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August 23, 2022

Emotet Resurgence: Cross-Industry Analysis

Technical insights on the Emotet resurgence in 2022 across various client environments, industries, and regions.
Written by
Eugene Chua
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
Eugene Chua
Cyber Security Analyst
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23
Aug 2022

Introduction

Last year provided further evidence that the cyber threat landscape remains both complex and challenging to predict. Between uncertain attribution, novel exploits and rapid malware developments, it is becoming harder to know where to focus security efforts. One of the largest surprises of 2021 was the re-emergence of the infamous Emotet botnet. This is an example of a campaign that ignored industry verticals or regions and seemingly targeted companies indiscriminately. Only 10 months after the Emotet takedown by law enforcement agencies in January, new Emotet activities in November were discovered by security researchers. These continued into the first quarter of 2022, a period which this blog will explore through findings from the Darktrace Threat Intel Unit. 

Dating back to 2019, Emotet was known to deliver Trickbot payloads which ultimately deployed Ryuk ransomware strains on compromised devices. This interconnectivity highlighted the hydra-like nature of threat groups wherein eliminating one (even with full-scale law enforcement intervention) would not rule them out as a threat nor indicate that the threat landscape would be any more secure. 

When Emotet resurged, as expected, one of the initial infection vectors involved leveraging existing Trickbot infrastructure. However, unlike the original attacks, it featured a brand new phishing campaign.

Figure 1: Distribution of observed Emotet activities across Darktrace deployments

Although similar to the original Emotet infections, the new wave of infections has been classified into two categories: Epochs 4 and 5. These had several key differences compared to Epochs 1 to 3. Within Darktrace’s global deployments, Emotet compromises associated to Epoch 4 appeared to be the most prevalent. Affected customer environments were seen within a large range of countries (Figure 1) and industry verticals such as manufacturing and supply chain, hospitality and travel, public administration, technology and telecoms and healthcare. Company demographics and size did not appear to be a targeting factor as affected customers had varying employee counts ranging from less than 250, to over 5000.

Key differences between Epochs 1-3 vs 4-5

Based on wider security research into the innerworkings of the Emotet exploits, several key differences were identified between Epochs 4/5 and its predecessors. The newer epochs used:

·       A different Microsoft document format (OLE vs XML-based).

·       A different encryption algorithm for communication. The new epochs used Elliptic Curve Cryptograph (ECC) [1] with public encryption keys contained in the C2 configuration file [2]. This was different from the previous Rivest-Shamir-Adleman (RSA) key encryption method.

·       Control Flow Flattening was used as an obfuscation technique to make detection and reverse engineering more difficult. This is done by hiding a program’s control flow [3].

·       New C2 infrastructure was observed as C2 communications were directed to over 230 unique IPs all associated to the new Epochs 4 and 5.

In addition to the new Epoch 4 and 5 features, Darktrace detected unsurprising similarities in those deployments affected by the renewed campaign. This included self-signed SSL connections to Emotet’s new infrastructure as well as malware spam activities to multiple rare external endpoints. Preceding these outbound communications, devices across multiple deployments were detected downloading Emotet-associated payloads (algorithmically generated DLL files).

Emotet Resurgence Campaign

Figure 2: Darktrace’s Detection Timeline for Emotet Epoch 4 and 5 compromises

1. Initial Compromise

The initial point of entry for the resurgence activity was almost certainly via Trickbot infrastructure or a successful phishing attack (Figure 2). Following the initial intrusion, the malware strain begins to download payloads via macro-ladened files which are used to spawn PowerShell for subsequent malware downloads.

Following the downloads, malicious communication with Emotet’s C2 infrastructure was observed alongside activities from the spam module. Within Darktrace, key techniques were observed and documented below.

