate Microsoft infrastructure.

Malicious actors can leverage these commonly used platforms as a means to carry out their cyber-attacks, therefore organizations must take appropriate measures to protect and secure their digital environments. As demonstrated here, threat actors can attempt to deploy malware, like DarkGate, by targeting users with spoofed Microsoft Teams messages. By masking malicious links as legitimate SharePoint links, these attempts can easily convince targets and bypass traditional security tools and even Microsoft’s own Safe Links and Safe Attachments security capabilities.

When the chain of events of an attack escalates within milliseconds, organizations must rely on AI-driven tools that can quickly identify and automatically respond to suspicious events without latency. As such, the value of Darktrace DETECT and Darktrace RESPOND cannot be overstated. Given the efficacy and efficiency of Darktrace’s detection and autonomous response capabilities, a more severe network compromise in the form of the DarkGate commodity loader was ultimately averted.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Zoe Tilsiter.

Appendices

Darktrace DETECT Model Detections

[Model Breach: Device / Initial Breach Chain Compromise 100% –– Breach URI: /#modelbreach/114039 ] (Enhanced Monitoring)· [Model Breach: Device / Initial Breach Chain Compromise 100% –– Breach URI: /#modelbreach/114124 ] (Enhanced Monitoring)
[Model Breach: Device / New User Agent and New IP 62% –– Breach URI: /#modelbreach/114030 ]
[Model Breach: Anomalous Connection / Application Protocol on Uncommon Port 46% –– Breach URI: /#modelbreach/114031 ]
[Model Breach: Anomalous Connection / New User Agent to IP Without Hostname 62% –– Breach URI: /#modelbreach/114032 ]
[Model Breach: Device / New User Agent 32% –– Breach URI: /#modelbreach/114035 ]
[Model Breach: Device / Three Or More New User Agents 31% –– Breach URI: /#modelbreach/114036 ]
[Model Breach: Anomalous Server Activity / Anomalous External Activity from Critical Network Device 62% –– Breach URI: /#modelbreach/612173 ]
[Model Breach: Anomalous File / EXE from Rare External Location 61% –– Breach URI: /#modelbreach/114037 ]
[Model Breach: Anomalous Connection / Multiple Connections to New External TCP Port 61% –– Breach URI: /#modelbreach/114042 ]
[Model Breach: Security Integration / Integration Ransomware Detected 100% –– Breach URI: /#modelbreach/114049 ]
[Model Breach: Compromise / Beaconing Activity To External Rare 62% –– Breach URI: /#modelbreach/114059 ]
[Model Breach: Compromise / HTTP Beaconing to New Endpoint 30% –– Breach URI: /#modelbreach/114067 ]
[Model Breach: Security Integration / C2 Activity and Integration Detection 100% –– Breach URI: /#modelbreach/114069 ]
[Model Breach: Anomalous File / EXE from Rare External Location 55% –– Breach URI: /#modelbreach/114077 ]
[Model Breach: Compromise / High Volume of Connections with Beacon Score 66% –– Breach URI: /#modelbreach/114260 ]
[Model Breach: Security Integration / Low Severity Integration Detection 59% –– Breach URI: /#modelbreach/114293 ]
[Model Breach: Security Integration / Low Severity Integration Detection 33% –– Breach URI: /#modelbreach/114462 ]
[Model Breach: Security Integration / Integration Ransomware Detected 100% –– Breach URI: /#modelbreach/114109 ]· [Model Breach: Device / Three Or More New User Agents 31% –– Breach URI: /#modelbreach/114118 ]· [Model Breach: Anomalous Connection / Application Protocol on Uncommon Port 46% –– Breach URI: /#modelbreach/114113 ] · [Model Breach: Anomalous Connection / New User Agent to IP Without Hostname 62% –– Breach URI: /#modelbreach/114114 ]· [Model Breach: Device / New User Agent 32% –– Breach URI: /#modelbreach/114117 ]· [Model Breach: Anomalous File / EXE from Rare External Location 61% –– Breach URI: /#modelbreach/114122 ]· [Model Breach: Security Integration / Low Severity Integration Detection 54% –– Breach URI: /#modelbreach/114310 ]
[Model Breach: Security Integration / Integration Ransomware Detected 65% –– Breach URI: /#modelbreach/114662 ]Darktrace/Respond Model Breaches
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 61% –– Breach URI: /#modelbreach/114033 ]
[Model Breach: Antigena / Network::External Threat::Antigena File then New Outbound Block 100% –– Breach URI: /#modelbreach/114038 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block 100% –– Breach URI: /#modelbreach/114040 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block 87% –– Breach URI: /#modelbreach/114041 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach 87% –– Breach URI: /#modelbreach/114043 ]
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 100% –– Breach URI: /#modelbreach/114052 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Security Integration and Network Activity Block 87% –– Breach URI: /#modelbreach/114070 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 87% –– Breach URI: /#modelbreach/114071 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious Activity Block 87% –– Breach URI: /#modelbreach/114072 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 53% –– Breach URI: /#modelbreach/114079 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 64% –– Breach URI: /#modelbreach/114539 ]
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 66% –– Breach URI: /#modelbreach/114667 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious Activity Block 79% –– Breach URI: /#modelbreach/114684 ]·
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 100% –– Breach URI: /#modelbreach/114110 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block 87% –– Breach URI: /#modelbreach/114111 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach 87% –– Breach URI: /#modelbreach/114115 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 87% –– Breach URI: /#modelbreach/114116 ]·
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 61% –– Breach URI: /#modelbreach/114121 ]·
[Model Breach: Antigena / Network::External Threat::Antigena File then New Outbound Block 100% –– Breach URI: /#modelbreach/114123 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block 100% –– Breach URI: /#modelbreach/114125 ]

