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August 7, 2023

Detection of an Evasive Credential Harvester | IPFS Phishing

Discover the emerging trend of malicious actors abusing the Interplanetary File System (IPFS) file storage protocol in phishing campaigns. Learn more here!
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
Lena Yu
Cyber Security Analyst
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07
Aug 2023

IPFS Phishing Attacks

Phishing attacks continue to be one of the most common methods of infiltration utilized by threat actors and they represent a significant threat to an organization’s digital estate. As phishing campaigns typically leverage social engineering methods to evade security tools and manipulate users into following links, downloading files, or divulging confidential information. It is a relatively low effort but high-yield type of cyber-attack.

That said, in recent years security teams have become increasingly savvy to these efforts. Attackers are having to adapt and come up with novel ways to carry out their phishing campaigns. Recently, Darktrace has observed a rise in phishing attacks attempting to abuse the InterPlanetary File System (IPFS) in campaigns that are able to dynamically adapt depending on the target, making it extremely difficult for security vendors to detect and investigate.

What is a IPFS?

IPFS is a file storage protocol a peer-to-peer (P2P) network used for storing and sharing resources in a distributed file system [1]. It is also a file storage system similar in nature to other centralized file storage services like Dropbox and Google Drive.

File storage systems, like IPFS, are often abused by malicious actors, as they allow attackers to easily host their own content without maintaining infrastructure themselves. However, as these file storage systems often have legitimate usages, blocking everything related to file storages may cause unwanted problems and affect normal business operations. Thus, the challenge lies in differentiating between legitimate and malicious usage.

While centralized, web-based file storage services use a Client-Server model and typically deliver files over HTTP, IPFS uses a Peer-to-Peer model for storing and sharing files, as shown in Figure 1.

Figure 1: (a) shows the Client-Server model that centralized, web-based file storage services use. The resource is available on the server, and the clients access the resource from the server. (b) shows the Peer-to-Peer model that IPFS use. The resources are available on the peers.

To verify the authenticity and integrity of files, IPFS utilizes cryptographic hashes.

A cryptographic hash value is generated using a file’s content upon upload to IPFS. This is used to generate the Content Identifier (CID). IPFS uses Content Addressing as opposed to Location Addressing, and this CID is used to point to a resource in IPFS [4].

When a computer running IPFS requires a particular file, it asks the connected peers if they have the file with a specific hash. If a peer has the file with the matching hash, it will provide it to the requesting computer [1][6].

Taking down content on IPFS is much more difficult compared to centralized file storage hosts, as content is stored on several nodes without a centralized entity, as shown in Figure 2. To take down content from IPFS, it must be removed from all the nodes. Thus, IPFS is prone to being abused for malicious purposes.

Figure 2: When the resource is unavailable on the server for (a), all the clients are unable to access the resource. When the resource is unavailable on one of the peers for (b), the resources are still available on the other peers.

The domains used in these IPFS phishing links are gateways that enable an HTTPS URL to access resources within the distributed IPFS file system.

There are two types of IPFS links, the Path Gateway and Subdomain Gateway [1].

Path Gateways have a fixed domain/host and identifies the IPFS resource through a resource-identifying string in the path. The Path Gateway has the following structure:

•       https://<gateway-host>.tld/ipfs/<CID>/path/to/resource

•       https://<gateway-host>.tld/ipns/<dnslink/ipnsid>/path/to/resource

On the other hand, Subdomain Gateways have a resource-identifying string in the subdomain. Subdomain Gateways have the following structure:

•       https://<cidv1b32>.ipfs.<gateway-host>.tld/path/to/resource

One gateway domain serves the same role as any other, which means attackers can easily change the gateways that are used.

Thus, these link domains involved in these attacks can be much more variable than the ones in traditional file storage attacks, where a centralized service with a single domain is used (e.g., Dropbox, Google Docs), making detecting the malicious use of IPFS extremely challenging for traditional security vendors. Through its anomaly-based approach to threat detection, Darktrace/Email™ is consistently able to identify such tactics and respond to them, preventing malicious actors from abusing file storage systems life IPFS.

IPFS Campaign Details

In several recent examples of IPFS abuse that Darktrace detected on a customer’s network, the apparent end goal was to harvest user credentials. Stolen credentials can be exploited by threat actors to further their attacks on organizations by escalating their privileges within the network, or even sold on the dark web.

Darktrace detected multiple IPFS links sent in malicious emails that contained the victim’s email address. Based on the domain in this email address, users would then be redirected to a fake login page that uses their organizations’ webpage visuals and branding to convince targets to enter their login details, unknowingly compromising their accounts in the process.

Figure 3: The credential harvester changes visuals depending on the victim’s email address specified in the URL.

These IPFS credential harvesting sites use various techniques to evade detection the detection of traditional security tools and prevent further analysis, such as obfuscation by Percent Encoding and Base64 Encoding the code.

There are also other mechanisms put into place to hinder investigation by security teams. For example, some IPFS credential harvester sites investigated by Darktrace did not allow right clicking and certain keystrokes, as a means to make post-attack analysis more difficult.

Figure 4: The code shows that it attempts to prevent certain keystrokes.

In the campaign highlighted in this blog, the following IPFS link was observed:

hxxps://ipfs[.]io/ipfs/QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP?filename=at ob.html#<EmailAddress>

This uses a Path Gateway, as it identifies the IPFS resource through a resource-identifying string in the path. The CID is QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP in this case.

