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July 6, 2023

How Darktrace Foiled QR Code Phishing

Explore Darktrace's successful detection of QR code phishing. Understand the methods used to thwart these sophisticated cyber threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Alexandra Sentenac
Cyber Analyst
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06
Jul 2023

What is a QR Code?

Invented by a Japanese company in 1994 to label automobile parts, Quick Response codes, best known as QR codes, are rapidly becoming ubiquitous everywhere in the world. Their design, inspired by the board and black and white pieces of the game of Go, permits the storage of more information than regular barcodes and to access that information more quickly. The COVID-19 pandemic contributed to their increased popularity as it conveniently replaced physical media of all types for the purpose of content sharing. It is now common to see them in restaurant menus, plane tickets, advertisements and even in stickers containing minimal to no text pasted on lamp posts and other surfaces, enticing passers-by to scan its content. 

QR Code Phishing Attacks (Quishing)

Recently, threat actors have been identified using QR codes too to embed malicious URLs leading the unsuspecting user to compromised websites containing malware or designed to harvest credentials. In the past month, Darktrace has observed an increase in the number of phishing emails leveraging malicious QR codes for malware distribution and/or credential harvesting, a new form of social engineering attack labelled “Quishing” (i.e., QR code phishing).

Between June 13 and June 22, 2023, Darktrace protected a tech company against one such Quishing attack when five of its senior employees were sent malicious emails impersonating the company’s IT department. The emails contained a QR code that led to a login page designed to harvest the credentials of these senior staff members. Fortunately for the customer, Darktrace / EMAIL thwarted this phishing campaign in the first instance and the emails never reached the employee inboxes. 

Trends in Quishing Attacks

The Darktrace/Email team have noticed a recent and rapid increase in QR code abuse, suggesting that it is a growing tactic used by threat actors to deliver malicious payload links. This trend has also been observed by other security solutions [1] [2] [3] [4]. The Darktrace/Email team has identified malicious emails abusing QR codes in multiple ways. Examples include embedded image links which load a QR code and QR code images being delivered as attachments, such as those explored in this case study. Darktrace/Email is continually refining its detection of malicious QR codes and QR code extraction capabilities so that it can detect and block them regardless of their size and location within the email.   

Quishing Attack Overview

The attack consisted of five emails, each sent from different sender and envelope addresses, displayed common points between them. The emails all conveyed a sense of urgency, either via the use of words such as “urgent”, “now”, “required” or “important” in the subject field or by marking the email as high priority, thus making the recipient believe the message is pressing and requires immediate attention. 

Additionally, the subject of three of the emails directly referred to two factor authentication (2FA) enabling or QR code activation. Another particularity of these emails was that three of them attempted to impersonate the internal IT team of the company by inserting the company domain alongside strings, such as “it-desk” and “IT”, into the personal field of the emails. Email header fields like this are often abused by attackers to trick users by pretending to be an internal department or senior employee, thus avoiding more thorough validation checks. Both instilling a sense of urgency and including a known domain or name in the personal field are techniques that help draw attention to the email and maximize the chances that it is opened and engaged by the recipient. 

However, threat actors also need to make sure that the emails actually reach the intended inboxes, and this can be done in several ways. In this case, several tactics were employed. Two of the five emails were sent from legitimate sender addresses that successfully passed SPF validation, suggesting they were sent from compromised accounts. SPF is a standard email authentication method that tells the receiving email servers whether emails have been sent from authorized servers for a given domain. Without SPF validation, emails are more likely to be categorized as spam and be sent to the junk folder as they do not come from authorized sources.

Another of the malicious emails, which also passed SPF checks, used a health care facility company domain in the header-from address field but was actually sent from a different domain (i.e., envelope domain), which lowers the value of the SPF authentication. However, the envelope domain observed in this instance belonged to a company recently acquired by the tech company targeted by the campaign.

This shows a high level of targeting from the attackers, who likely hoped that this detail would make the email more familiar and less suspicious. In another case, the sender domain (i.e., banes-gn[.]com) had been created just 6 days prior, thus lowering the chances of there being open-source intelligence (OSINT) available on the domain. This reduces the chances of the email being detected by traditional email security solutions relying on signatures and known-bad lists.

Darktrace Detects Quishing Attack

Despite its novelty, the domain was detected and assessed as highly suspicious by Darktrace. Darktrace/Email was able to recognize all of the emails as spoofing and impersonation attempts and applied the relevant tags to them, namely “IT Impersonation” and “Fake Account Alert”, depending on the choice of personal field and subject. The senders of the five emails had no prior history or association with the recipient nor the company as no previous correspondence had been observed between the sender and recipient. The tags applied informed on the likely intent and nature of the suspicious indicators present in the email, as shown in Figure 1. 