List of IoCs

IoC - Type - Description + Confidence

5.188.87[.]58 - IP address - C2 endpoint

80.66.88[.]145 - IP address - C2 endpoint

/bfyxraav - URI - Possible C2 endpoint URI

/msibfyxraav - URI - Possible C2 endpoint URI

Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5) - User agent - Probable user agent leveraged

curl - User agent - Probable user agent leveraged

curl/8.0.1 - User agent - Probable user agent leveraged

Mozilla/4.0 (compatible; Synapse) - User agent - Probable user agent leveraged

Autoit3.exe - Filename - Exe file

CvUYLoTv.au3

eDVeqcCe.au3

FeLlcFRS.au3

FTEZlGhe.au3

HOrzcEWV.au3

rKlArXHH.au3

SjadeWUz.au3

ZgOLxJQy.au3

zSrxhagw.au3

ALOXitYE.au3

DKRcfZfV.au3

gQZVKzek.au3

JZrvmJXK.au3

kLECCtMw.au3

LEXCjXKl.au3

luqWdAzF.au3

mUBNrGpv.au3

OoCdHeJT.au3

PcEJXfIl.au3

ssElzrDV.au3

TcBwRRnp.au3

TFvAUIgu.au3

xkwtvq.au3

otxynh.au3

dcthbq.au3 - Filenames - Possible exe files delivered in response to curl/8.0.1 GET requests with Target URI '/msibfyxraav

f3a0a85fe2ea4a00b3710ef4833b07a5d766702b263fda88101e0cb804d8c699 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

afa3feea5964846cd436b978faa7d31938e666288ffaa75d6ba75bfe6c12bf61 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

63aeac3b007436fa8b7ea25298362330423b80a4cb9269fd2c3e6ab1b1289208 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

ab6704e836a51555ec32d1ff009a79692fa2d11205f9b4962121bda88ba55486 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

References

1. https://www.truesec.com/hub/blog/darkgate-loader-delivered-via-teams

2. https://feedit.cz/wp-content/uploads/2023/03/YiR2022_onepager_ransomware_loaders.pdf

‍3. https://www.virustotal.com/gui/ip-address/5.188.87[.]58

‍4. https://www.forescout.com/resources/darkgate-loader-malspam-campaign/

‍5. https://otx.alienvault.com/indicator/ip/80.66.88[.]145

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Inside the SOC

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Product Marketing Manager, OT Security & Compliance

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Blog

OT

November 20, 2025

Managing OT Remote Access with Zero Trust Control & AI Driven Detection

managing OT remote access with zero trust control and ai driven detection

The shift toward IT-OT convergence

Recently, industrial environments have become more connected and dependent on external collaboration. As a result, truly air-gapped OT systems have become less of a reality, especially when working with OEM-managed assets, legacy equipment requiring remote diagnostics, or third-party integrators who routinely connect in.

