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November 20, 2023

Understanding and Mitigating Sectop RAT

Understand the risks posed by the Sectop remote access Trojan and how Darktrace implements strategies to enhance cybersecurity defenses.
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 Torres
Cyber Analyst
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20
Nov 2023

Introduction

As malicious actors across the threat landscape continue to look for new ways to gain unauthorized access to target networks, it is unsurprising to see Remote Access Trojans (RATs) leveraged more and more. These RATs are downloaded discretely without the target’s knowledge, typically through seemingly legitimate software downloads, and are designed to gain highly privileged network credentials, ultimately allowing attackers to have remote control over compromised devices. [1]

SectopRAT is one pertinent example of a RAT known to adopt a number of stealth functions in order to gather and exfiltrate sensitive data from its targets including passwords, cookies, autofill and history data stores in browsers, as well as cryptocurrency wallet details and system hardware information. [2]

In early 2023, Darktrace identified a resurgence of the SectopRAT across customer environments, primarily targeting educational industries located in the United States (US), Europe, the Middle East and Africa (EMEA) and Asia-Pacific (APAC) regions. Darktrace DETECT™ was able to successfully identify suspicious activity related to SectopRAT at the network level, as well as any indicators of post-compromise on customer environments that did not have Darktrace RESPOND™ in place to take autonomous preventative action.

What is SectopRAT?

First discovered in early 2019, the SectopRAT is a .NET RAT that contains information stealing capabilities. It is also known under the alias ‘ArechClient2’, and is commonly distributed through drive-by downloads of illegitimate software and utilizes malvertising, including via Google Ads, to increase the chances of it being downloaded.

The malware’s code was updated at the beginning of 2021, which led to refined and newly implemented features, including command and control (C2) communication encryption with Advanced Encryption Stanard 256 (AES256) and additional commands. SectopRAT also has a function called "BrowserLogging", ultimately sending any actions it conducts on web browsers to its C2 infrastructure. When the RAT is executed, it then connects to a Pastebin associated hostname to retrieve C2 information; the requested file reaches out to get the public IP address of the infected device. To receive commands, it connects to its C2 server primarily on port 15647, although other ports have been highlighted by open source intelligence (OSINT), which include 15678, 15649, 228 and 80. Ultimately, sensitive data data gathered from target networks is then exfiltrated to the attacker’s C2 infrastructure, typically in a JSON file [3].

Darktrace Coverage

During autonomous investigations into affected customer networks, Darktrace DETECT was able to identify SSL connections to the endpoint pastebin[.]com over port 443, followed by failed connections to one of the IPs and ports (i.e., 15647, 15648, 15649) associated with SectopRAT. This resulted in the devices breaching the ‘Compliance/Pastebin and Anomalous Connection/Multiple Failed Connections to Rare Endpoint’ models, respectively.

In some instances, Darktrace observed a higher number of attempted connections that resulted in the additional breach of the model ‘Compromise / Large Number of Suspicious Failed Connections’.

Over a period of three months, Darktrace investigated multiple instances of SectopRAT infections across multiple clients, highlighting indicators of compromise (IoCs) through related endpoints.Looking specififically at one customer’s activity which centred on January 25, 2023, one device was observed initially making suspicious connections to a Pastebin endpoint, 104.20.67[.]143, likely in an attempt to receive C2 information.

Darktrace DETECT recognized this activity as suspicious, causing the 'Compliance / Pastebin' DETECT models to breach. In response to this detection, Darktrace RESPOND took swift action against the Pastebin connections by blocking them and preventing the device from carrying out further connections with Pastebin endpoints. Darktrace RESPOND actions related to blocking Pastebin connections were commonly observed on this device throughout the course of the attack and likely represented threat actors attempting to exfiltrate sensitive data outside the network.

Darktrace UI image
Figure 1: Model breach event log highlighting the Darktrace DETECT model breach ‘Compliance / Pastebin’.

Around the same time, Darktrace observed the device making a large number of failed connections to an unusual exernal location in the Netherlands, 5.75.147[.]135, via port 15647. Darktrace recognized that this endpoint had never previously been observed on the customer’s network and that the frequency of the failed connections could be indicative of beaconing activity. Subsequent investigation into the endpoint using OSINT indicated it had links to malware, though Darktrace’s successful detection did not need to rely on this intelligence.