Darktrace/Email UI
Figure 1: Email log overview page, displaying important information clearly and concisely. 

Quishing Attack Tactics

Minimal Plain Text

Another characteristic shared by these emails was that they had little to no text included in the body of the email and they did not contain a plain text portion, as shown in Figure 2. For most normal emails sent by email clients and most automated programs, an email will contain an HTML component and a text component, in addition to any potential attachments present. All the emails had one image attachment, suggesting the bulk of the message was displayed in the image rather than the email body. This hinders textual analysis and filtering of the email for suspicious keywords and language that could reveal its phishing intent. Additionally, the emails were well-formatted and used the logo of the well-known corporation Microsoft, suggesting some level of technical ability on the part of the attackers. 

Figure 2: Email body properties giving additional insights into the content of the email. 

Attachment and link payloads

The threat actors employed some particularly innovative and novel techniques with regards to the attachments and link payloads within these emails. As previously stated, all emails contained an image attachment and one or two links. Figure 3 shows that Darktrace/Email detected that the malicious links present in these emails were located in the attachments, rather than the body of the email. This is a technique often employed by threat actors to bypass link analysis by security gateways. Darktrace/Email was also able to detect this link as a QR code link, as shown in Figure 4.

Figure 3: Further properties and metrics regarding the location of the link within the email. 
Figure 4: Darktrace / EMAIL analyzes multiple metrics and properties related to links, some of which are detailed here. 

The majority of the text, as well as the malicious payload, was contained within the image attachment, which for one of the emails looked like this: 

example of quishing email
Figure 5: Redacted screenshot of the image payload contained in one of the emails. 

Convincing Appearance

As shown, the recipient is asked to setup 2FA authentication for their account within two days if they don’t want to be locked out. The visual formatting of the image, which includes a corporate logo and Privacy Statement and Acceptable Use Policy notices, is well balanced and convincing. The payload, in this case the QR code containing a malicious link, is positioned in the centre so as to draw attention and encourage the user to scan and click. This is a type of email employees are increasingly accustomed to receiving in order to log into corporate networks and applications. Therefore, recipients of such malicious emails might assume represents expected business activity and thus engage with the QR code without questioning it, especially if the email is claiming to be from the IT department.  

Malicious Redirection

Two of the Quishing emails contained links to legitimate file storage and sharing solutions Amazon Web Services (AWS) and and InterPlanetary File System (IPFS), whose domains are less likely to be blocked by traditional security solutions. Additionally, the AWS domain link contained a redirect to a different domain that has been flagged as malicious by multiple security vendors [5]. Malicious redirection was observed in four of the five emails, initially from well-known and benign services’ domains such as bing[.]com and login[.]microsoftonline[.]com. This technique allows attackers to hide the real destination of the link from the user and increase the likelihood that the link is clicked. In two of the emails, the redirect domain had only recently been registered, and in one case, the redirect domain observed was hosted on the new .zip top level domain (i.e., docusafe[.]zip). The domain name suggests it is attempting to masquerade as a compressed file containing important documentation. As seen in Figure 6, a new Darktrace/Email feature allows customers to safely view the final destination of the link, which in this case was a seemingly fake Microsoft login page which could be used to harvest corporate credentials.

Figure 6: Safe preview available from the Darktrace/Email Console showing the destination webpage of one of the redirect links observed.

Gathering Account Credentials

Given the nature of the landing page, it is highly likely that this phishing campaign had the objective of stealing the recipients’ credentials, as further indicated by the presence of the recipients’ email addresses in the links. Additionally, these emails were sent to senior employees, likely in an attempt to gather high value credentials to use in future attacks against the company. Had they succeeded, this would have represented a serious security incident, especially considering that 61% of attacks in 2023 involved stolen or hacked credentials according to Verizon’s 2023 data breach investigations report [6]. However, these emails received the highest possible anomaly score (100%) and were held by Darktrace/Email, thus ensuring that their intended recipients were never exposed to them. 

Looking at the indicators of compromise (IoCs) identified in this campaign, it appears that several of the IPs associated with the link payloads have been involved in previous phishing campaigns. Exploring the relations tab for these IPs in Virus Total, some of the communicating files appear to be .eml files and others have generic filenames including strings such as “invoice” “remittance details” “statement” “voice memo”, suggesting they have been involved in other phishing campaigns seemingly related to payment solicitation and other fraud attempts.

Figure 7: Virus Total’s relations tab for the IP 209.94.90[.]1 showing files communicating with the IP. 

Conclusion

Even though the authors of this Quishing campaign used all the tricks in the book to ensure that their emails would arrive unactioned by security tools to the targeted high value recipients’ inboxes, Darktrace/Email was able to immediately recognize the phishing attempts for what they were and block the emails from reaching their destination. 