This convergence, whether it’s driven by digital transformation mandates or operational efficiency goals, are making OT environments more connected, more automated, and more intertwined with IT systems. While this convergence opens new possibilities, it also exposes the environment to risks that traditional OT architectures were never designed to withstand.

The modernization gap and why visibility alone isn’t enough

The push toward modernization has introduced new technology into industrial environments, creating convergence between IT and OT environments, and resulting in a lack of visibility. However, regaining that visibility is just a starting point. Visibility only tells you what is connected, not how access should be governed. And this is where the divide between IT and OT becomes unavoidable.

Security strategies that work well in IT often fall short in OT, where even small missteps can lead to environmental risk, safety incidents, or costly disruptions. Add in mounting regulatory pressure to enforce secure access, enforce segmentation, and demonstrate accountability, and it becomes clear: visibility alone is no longer sufficient. What industrial environments need now is precision. They need control. And they need to implement both without interrupting operations. All this requires identity-based access controls, real-time session oversight, and continuous behavioral detection.

The risk of unmonitored remote access

This risk becomes most evident during critical moments, such as when an OEM needs urgent access to troubleshoot a malfunctioning asset.

Under that time pressure, access is often provisioned quickly with minimal verification, bypassing established processes. Once inside, there’s little to no real-time oversight of user actions whether they’re executing commands, changing configurations, or moving laterally across the network. These actions typically go unlogged or unnoticed until something breaks. At that point, teams are stuck piecing together fragmented logs or post-incident forensics, with no clear line of accountability.

In environments where uptime is critical and safety is non-negotiable, this level of uncertainty simply isn’t sustainable.

The visibility gap: Who’s doing what, and when?

The fundamental issue we encounter is the disconnect between who has access and what they are doing with it.

Traditional access management tools may validate credentials and restrict entry points, but they rarely provide real-time visibility into in-session activity. Even fewer can distinguish between expected vendor behavior and subtle signs of compromise, misuse or misconfiguration.

As a result, OT and security teams are often left blind to the most critical part of the puzzle, intent and behavior.

Closing the gaps with zero trust controls and AI‑driven detection

Managing remote access in OT is no longer just about granting a connection, it’s about enforcing strict access parameters while continuously monitoring for abnormal behavior. This requires a two-pronged approach: precision access control, and intelligent, real-time detection.

Zero Trust access controls provide the foundation. By enforcing identity-based, just-in-time permissions, OT environments can ensure that vendors and remote users only access the systems they’re explicitly authorized to interact with, and only for the time they need. These controls should be granular enough to limit access down to specific devices, commands, or functions. By applying these principles consistently across the Purdue Model, organizations can eliminate reliance on catch-all VPN tunnels, jump servers, and brittle firewall exceptions that expose the environment to excess risk.

Access control is only one part of the equation

Darktrace / OT complements zero trust controls with continuous, AI-driven behavioral detection. Rather than relying on static rules or pre-defined signatures, Darktrace uses Self-Learning AI to build a live, evolving understanding of what’s “normal” in the environment, across every device, protocol, and user. This enables real-time detection of subtle misconfigurations, credential misuse, or lateral movement as they happen, not after the fact.

By correlating user identity and session activity with behavioral analytics, Darktrace gives organizations the full picture: who accessed which system, what actions they performed, how those actions compared to historical norms, and whether any deviations occurred. It eliminates guesswork around remote access sessions and replaces it with clear, contextual insight.

Importantly, Darktrace distinguishes between operational noise and true cyber-relevant anomalies. Unlike other tools that lump everything, from CVE alerts to routine activity, into a single stream, Darktrace separates legitimate remote access behavior from potential misuse or abuse. This means organizations can both audit access from a compliance standpoint and be confident that if a session is ever exploited, the misuse will be surfaced as a high-fidelity, cyber-relevant alert. This approach serves as a compensating control, ensuring that even if access is overextended or misused, the behavior is still visible and actionable.