Darktrace model breach event log
Figure 2: Model breach event log highlighting the multiple failed connectiosn to the suspicious IP address, 5.75.147[.]135 on January 25, 2023, causing the Darktrace DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach.

After these initial set of breaches on January 25, the same device was observed engaging in further external connectivity roughly a month later on February 27, including additional failed connections to the IP 167.235.134[.]14 over port 15647. Once more, multiple OSINT sources revealed that this endpoint was indeed a malicious C2 endpoint.

Darktrace model breach event log 2
Figure 3: Model breach event log highlighting the multiple failed connectiosn to the suspicious IP address, 167.235.134[.]14 on February 27, 2023, causing the Darktrace DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach.

While the initial Darktrace coverage up to this point has highlighted the attempted C2 communication and how DETECT was able to alert on the suspicious activity, Pastebin activity was commonly observed throughout the course of this attack. As a result, when enabled in autonomous response mode, Darktrace RESPOND was able to take swift mitigative action by blocking all connections to Pastebin associated hostnames and IP addresses. These interventions by RESPOND ultimately prevented malicious actors from stealing sensitive data from Darktrace customers.

Darktrace RESPOND action list
Figure 4: A total of nine Darktrace RESPOND actions were applied against suspicious Pastebin activity during the course of the attack.

In another similar case investigated by the Darktrace, multiple devices were observed engaging in external connectivity to another malicious endpoint,  88.218.170[.]169 (AS207651 Hosting technology LTD) on port 15647.  On April 17, 2023, at 22:35:24 UTC, the breach device started making connections; of the 34 attempts, one connection was successful – this connection lasted 8 minutes and 49 seconds. Darktrace DETECT’s Self-Learning AI understood that these connections represented a deviation from the device’s usual pattern of behavior and alerted on the activity with the ‘Multiple Connections to new External TCP Port’ model.

Darktrace model breach event log
Figure 5: Model breach event log highlighting the affected device successfully connecting to the suspicious endpoint, 88.218.170[.]169.
Darktrace advanced search query
Figure 6: Advanced Search query highlighting the one successful connection to the endpoint 88.218.170[.]169 out of the 34 attempted connections.

A few days later, on April 20, 2023, at 12:33:59 (UTC) the source device connected to a Pastebin endpoint, 172.67.34[.]170 on port 443 using the SSL protocol, that had never previously be seen on the network. According to Advanced Search data, the first SSL connection lasted over two hours. In total, the device made 9 connections to pastebin[.]com and downloaded 85 KB of data from it.

Darktrace UI highlighting connections
Figure 7: Screenshot of the Darktrace UI highlighting the affected device making multiple connections to Pastebin and downloading 85 KB of data.

Within the same minute, Darktrace detected the device beginning to make a large number of failed connections to another suspicious endpoints, 34.107.84[.]7 (AS396982 GOOGLE-CLOUD-PLATFORM) via port 15647. In total the affected device was observed initiating 1,021 connections to this malicious endpoint, all occurring over the same port and resulting the failed attempts.

Darktrace advanced search query 2
Figure 8: Advanced Search query highlighting the affected device making over one thousand connections to the suspicious endpoint 34.107.84[.]7, all of which failed.

Conclusion

Ultimately, thanks to its Self-Learning AI and anomaly-based approach to threat detection, Darktrace was able to preemptively identify any suspicious activity relating to SectopRAT at the network level, as well as post-compromise activity, and bring it to the immediate attention of customer security teams.

In addition to the successful and timely detection of SectopRAT activity, when enabled in autonomous response mode Darktrace RESPOND was able to shut down suspicious connections to endpoints used by threat actors as malicious infrastructure, thus preventing successful C2 communication and potential data exfiltration.

In the face of a Remote Access Trojan, like SectopRAT, designed to steal sensitive corporate and personal information, the Darktrace suite of products is uniquely placed to offer organizations full visibility over any emerging activity on their networks and respond to it without latency, safeguarding their digital estate whilst causing minimal disruption to business operations.

