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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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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician

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July 1, 2026

5 Ways AI is changing traditional security models according to modern CISOs

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The Reality of Securing AI in Motion

Traditional security tools were built for environments defined by fixed rules and predictable workflows. But AI behavior is non-deterministic. The same prompt can produce different outcomes, and risk often emerges gradually as AI behavior adapts, and permissions drift over time. This creates a constantly shifting environment where security teams are working to define control in a system that resists stability. “In AI security, yesterday's priorities can become tomorrow's blind spots. The landscape shifts that fast,” warned the SVP and Head of Technology and Cybersecurity of a real estate investment trust. Conventional approaches, which rely on establishing and maintaining a steady baseline, struggle to keep up with that level of change.

At the same time, AI adoption is accelerating across organizations, often faster than security teams can implement the controls needed to manage it. “The car is being built while it’s already on the road,” explained the CISO of a global private fund administrator. “The threats we're securing against today won't be the threats we're facing tomorrow. What kept us up three months ago looks nothing like what we're dealing with today.”

As businesses move quickly to unlock value from AI, security teams are left closing gaps in real time, while also facing adversaries who are using AI to make their attacks more scalable, adaptive, and difficult to detect. In this recent roundtable discussion of CISOs and security leaders, five themes emerged around AI cyber risk.  

1. AI agents with human access but no human judgment

In Darktrace’s 2026 State of AI Cybersecurity report, 96% of the surveyed security professionals agree that AI significantly improves the speed and efficiency with which they work. Yet, 92% admitted that they’re concerned with the security implications of the use of AI agents across their workforce.

AI agents now operate with human-level permissions across systems, acting at machine speed, orchestrating actions across platforms, and making decisions without the judgment or caution a person would apply. Unlike human users, they cannot be expected to pause and question whether a given action is appropriate.

Their identities are also difficult to inventory, govern, and audit. As agents become easier to deploy than legacy IT systems ever were, organizations are quickly losing track of what is running, what it has access to, and what it is doing. This creates a growing class of highly privileged, autonomous actors operating without the visibility or oversight that traditional identity and access controls were designed to provide.“While AI adoption is critical to running a modern business, AI alone can’t solve all our cybersecurity challenges,” said a global financial sector CISO. “We still need think critically and use human judgement. Those are two things AI can’t do.”

This lack of human judgment becomes especially risky as new architectures, such as Model Context Protocol (MCP), can expand how agents connect to data, tools, and external systems. By design, MCP enables agents to dynamically discover and interact with new resources, increasing flexibility but also introducing new pathways for unintended access, data exposure, or abuse if not properly governed.

The CISO of a fund administrator highlighted one emerging vector as an example: rogue MCP servers. “Our developers want to move quickly and bring value to the business, but technologies like these can unintentionally expose sensitive data in ways that would never have happened before.”

2. Increased digital complexity and expanded attack surface

AI activity rarely stays contained. A single prompt can trigger a chain of actions across networks, email, cloud infrastructure, SaaS platforms, endpoints, identity systems, and development environments, spanning systems that were never designed to be secured as a single, connected flow. This expands both the scale and complexity of what security teams need to monitor and defend.

Yet no single control has visibility across that entire chain. “You can’t defend effectively what you can’t see,” cautioned the private fund administrator CISO. As AI-driven activity moves fluidly across environments, gaps in coverage become inevitable, creating blind spots that attackers can exploit.

Threat actors are already capitalizing on this lack of visibility. “Threat actors have advanced their use of generative AI to launch more convincing phishing campaigns, automate social engineering, and scale attacks with greater precision down to the individual level,” said the SVP of Technology and Cybersecurity for the real estate investment trust. What was once manual and targeted can now be automated and personalized at scale, making attacks harder to detect and easier to execute.

At the same time, the pace of exploitation is accelerating. As a global CISO operating across 40+ countries described it: “Zero-day vulnerabilities are no longer zero day; it’s minus one day. By the time you get to it and address it, it’s already a problem.” By the time risk is identified, it has often already been realized.