If a session deviates from learned baselines, such as an unusual command sequence, new lateral movement path, or activity outside of scheduled hours, Darktrace can flag it immediately. These insights can be used to trigger manual investigation or automated enforcement actions, such as access revocation or session isolation, depending on policy.

This layered approach enables real-time decision-making, supports uninterrupted operations, and delivers complete accountability for all remote activity, without slowing down critical work or disrupting industrial workflows.

Where Zero Trust Access Meets AI‑Driven Oversight:

Granular Access Enforcement: Role-based, just-in-time access that aligns with Zero Trust principles and meets compliance expectations. ‍
Context-Enriched Threat Detection: Self-Learning AI detects anomalous OT behavior in real time and ties threats to access events and user activity. ‍
Automated Session Oversight: Behavioral anomalies can trigger alerting or automated controls, reducing time-to-contain while preserving uptime. ‍
Full Visibility Across Purdue Layers: Correlated data connects remote access events with device-level behavior, spanning IT and OT layers. ‍
Scalable, Passive Monitoring: Passive behavioral learning enables coverage across legacy systems and air-gapped environments, no signatures, agents, or intrusive scans required.

Complete security without compromise

We no longer have to choose between operational agility and security control, or between visibility and simplicity. A Zero Trust approach, reinforced by real-time AI detection, enables secure remote access that is both permission-aware and behavior-aware, tailored to the realities of industrial operations and scalable across diverse environments.

Because when it comes to protecting critical infrastructure, access without detection is a risk and detection without access control is incomplete.

About the author

Pallavi Singh

Product Marketing Manager, OT Security & Compliance

Blog

November 20, 2025

Securing Generative AI: Managing Risk in Amazon Bedrock with Darktrace / CLOUD

Security risks and challenges of generative AI in the enterprise

Generative AI and managed foundation model platforms like Amazon Bedrock are transforming how organizations build and deploy intelligent applications. From chatbots to summarization tools, Bedrock enables rapid agent development by connecting foundation models to enterprise data and services. But with this flexibility comes a new set of security challenges, especially around visibility, access control, and unintended data exposure.

As organizations move quickly to operationalize generative AI, traditional security controls are struggling to keep up. Bedrock’s multi-layered architecture, spanning agents, models, guardrails, and underlying AWS services, creates new blind spots that standard posture management tools weren’t designed to handle. Visibility gaps make it difficult to know which datasets agents can access, or how model outputs might expose sensitive information. Meanwhile, developers often move faster than security teams can review IAM permissions or validate guardrails, leading to misconfigurations that expand risk. In shared-responsibility environments like AWS, this complexity can blur the lines of ownership, making it critical for security teams to have continuous, automated insight into how AI systems interact with enterprise data.

Darktrace / CLOUD provides comprehensive visibility and posture management for Bedrock environments, automatically detecting and proactively scanning agents and knowledge bases, helping teams secure their AI infrastructure without slowing down expansion and innovation.

A real-world scenario: When access goes too far

Consider a scenario where an organization deploys a Bedrock agent to help internal staff quickly answer business questions using company knowledge. The agent was connected to a knowledge base pointing at documents stored in Amazon S3 and given access to internal services via APIs.

To get the system running quickly, developers assigned the agent a broad execution role. This role granted access to multiple S3 buckets, including one containing sensitive customer records. The over-permissioning wasn’t malicious; it stemmed from the complexity of IAM policy creation and the difficulty of identifying which buckets held sensitive data.

The team assumed the agent would only use the intended documents. However, they did not fully consider how employees might interact with the agent or how it might act on the data it processed.

When an employee asked a routine question about quarterly customer activity, the agent surfaced insights that included regulated data, revealing it to someone without the appropriate access.

This wasn’t a case of prompt injection or model manipulation. The agent simply followed instructions and used the resources it was allowed to access. The exposure was valid under IAM policy, but entirely unintended.