It makes a GET request to image[.]thum[.]io and logo[.]clearbit[.]com as shown in Figure 5. The image[.]thum[.]io is a Free Website Screenshot Generator, that provides real-time screenshot of websites [2]. The logo[.]clearbit[.]com is used to lookup company logos using the domain [3]. These visuals are integrated into the credential harvester site. Figure 6 shows the domain name being extracted from the victim’s email address and used to obtain the visuals.

Figure 5: The GET requests to image[.]thum[.]io and logo[.]clearbit[.].
Figure 6: The code shows that it utilizes the domain name from the victim’s email address to obtain the visuals from logo.clearbit[.]com and image[.]thum.io.

The code reveals the credential POST endpoint as shown in Figure 16. When credentials are submitted, it makes a POST request to this endpoint as shown in Figure 7.

Figure 7: The credential POST endpoint can be seen inside the code.
Figure 8: The Outlook credential harvester will redirect to the real Outlook page when wrong credentials are submitted multiple times.

From the IPFS link alone, it is difficult to determine whether it leads to a malicious endpoint, however Darktrace has consistently identified emails containing these IPFS credential harvesting links as phishing attempts.

Darktrace Coverage

During one case of IPFS abuse detected by Darktrace in March 2023, a threat actor sent malicious emails with the subject “Renew Your E-mail Password” to 55 different recipients at. The sender appeared to be the organization’s administrator and used their internal domain.

Figure 9: Darktrace/Email’s detection of the “Renew Your E-mail Password” emails from “administrator”. These were all sent at 2023.03.21 02:39 UTC.

However, Darktrace recognized that the email did not pass Sender Policy Framework (SPF), and therefore it could not be validated as being sent from the organization’s domain. Darktrace also detected that the email contained a link to “ipfs.io, the official IPFS gateway. This was identified as a spoofing and phishing attempt by Darktrace/Email.

Figure 10: The Darktrace/Email overview tab shows the Anomaly Indicators, History, Association, and Validation information of this sender. It contained a link to “ipfs.io”, and did not pass SPF.

Following the successful identification of the malicious emails, Darktrace RESPOND™ took immediate autonomous action to prevent them from leading to potentially damaging network compromise. For email-based threats, Darktrace RESPOND is able to carry out numerous actions to stop malicious emails and reduce the risk of compromise. In response to this specific incident, RESPOND took multiple preventative actions (as seen in Figure 11), including include lock link, an action that prevents access to URLs deemed as suspicious, send to junk, an action that automatically places emails in the recipient’s junk folder, and hold message, the most severe RESPOND action that prevents malicious emails from reaching the recipients inbox at all.

Figure 11: The Darktrace/Email model tab shows all the models that triggered on the email and the associated RESPOND actions.
Figure 12: The ipfs.io link used in this email contains the recipient’s email address, and has a CID of QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP. It has a Darktrace Domain Rarity Score of 100
Figure 13: The IPFS credential harvester that uses the organization’s website’s visuals.

Further investigation revealed that the IPFS link contained the recipients’ email address, and when clicked led to a credential harvester that utilized the same visuals and branding as the customer’s website.

Concluding Thoughts

Ultimately, despite the various tactics employed threat actors to evade the detection of traditional security tools, Darktrace was able to successfully detect and mitigate these often very fruitful phishing attacks that attempted to abuse the IPFS file storage system.

As file storage platforms like IPFS do have legitimate business uses, blocking traffic related to file storage is likely to negatively impact the day-to-day operations of an organization. The challenge security teams face is to differentiate between malicious and legitimate uses of such services, and only act on malicious cases. As such, it is more important than ever for organizations to have an effective anomaly detection tool in place that is able to identify emerging threats without relying on rules, signatures or previously observed indicators of compromise (IoC).

By leveraging its Self-Learning AI, Darktrace understands what represents expected activity on customer networks and can recognize subtle deviations from expected behavior, that may be indicative of compromise. Then, using its autonomous response capabilities, Darktrace RESPOND is able to instantly and autonomously take action against emerging threats to stop them at the earliest possible stage.

Credit to Ben Atkins, Senior Model Developer for their contribution to this blog.

Appendices

Example IOCs

Type: URL

IOC: hxxps://ipfs[.]io/ipfs/QmfDDxLWoLi qFURX6dUZcsHxVBP1ZnM21H5jXGs

1ffNxtP?filename=atob.html#<Email Address>

Description: Path Gateway link

Type: URL

IOC: hxxps://bafybeibisyerwlu46re6rxrfw doo2ubvucw7yu6zjcfjmn7rqbwcix2 mku.ipfs[.]dweb.link/webn cpmk.htm?bafybeigh77sqswniy74nzyklybstfpkxhsqhpf3qt26nwnh4wf2vv gbdaybafybeigh77sqswniy74nzyklybstfpkxhsqhpf3qt26nwnh4wf2vvgbda y#<EmailAddress>

Description: Subdomain Gateway link

Relevant Darktrace DETECT Models

•       Spoof / Internal Domain from Unexpected Source + New Unknown Link

•       Link / High Risk Link + Low Sender Association

•       Link / New Correspondent Classified Link

•       Link / Watched Link Type

•       Proximity / Phishing + New activity

•       Proximity / Phishing + New Address Known Domain

•       Spoof / Internal Domain from Unexpected Source + High Risk Link

References

[1]    https://docs.ipfs.tech/

[2]    https://www.thum.io/

[3]    https://clearbit.com/logo

[4]    https://filebase.com/blog/ipfs-content-addressing-explained/

[5]    https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/the-attack-of-the-chameleon-phishing-page/

[6]    https://wiki.ipfsblox.com/

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
Lena Yu
Cyber Security Analyst

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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About the author
Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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