2. Establish Foothold: Binary Dynamic-link library (.dll) with algorithmically generated filenames 

Emotet payloads are polymorphic and contain algorithmically generated filenames . Within deployments, HTTP GET requests involving a suspicious hostname, www[.]arkpp[.]com, and Emotet related samples such as those seen below were observed:

·       hpixQfCoJb0fS1.dll (SHA256 hash: 859a41b911688b00e104e9c474fc7aaf7b1f2d6e885e8d7fbf11347bc2e21eaa)

·       M0uZ6kd8hnzVUt2BNbRzRFjRoz08WFYfPj2.dll (SHA256 hash: 9fbd590cf65cbfb2b842d46d82e886e3acb5bfecfdb82afc22a5f95bda7dd804)

·       TpipJHHy7P.dll (SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b)

These DLL files likely represent the distribution of Emotet loaders which depends on windows processes such as rundll32[.]exe and regsvr32[.]exe to execute. 

3. Establish Foothold: Outbound SSL connections to Emotet C2 servers 

A clear network indicator of compromise for Emotet’s C2 communication involved self-signed SSL using certificate issuers and subjects which matched ‘CN=example[.]com,OU=IT Department,O=Global Security,L=London,ST=London,C=GB’ , and a common JA3 client fingerprint (72a589da586844d7f0818ce684948eea). The primary C2 communications were seen involving infrastructures classified as Epoch 4 rather than 5. Despite encryption in the communication content, network contextual connection details were sufficient for the detection of the C2 activities (Figure 3).

Figure 3: UI Model Breach logs on download and outbound SSL activities.

Outbound SSL and SMTP connections on TCP ports 25, 465, 587 

An anomalous user agent such as, ‘Microsoft Outlook 15.0’, was observed being used for SMTP connections with some subject lines of the outbound emails containing Base64-encoded strings. In addition, this JA3 client fingerprint (37cdab6ff1bd1c195bacb776c5213bf2) was commonly seen from the SSL connections. Based on the set of malware spam hostnames observed across at least 10 deployments, the majority of the TLDs were .jp, .com, .net, .mx, with the Japanese TLD being the most common (Figure 4).

Figure 4: Malware Spam TLDs observed in outbound SSL and SMTP

 Plaintext spam content generated from the spam module were seen in PCAPs (Figure 5). Examples of clear phishing or spam indicators included 1) mismatched personal header and email headers, 2) unusual reply chain and recipient references in the subject line, and 3) suspicious compressed file attachments, e.g. Electronic form[.]zip.

Figure 5: Example of PCAP associated to SPAM Module

4. Accomplish Mission

 The Emotet resurgence also showed through secondary compromises involving anomalous SMB drive writes related to CobaltStrike. This consistently included the following JA3 hash (72a589da586844d7f0818ce684948eea) seen in SSL activities as well as SMB writes involving the svchost.exe file.

Darktrace Detection

 The key DETECT models used to identify Emotet Resurgence activities were focused on determining possible C2. These included:

·       Suspicious SSL Activity

·       Suspicious Self-Signed SSL

·       Rare External SSL Self-Signed

·       Possible Outbound Spam

File-focused models were also beneficial and included:

·       Zip or Gzip from Rare External Location

·       EXE from Rare External Location

Darktrace’s detection capabilities can also be shown through a sample of case studies identified during the Threat Research team’s investigations.

Case Studies 

Darktrace’s detection of Emotet activities was not limited by industry verticals or company sizing. Although there were many similar features seen across the new epoch, each incident displayed varying techniques from the campaign. This is shown in two client environments below:

When investigating a large customer environment within the public administration sector, 16 different devices were detected making 52,536 SSL connections with the example[.]com issuer. Devices associated with this issuer were mainly seen breaching the same Self-Signed and Spam DETECT models. Although anomalous incoming octet-streams were observed prior to this SSL, there was no clear relation between the downloads and the Emotet C2 connections. Despite the total affected devices occupying only a small portion of the total network, Darktrace analysts were able to filter against the much larger network ‘noise’ and locate detailed evidence of compromise to notify the customer.

Darktrace also identified new Emotet activities in much smaller customer environments. Looking at a company in the healthcare and pharmaceutical sector, from mid-March 2022 a single internal device was detected making an HTTP GET request to the host arkpp[.]com involving the algorithmically-generated DLL, TpipJHHy7P.dll with the SHA256 hash: 40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b (Figure 6). 

Figure 6: A screenshot from VirusTotal, showing that the SHA256 hash has been flagged as malicious by other security vendors.