Credit to Justin Torres, Cyber Analyst, Brianna Leddy, Director of Analysis

Appendices

Darktrace Model Detection:

  • Compliance / Pastebin
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Large Number of Suspicious Failed Connections
  • Anomalous Connection / Multiple Connections to New External TCP Port

List of IoCs

IoC - Type - Description + Confidence

5.75.147[.]135 - IP - SectopRAT C2 Endpoint

5.75.149[.]1 - IP - SectopRAT C2 Endpoint

34.27.150[.]38 - IP - SectopRAT C2 Endpoint

34.89.247[.]212 - IP - SectopRAT C2 Endpoint

34.107.84[.]7 - IP - SectopRAT C2 Endpoint

34.141.16[.]89 - IP - SectopRAT C2 Endpoint

34.159.180[.]55 - IP - SectopRAT C2 Endpoint

35.198.132[.]51 - IP - SectopRAT C2 Endpoint

35.226.102[.]12 - IP - SectopRAT C2 Endpoint

35.234.79[.]173 - IP - SectopRAT C2 Endpoint

35.234.159[.]213 - IP - SectopRAT C2 Endpoint

35.242.150[.]95 - IP - SectopRAT C2 Endpoint

88.218.170[.]169 - IP - SectopRAT C2 Endpoint

162.55.188[.]246 - IP - SectopRAT C2 Endpoint

167.235.134[.]14 - IP - SectopRAT C2 Endpoint

MITRE ATT&CK Mapping

Model: Compliance / Pastebin

ID: T1537

Tactic: EXFILTRATION

Technique Name: Transfer Data to Cloud Account

Model: Anomalous Connection / Multiple Failed Connections to Rare Endpoint

ID: T1090.002

Sub technique of: T1090

Tactic: COMMAND AND CONTROL

Technique Name: External Proxy

ID: T1095

Tactic: COMMAND AND CONTROL

Technique Name: Non-Application Layer Protocol

ID: T1571

Tactic: COMMAND AND CONTROL

Technique Name: Non-Standard Port

Model: Compromise / Large Number of Suspicious Failed Connections

ID: T1571

Tactic: COMMAND AND CONTROL

Technique Name: Non-Standard Port

ID: T1583.006

Sub technique of: T1583

Tactic: RESOURCE DEVELOPMENT

Technique Name: Web Services

Model: Anomalous Connection / Multiple Connections to New External TCP Port

ID: T1095        

Tactic: COMMAND AND CONTROL    

Technique Name: Non-Application Layer Protocol

ID: T1571

Tactic: COMMAND AND CONTROL    

Technique Name: Non-Standard Port

References

1.     https://www.techtarget.com/searchsecurity/definition/RAT-remote-access-Trojan

2.     https://malpedia.caad.fkie.fraunhofer.de/details/win.sectop_rat

3.     https://threatfox.abuse.ch/browse/malware/win.sectop_rat

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 Torres
Cyber Analyst

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August 5, 2026

Testing a Prompt injection Attack Against an Enterprise AI Agent

prompt injectionDefault blog imageDefault blog image

Key takeaways

  • Darktrace successfully detected and quarantined a prompt injection email before it could be processed by an enterprise AI agent.  
  • Prompt injection attacks increasingly rely on natural language rather than traditional malware, making behavioral analysis an important complement to signature-based detection.  
  • Organizations deploying AI agents should combine model guardrails with behavioral monitoring to reduce the risk of malicious instructions reaching enterprise systems.

How behavioral detection helps stop prompt injection attacks

A Darktrace customer running a Gemini AI agent in Google Cloud asked us two simple questions:

“If my agent can read inbound emails and access internal data, what stops an attacker from hiding malicious instructions in the message? Couldn’t the agent be tricked into deleting or exfiltrating sensitive data?”

The scenario centers on an indirect prompt injection attack, where malicious instructions are hidden inside content that an AI model later interprets as trusted input. The same weakness was exposed by  EchoLeak (CVE-2025-32711), a zero-click Microsoft 365 Copilot vulnerability enabled data exfiltration from a single well-crafted email..

This blog follows the Darktrace team’s investigation of the customer’s hypothesis and examines how the attack interacted with their existing security stack. The results highlight which defenses held, where gaps emerged, and how behavioral detection mattered more than guardrails. This investigation also demonstrates why behavioral detection is becoming increasingly important for AI security, as prompt injections often contain no traditional indicators of compromise.  