How Darktrace / CLOUD prevents these risks

Darktrace / CLOUD helps organizations avoid scenarios like unintended data exposure by providing layered visibility and intelligent analysis across Bedrock and SageMaker environments. Here’s how each capability works in practice:

Configuration-level visibility

Bedrock deployments often involve multiple components: agents, guardrails, and foundation models, each with its own configuration. Darktrace / CLOUD indexes these configurations so teams can:

Inspect deployed agents and confirm they are connected only to approved data sources.
Track evaluation job setups and their links to Amazon S3 datasets, uncovering hidden data flows that could expose sensitive information.
Maintain full awareness of all AI components, reducing the chance of overlooked assets introducing risk.

By unifying configuration data across Bedrock, SageMaker, and other AWS services, Darktrace / CLOUD provides a single source of truth for AI asset visibility. Teams can instantly see how each component is configured and whether it aligns with corporate security policies. This eliminates guesswork, accelerates audits, and helps prevent misaligned settings from creating data exposure risks.

Agents for bedrock relationship views. — Figure 1: Agents for bedrock relationship views

Architectural awareness

Complex AI environments can make it difficult to understand how components interact. Darktrace / CLOUD generates real-time architectural diagrams that:

Visualize relationships between agents, models, and datasets.

Highlight unintended data access paths or risk propagation across interconnected services.

This clarity helps security teams spot vulnerabilities before they lead to exposure. By surfacing these relationships dynamically, Darktrace / CLOUD enables proactive risk management, helping teams identify architectural drift, redundant data connections, or unmonitored agents before attackers or accidental misuse can exploit them. This reduces investigation time and strengthens compliance confidence across AI workloads.

Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping

Access & privilege analysis

IAM permissions apply to every AWS service, including Bedrock. When Bedrock agents assume IAM roles that were broadly defined for other workloads, they often inherit excessive privileges. Without strict least-privilege controls, the agent may have access to far more data and services than required, creating avoidable security exposure. Darktrace / CLOUD:

Reviews execution roles and user permissions to identify excessive privileges.
Flags anomalies that could enable privilege escalation or unauthorized API actions.

This ensures agents operate within the principle of least privilege, reducing attack surface. Beyond flagging risky roles, Darktrace / CLOUD continuously learns normal patterns of access to identify when permissions are abused or expanded in real time. Security teams gain context into why an action is anomalous and how it could affect connected assets, allowing them to take targeted remediation steps that preserve productivity while minimizing exposure.

Misconfiguration detection

Misconfigurations are a leading cause of cloud security incidents. Darktrace / CLOUD automatically detects:

Publicly accessible S3 buckets that may contain sensitive training data.
Missing guardrails in Bedrock deployments, which can allow inappropriate or sensitive outputs.
Other issues such as lack of encryption, direct internet access, and root access to models.

By surfacing these risks early, teams can remediate before they become exploitable. Darktrace / CLOUD turns what would otherwise be manual reviews into automated, continuous checks, reducing time to discovery and preventing small oversights from escalating into full-scale incidents. This automated assurance allows organizations to innovate confidently while keeping their AI systems compliant and secure by design.

Figure 3: Configuration data for Anthropic foundation model

Behavioral anomaly detection

Even with correct configurations, behavior can signal emerging threats. Using AWS CloudTrail, Darktrace / CLOUD:

Monitors for unusual data access patterns, such as agents querying unexpected datasets.
Detects anomalous training job invocations that could indicate attempts to pollute models.

This real-time behavioral insight helps organizations respond quickly to suspicious activity. Because it learns the “normal” behavior of each Bedrock component over time, Darktrace / CLOUD can detect subtle shifts that indicate emerging risks, before formal indicators of compromise appear. The result is faster detection, reduced investigation effort, and continuous assurance that AI-driven workloads behave as intended.

Conclusion

Generative AI introduces transformative capabilities but also complex risks that evolve alongside innovation. The flexibility of services like Amazon Bedrock enables new efficiencies and insights, yet even legitimate use can inadvertently expose sensitive data or bypass security controls. As organizations embrace AI at scale, the ability to monitor and secure these environments holistically, without slowing development, is becoming essential.

By combining deep configuration visibility, architectural insight, privilege and behavior analysis, and real-time threat detection, Darktrace gives security teams continuous assurance across AI tools like Bedrock and SageMaker. Organizations can innovate with confidence, knowing their AI systems are governed by adaptive, intelligent protection.

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About the author

Adam Stevens

Senior Director of Product, Cloud | Darktrace

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