The result is a rapidly expanding and increasingly interconnected attack surface that challenges security teams to maintain visibility, context, and control across AI-driven activity.

3. Shadow AI is already everywhere

76% of organizations now cite shadow AI as a problem, one that is spreading through organizations in ways that are hard to track and even harder to control.

Employees are experimenting with publicly available Gen AI tools. Teams are spinning up low-code automations on their own. SaaS providers are quietly embedding AI into existing products. Developers are plugging AI services directly into workflows, often without pausing to consider what that exposure means.

The result is a lack of visibility into:

  • What AI tools are being used
  • What data those tools can access
  • Where prompts and outputs are going
  • Which AI agents are interacting with enterprise systems

The SVP of Cybersecurity at a real estate investment trust described the shift: “Before, I was worried about someone sending data erroneously to their personal email. Now we have all these agents online that people are utilizing, and we’re looking at those vectors as well.” For security teams, this means operating without a complete view of how AI is being used, what it can access, and where risk may already be emerging.

4. Built-in guardrails are not enough

Organizations often assume that native AI guardrails or provider-level controls are sufficient to manage AI risk. But securing AI requires ongoing visibility, oversight, and governance, not just controls configured at deployment. "It’s a misconception that adopting AI is going to solve all your problems,” warns a global financial services CISO.

Security leaders are increasingly recognizing the limitations of these controls as:

  • Fragmented and difficult to enforce consistently across multiple AI systems, workflows, and environments
  • Ambiguous in terms of accountability due to shared responsibility for AI governance between IT, security, developers, business teams, and third-party providers
  • Limited in end-to-end oversight, leaving gaps that stretch from the initial prompt all the way through to the downstream impact of an agent's actions

Securing AI demands more than simple prompt filtering or static policy enforcement. It requires understanding intent, behavior, and context across both human and AI activity.

The next phase of cybersecurity: securing AI

To safely and responsibly adopt AI at scale, organizations need a new operational model for cybersecurity that’s capable of:

• Understanding AI behavior

• Identifying risk in real time

• Maintaining governance without slowing innovation

The CSO of a $10 billion municipal utility organization described the challenge with precision: “We have to move at the speed of innovation and risk, because both are accelerating faster than ever.”

Embrace AI with confidence with Darktrace / SECURE AI

Darktrace has introduced Darktrace / SECURE AI™, a new product within the Darktrace ActiveAI Security Platform™  ,designed to provide enterprise-wide security for AI by applying industry leading behavioral analysis to how prompts, agents, and AI systems are used.

Darktrace / SECURE AITM delivers real-time visibility and control across Enterprise and SaaS GenAI prompts, AI agent identities, development and production environments, and Shadow AI - detecting even subtle misuse, misconfiguration, and drift that traditional, rule-based controls simply do not understand. By interpreting context and intent across humans and machines, Darktrace enables organizations to adopt AI at scale without introducing unmanaged risk

What makes this possible is Darktrace’s decade-long maturity and expertise in behavioral understanding and AI-native cybersecurity. Achieved with Self-Learning AI that has been proven across more than 10,000 organizations, Darktrace understands what “normal” looks like for a business, across its users, systems, and now AI, so that meaningful deviations can be detected and acted on before they become incidents.

With one CISO describing Darktrace’s Self-Learning AI as “a leap forward compared to other tools” and another as a “force multiplier,” the technology can interpret ambiguous interactions, understand how access accumulates over time, and recognize when behavior, human or machine, begins to drift.

“Strategically, we’re looking to gain more visibility into how AI is operating across the environment and achieve greater control over what AI should be allowed to access and do,” shared the CISO at a private fund administrator.  

“What I’ve seen from Darktrace / SECURE AI is extremely promising. I have tremendous confidence in Darktrace’s vision for where this is headed and its ability to execute on this new solution.”

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