How do prompt injection attacks work?

Prompt injection works by carefully crafting the content and structure of the prompt to alter the LLM’s behavior or output in unintended ways. This can cause models to violate guardrails, generate harmful content or enable unauthorised access.

Prompt injection attack example

The well-known example, EchoLeak (CVE-2025-32711), was a zero-click vulnerability in Microsoft 365 Copilot that relied on a carefully crafted email containing hidden instructions that the AI system interpreted as commands rather than content, creating a pathway for unauthorized access to sensitive information without any user interaction.

While Darktrace / SECURE AI is designed to prevent agents from producing unintended outcomes, we wanted to see if we could catch and prevent this threat type earlier in the attack life-cycle, at the email security layer.

How we tested prompt injection attacks on an enterprise agent

Summary:

  1. Claude generated a prompt injection payload.  
  2. Hidden instructions were embedded in an email.  
  3. The email passed traditional validation checks.  
  4. Darktrace analyzed the language and sender behavior.  
  5. The email was quarantined before the AI agent could process it.

To test Darktrace / EMAIL against this attack class, we opened Claude, gave it the customer's context and problem statement (Gemini agent with inbox access, internal tool calls), told it we were validating Darktrace / EMAIL's detection of prompt injections, and asked for a test payload. See below:

Figure 1
Figure 2

Despite the guardrails supposedly built into the model, Claude surprisingly gave us the entire exploit in plaintext (albeit very basic), illegible to a human as the text was sent in white text (see Figure 1) but framed as an authoritative override for anything downstream reading the mail programmatically (i.e. the Gemini agent).

How Darktrace detected a prompt injection attack

We then sent the Claude-crafted email from a freemail address to the target recipient’s inbox. Despite the email containing no malicious payload, the freemail address having no malicious reputation, and the validation checks all passing, Darktrace  /EMAIL flagged the email as a 93/100 anomaly and moved it to junk, out of scope for the AI agent.

Figure 3: The test email sent with the hidden prompt injection
Figure 4: The email analysis in Darktrace / EMAIL 
Figure 5: Darktrace / EMAIL detection of malicious activity

The interesting part is what triggered the detection (see Figure 5)

  • Possible machine prompt content: text in the body detected as instructions written for a machine to execute, not for a human to read
  • Possible machine prompt content + basic suspicious correspondence: the same content, correlated with sender-side anomalies: freemail domain (yahoo[.]com), unknown correspondent, no prior mail history with the recipient, and suspicious references to payment information

Neither of those is a signature match. Nothing in the email was on a blacklist. There was no malware, no link and no attachment. Darktrace analyzed the context in which the email was delivered and flagged it as likely risky.  The anomalous language features and the context of the sender relative to the recipient's normal behavior, combined with the unusual hidden text (prompt) were enough for Darktrace / EMAIL to act on the risk.

Result: Darktrace / EMAIL autonomously junked the email, out of scope for any AI agent parsing the inbox.

Why behavioral security makes a difference detecting prompt injection attacks

Cyberattacks don't look like traditional exploits anymore. They now operate in natural language, not strictly code.

That breaks the traditional stack. AV, firewalls, static scanning and signature-based SEGs all assume a payload to inspect.  

A prompt injection has no payload. It's just an instruction, written in natural language, dressed up as anything the attacker wants: an invoice, an HR request, a calendar invite, some simple PowerPoint slides.

EchoLeak proved that hidden instructions can sit inside an email invisible to the user but fully readable by the LLM, and the LLM will follow them blindly.  

This test and GTG-1002 proved that the LLM itself can be socially engineered. Tell it you're an authorized tester and it will hand you the attack.

Rules and static classifiers can catch the obvious cases. But natural language has infinite variants, and the attack surface is the model's innate functionality to comply.  

The deeper problem here is intent: an LLM can't reliably tell whether an instruction in its context came from its developer, its user, or an attacker who slipped it into an email. To the LLM, everything reads as language and looks like a legitimate ask. This is why behavioural detection wins, as you become aware of intent when you look at the context of an interaction. Does this sender normally send this kind of message to this recipient? Does this prompt fit the user's normal pattern? Is this agent behaving the way this agent normally behaves?  