This campaign used both classic and novel tactics, techniques, and procedures, but ultimately were detected and thwarted by Darktrace/Email. It is yet another example of the increasing attack sophistication mentioned in a previous Darktrace blog [7], wherein the attack landscape is moving from low-sophistication, low-impact, and generic phishing tactics to more targeted, sophisticated and higher impact attacks. Darktrace/Email does not rely on historical data nor known-bad lists and is best positioned to protect organizations from these highly targeted and sophisticated attacks.

References

[1] https://www.infosecurity-magazine.com/opinions/qr-codes-vulnerability-cybercrimes/ 

[2] https://www.helpnetsecurity.com/2023/03/21/qr-scan-scams/ 

[3] https://www.techtarget.com/searchsecurity/feature/Quishing-on-the-rise-How-to-prevent-QR-code-phishing 

[4] https://businessplus.ie/tech/qr-code-phishing-hp/ 

[5] https://www.virustotal.com/gui/domain/fistulacure.com

[6] https://www.verizon.com/business/en-gb/resources/reports/dbir/ ; https://www.verizon.com/business/en-gb/resources/reports/dbir/

[7] https://darktrace.com/blog/shifting-email-conversation 

Darktrace Model Detections 

Association models

No Sender or Content Association

New Sender

Unknown Sender

Low Sender Association

Link models

Focused Link to File Storage

Focused Rare Classified Links

New Unknown Hidden Redirect

High Risk Link + Low Sender Association

Watched Link Type

High Classified Link

File Storage From New

Hidden Link To File Storage

New Correspondent Classified Link

New Unknown Redirect

Rare Hidden Classified Link

Rare Hidden Link

Link To File Storage

Link To File Storage and Unknown Sender

Open Redirect

Unknown Sender Isolated Rare Link

Visually Prominent Link

Visually Prominent Link Unexpected For Sender

Low Link Association

Low Link Association and Unknown Sender

Spoof models

Fake Support Style

External Domain Similarities

Basic Known Entity Similarities

Unusual models

Urgent Request Banner

Urgent Request Banner + Basic Suspicious Sender

Very Young Header Domain

Young Header Domain

Unknown User Tracking

Unrelated Personal Name Address

Unrelated Personal Name Address + Freemail

Unusual Header TLD

Unusual Connection From Unknown

Unbroken Personal

Proximity models

Spam + Unknown Sender

Spam

Spam models

Unlikely Freemail Correspondence

Unlikely Freemail Personalization

General Indicators models

Incoming Mail Security Warning Message

Darktrace Model Tags

Credential Harvesting

Internal IT Impersonation

Multistage payload

Lookalike Domain

Phishing Link

Email Account Takeover

Fake Account Alert

Low Mailing History

No Association

Spoofing Indicators

Unknown Correspondent

VIP

Freemail

IoC - Type - Description & Confidence

fistulacure[.]com

domain

C2 Infrastructure

docusafe[.]zip

domain

Possible C2 Infrastructure

mwmailtec[.]com

domain

Possible C2 Infrastructure

czeromedia[.]com

domain

Possible C2 Infrastructure

192.40.165[.]109

IP address

Probable C2 Infrastructure

209.94.90[.]1

IP address

C2 Infrastructure

52.61.107[.]58

IP address

Possible C2 Infrastructure

40.126.32[.]133

IP address

Possible C2 Infrastructure

211.63.158[.]157

IP address

Possible C2 Infrastructure

119.9.27[.]129

IP address

Possible C2 Infrastructure

184.25.204[.]33

IP address

Possible C2 Infrastructure

40.107.8[.]107

IP address

Probable C2 Infrastructure

40.107.212[.]111

IP address

Possible Infrastructure

27.86.113[.]2

IP address

Possible C2 Infrastructure

192.40.191[.]19

IP address

Possible C2 Infrastructure

157.205.202[.]217

IP address

Possible C2 Infrastructure

a31f1f6063409ecebe8893e36d0048557142cbf13dbaf81af42bf14c43b12a48

SHA256 hash

Possible Malicious File

4c4fb35ab6445bf3749b9d0ab1b04f492f2bc651acb1bbf7af5f0a47502674c9

SHA256 hash

Possible Malicious File

f9c51d270091c34792b17391017a09724d9a7890737e00700dc36babeb97e252

SHA256 hash

Possible Malicious File

9f8ccfd616a8f73c69d25fd348b874d11a036b4d2b3fc7dbb99c1d6fa7413d9a

SHA256 hash

Possible Malicious File

b748894348c32d1dc5702085d70d846c6dd573296e79754df4857921e707c439

SHA256 hash

Possible Malicious File

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Alexandra Sentenac
Cyber Analyst

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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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