After the sample was downloaded, the device contacted a large number of endpoints that had never been contacted by devices on the network. The endpoints were contacted over ports 443, 8080, and 7080 involving Emotet related IOCs and the same SSL certificate mentioned previously. Malware spam activities were also observed during a similar timeframe.

 The Emotet case studies above demonstrate how autonomous detection of an anomalous sequence of activities - without depending on conventional rules and signatures - can reveal significant threat activities. Though possible staged payloads were only seen in a proportion of the affected environments, the following outbound C2 and malware spam activities involving many endpoints and ports were sufficient for the detection of Emotet.

 If present, in both instances Darktrace’s Autonomous Response technology, RESPOND, would recommend or implement surgical actions to precisely target activities associated with the staged payload downloads, outgoing C2 communications, and malware spam activities. Additionally, restriction to the devices’ normal pattern of life will prevent simultaneously occurring malicious activities while enabling the continuity of normal business operations.

 Conclusion 

·       The technical differences between past and present Emotet strains emphasizes the versatility of malicious threat actors and the need for a security solution that is not reliant on signatures.

·       Darktrace’s visibility and unique behavioral detection continues to provide visibility to network activities related to the novel Emotet strain without reliance on rules and signatures. Key examples include the C2 connections to new Emotet infrastructure.

·       Looking ahead, detection of C2 establishment using suspicious DLLs will prevent further propagation of the Emotet strains across networks.

·       Darktrace’s AI detection and response will outpace conventional post compromise research involving the analysis of Emotet strains through static and dynamic code analysis, followed by the implementation of rules and signatures.

Thanks to Paul Jennings and Hanah Darley for their contributions to this blog.

Appendices

Model breaches

·       Anomalous Connection / Anomalous SSL without SNI to New External 

·       Anomalous Connection / Application Protocol on Uncommon Port 

·       Anomalous Connection / Multiple Connections to New External TCP Port 

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint 

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·       Anomalous Connection / Possible Outbound Spam 

·       Anomalous Connection / Rare External SSL Self-Signed 

·       Anomalous Connection / Repeated Rare External SSL Self-Signed      

·       Anomalous Connection / Suspicious Expired SSL 

·       Anomalous Connection / Suspicious Self-Signed SSL

·       Anomalous File / Anomalous Octet Stream (No User Agent) 

·       Anomalous File / Zip or Gzip from Rare External Location 

·       Anomalous File / EXE from Rare External Location

·       Compromise / Agent Beacon to New Endpoint 

·       Compromise / Beacon to Young Endpoint 

·       Compromise / Beaconing Activity To External Rare 

·       Compromise / New or Repeated to Unusual SSL Port 

·       Compromise / Repeating Connections Over 4 Days 

·       Compromise / Slow Beaconing Activity To External Rare 

·       Compromise / SSL Beaconing to Rare Destination 

·       Compromise / Suspicious Beaconing Behaviour 

·       Compromise / Suspicious Spam Activity 

·       Compromise / Suspicious SSL Activity 

·       Compromise / Sustained SSL or HTTP Increase 

·       Device / Initial Breach Chain Compromise 

·       Device / Large Number of Connections to New Endpoints 

·       Device / Long Agent Connection to New Endpoint 

·       Device / New User Agent 

·       Device / New User Agent and New IP 

·       Device / SMB Session Bruteforce 

·       Device / Suspicious Domain 

·       Device / Suspicious SMB Scanning Activity 

‍

For Darktrace customers who want to know more about using Darktrace to triage Emotet, refer here for an exclusive supplement to this blog.

References

[1] https://blog.lumen.com/emotet-redux/

[2] https://blogs.vmware.com/security/2022/03/emotet-c2-configuration-extraction-and-analysis.html

[3] https://news.sophos.com/en-us/2022/05/04/attacking-emotets-control-flow-flattening/

Written by
Eugene Chua
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
Eugene Chua
Cyber Security Analyst

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August 5, 2026

Testing a Prompt injection Attack Against an Enterprise AI Agent

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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
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August 4, 2026

Extending AI Security Visibility with Darktrace and Microsoft Agent 365

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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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