Intent can't be read off a single email, it emerges from behavioral context. Which is how Darktrace enables threat detection, through behavioral understanding.

Why enterprise AI security requires more than guardrails

Claude didn't roll over immediately… the first section of the response was a (slight) pushback, but then it wrote the payload anyway without having to ask twice.

Here the framing of the prompt did all the work. The “testing security capabilities” angle moved the model from refusal to unquestioned compliance to the user prompt.

This isn't the first time this has happened, of course. Anthropic disclosed in November 2025 that a Chinese state-sponsored group they tracked as GTG-1002 ran the first documented AI-orchestrated espionage campaign against ~30 targets by posing as employees of a legitimate cybersecurity firm doing authorised penetration testing.

The takeaway isn't that AI guardrails are ineffective. They raise the cost of low-effort attacks and remain an important first layer of defense. However, for most organizations today, they’re the only line of defense when deploying AI agents. If a prompt injection bypasses those controls, organizations still need a way to detect and stop malicious behavior elsewhere in the attack chain.

Attackers will continue to have working prompt injections easily and quickly. The question is what stops one when it lands in an inbox your agent is reading.

That's where behavioral detection comes in.

How Darktrace detects prompt injection attacks in emails

Two things Darktrace does that a model-level guardrail or static rules and signatures can't:

Natural language analysis at the email or prompt layer. The email is assessed on its own merits: is this content shaped like instructions for a machine, regardless of what the receiving agent decides to do about it?

Behavioral context around the language. An AI agent behaves like an extremely agreeable human, and it will go above and beyond to comply with the user’s request. That's exactly why you must consider the business context, such sender behaviour, mailing history, and organisational norms, as these matter even more when the recipient is an AI.

Darktrace has been perfecting behavioral anomaly detection for over a decade; the same self-learning approach that catches BEC and account takeover applies directly to prompt injection delivery. Our multi-layered AI stack extracts content from the message, builds behavioural understanding through social graphing and Pattern of Life analysis, and then combines natural language, topic, inducement, sender relationship and anomaly signals before deciding what action to take.  

This matters for prompt injection because the threat is not the plain language itself, but the intent behind the language that can cause an AI agent to respond in unexpected ways.

How to secure enterprise AI operations from prompt injection attacks

Email was the entry point in this case, but it is only one of many possible vectors.  

Anywhere an agent can retrieve information, an attacker can potentially introduce a prompt injection.

Emails, documents, SharePoint sites, web pages, knowledge bases, chat platforms, and third-party integrations all provide opportunities to influence an agent's behavior. Wherever an agent finds its orders, a prompt injection opportunity exists.

This is why securing AI requires more than blocking malicious inputs. Organizations also need visibility into how agents behave after consuming information from across their environment. If an agent begins accessing unexpected data, taking unusual actions, or operating outside its normal patterns, those behaviors may provide the strongest signal that something has gone wrong.

Effective AI security requires defense in depth: reducing the likelihood of malicious instructions reaching the agent while maintaining the ability to detect and investigate suspicious behavior if they do.

The challenge isn't protecting a single entry point. It's recognizing that, in an AI-powered environment, every source of information is also a potential source of influence.

Are you deploying autonomous agents across your enterprise and want to see this tested in your environment? Let's talk.

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Carlo Loregian
Solutions Engineer

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August 4, 2026

Extending AI Security Visibility with Darktrace and Microsoft Agent 365

microsoft darktrace integrationDefault blog imageDefault blog image

AI agents are rapidly becoming embedded in everyday business operations, helping employees automate workflows, access information, and accelerate decision-making. As organizations embrace agentic AI, security teams face a growing challenge: understanding how AI is being used, what agents can access, and where risk may emerge.

As agents take on more business-critical work, security teams often need to move across multiple portals to understand risks spanning identity, data, and threat activity. This fragmented view can make it difficult to assess an agent's overall risk posture and determine where attention is needed. Organizations need a way to bring these signals together without disrupting existing security investments or workflows.

Today, Darktrace is announcing an integration between Darktrace / SECURE AI and Microsoft Agent 365 that brings Darktrace's Adaptive AI-driven risk signals directly into the Microsoft 365 Admin Center. By extending the visibility and risk understanding provided by Darktrace / SECURE AI into the Microsoft ecosystem, organizations can gain a more unified understanding of AI agent risk across their environments.

As one of the first security companies to partner with Microsoft to contribute third-party risk signals to the Agent Registry, Darktrace is helping shape how organizations understand and manage AI agent risk.

Extending visibility into the Microsoft Agent 365 experience

Microsoft Agent 365 provides administrators with a centralized registry of AI agents operating within their environment. As organizations expand their use of AI agents, this centralized visibility becomes increasingly important for governance and oversight.

This new integration extends that visibility by allowing Darktrace-generated risk signals to be surfaced directly within the Microsoft Agent 365 experience. By combining Microsoft's agent management and security capabilities with Darktrace's AI-powered risk analysis, organizations gain greater awareness of potential security concerns associated with AI agents operating across their environments.

By integrating Darktrace telemetry into Agent 365, customers can:  

  • Surface Darktrace-detected risks and signals alongside Microsoft-native signals in a single interface
  • Identify potentially compromised or anomalous AI agents more quickly
  • Gain unified understanding of context and agent behavior  

This approach reinforces a single control plane for AI security while allowing organizations to continue leveraging existing investments across both platforms.  

Why unified visibility of AI agent risk signals matters

As AI adoption accelerates across Microsoft environments, organizations must manage new forms of behavior, access patterns, and risk. Security teams need more than inventories and permissions. They need visibility into how AI systems operate and how risk evolves over time.  

This integration addresses a critical gap: how to bring behavioral AI security insights into the same control plane as identity, access, and agent management.  

With Darktrace and Microsoft Agent 365 together, organizations benefit from:  

  • Unified visibility: A single pane of glass for understanding AI agent risk signals across Microsoft and Darktrace signals  
  • Faster detection of abnormal agent behavior: Darktrace's Adaptive AI highlights deviations that may not be captured by static controls  
  • Operational efficiency: Security teams can triage and prioritize risk signals without switching between systems
  • Stronger trust in AI deployments: Clear attribution, context, and investigation pathways improve confidence in AI agent usage

Extending Microsoft's AI security model, not replacing it

Securing AI requires a layered approach that combines governance, visibility, threat detection, and risk management. This integration is designed to complement Microsoft's security capabilities, not duplicate them.  

Through Darktrace / SECURE AI, Darktrace contributes:  

  • Identification of risk via advanced prompt analysis  
  • Behavioral anomaly detection across AI agents  
  • Cross-environment threat correlation  
  • Autonomous insight into emerging or unknown risks  

Microsoft provides:  

  • Centralized agent management
  • Identity and access governance
  • Native detection of risk signals and enforcement capabilities

Together, these capabilities create a more complete, layered approach to securing AI-driven enterprises. Organizations gain the governance and policy controls needed to manage AI adoption while benefiting from continuous visibility into how AI is used across the business.

Building the future of secure AI

As AI agents become more deeply embedded in business processes, organizations need more than inventories and static controls. They need to understand how AI is being used, how agents behave, and where risk is emerging across the enterprise.

Darktrace / SECURE AI delivers that understanding through continuous visibility into AI activity, helping security teams assess intent, identify behavioral drift, and uncover emerging risk across both human and agent-driven workflows. Powered by Adaptive AI, it provides the context needed to secure AI as it evolves.  

The integration with Microsoft Agent 365 extends those insights into the workflows organizations already use. Agent 365 provides a unified control plane for governing and securing AI agents, while Darktrace contributes complementary behavioral risk signals that can be surfaced within the Agent 365 experience. Together, they give customers broader context on agent activity and risk while preserving the value of their existing Microsoft and Darktrace investments.

As enterprises move from AI experimentation to AI-powered execution, Microsoft and Darktrace help bring together governance, compliance, behavioral understanding, and oversight in a unified approach to AI security. For organizations adopting Microsoft 365 E7, Darktrace / SECURE AI further strengthens that foundation by providing continuous visibility into AI activity, agent behavior, and emerging risk as AI adoption scales.

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