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April 12, 2023

P2Pinfect - New Variant Targets MIPS Devices

A new P2Pinfect variant compiled for the Microprocessor without Interlocked Pipelined Stages (MIPS) architecture has been discovered. This demonstrates increased targeting of routers, Internet of Things (IoT) and other embedded devices by those behind P2Pinfect.
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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.
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Apr 2023

Introduction: P2PInfect

Since July 2023, researchers at Cado Security Labs (now part of Darktrace) have been monitoring and reporting on the rapid growth of a cross-platform botnet, named “P2Pinfect”. As the name suggests, the malware - written in Rust - acts as a botnet agent, connecting infected hosts in a peer-to-peer topology. In early samples, the malware exploited Redis for initial access - a relatively common technique in cloud environments. 

There are a number of methods for exploiting Redis servers, several of which appear to be utilized by P2Pinfect. These include exploitation of CVE-2022-0543[1] - a sandbox escape vulnerability in the LUA scripting language (reported by Unit42 [2]), and, as reported previously by Cado Security Labs, an unauthorized replication attack resulting in the loading of a malicious Redis module.  

Researchers have since encountered a new variant of the malware, specifically targeting embedded devices based on 32-bit MIPS processors, and attempting to brute force SSH access to these devices. It’s highly likely that by targeting MIPS, the P2Pinfect developers intend to infect routers and IoT devices with the malware. Use of MIPS processors is common for embedded devices and the architecture has been previously targeted by botnet malware, including high-profile families like Mirai [3], and its variants/derivatives.

Not only is this an interesting development in that it demonstrates a widening of scope for the developers behind P2Pinfect (more supported processor architectures equals more nodes in the botnet itself), but the MIPS32 sample includes some notable defense evasion techniques. 

This, combined with the malware’s utilization of Rust (aiding cross-platform development) and rapid growth of the botnet itself, reinforces previous suggestions that this campaign is being conducted by a sophisticated threat actor.

Initial access

Cado researchers encountered the MIPS variant of P2Pinfect after triaging files uploaded via SFTP and SCP to a SSH honeypot. Although earlier variants had been observed scanning for SSH servers, and attempting to propagate the malware via SSH as part of its worming procedure, researchers had yet to observe successful implantation of a P2Pinfect sample using this method - until now.

In keeping with similar botnet families, P2Pinfect includes a number of common username/password pairs embedded within the MIPS binary itself. The malware will then iterate through these pairs, initiating a SSH connection with servers identified during the scanning phase to conduct a brute force attack. 

It was assumed that SSH would be the primary method of propagation for the MIPS variant, due to routers and other embedded devices being more likely to utilize SSH. However, additional research shows that it is in fact possible to run the Redis server on MIPS. This is achievable via an OpenWRT package named redis-server. [4]

It is unclear what use-case running Redis on an embedded MIPS device solves, or whether it is commonly encountered in the wild. If such a device is compromised by P2Pinfect and has the Redis-server package installed, it is perfectly feasible for that node to then be used to compromise new peers via one of the reported P2Pinfect attack patterns, involving exploitation of Redis or SSH brute-forcing.

Static analysis

The MIPS variant of P2Pinfect is a 32-bit, statically-linked, ELF binary with stripped debug information. Basic static analysis revealed the presence of an additional ELF executable, along with a 32-bit Windows DLL in the PE32 format - more on this later. 

This piqued the interest of Cado analysts, as it is unusual to encounter a compiled ELF with an embedded DLL. Consequently, it was a defining feature of the original P2Pinfect samples.

Embedded Windows PE32 executable
Figure 1: Embedded Windows PE32 executable

Further analysis of the host executable revealed a structure named “BotnetConf” with members consistent in naming with the original P2Pinfect samples. 

Example of a partially populated version of the BotnetConf struct 
Figure 2: Example of a partially populated version of the BotnetConf struct 

As the name suggests, this structure defines the configuration of the malware itself, whilst also storing the IP addresses of nodes identified during the SSH and Redis scans. This, in combination with the embedded ELF and DLL, along with the use of the Rust programming language allowed for positive attribution of this sample to the P2Pinfect family.

Updated evasion - consulting tracerpid

One of the more interesting aspects of the MIPS sample was the inclusion of a new evasion technique. Shortly after execution, the sample calls fork() to spawn a child process. 

The child process then proceeds to access /proc using openat(), determines its own Process Identifier (PID) using the Linux getpid() syscall, and then uses this PID to consult the relevant /proc subdirectory and read the status file within that. Note that this is likely achieved in the source code by resolving the symbolic link at /proc/self/status.

Example contents of /proc/pid/status when process not being traced
Figure 3: Example contents of /proc/pid/status when process not being traced

/proc/<pid>/status contains human-readable metadata and other information about the process itself, including memory usage and the name of the command currently being run. Importantly, the status file also contains a field TracerPID:. This field is assigned a value of 0 if the current process is not being traced by dynamic analysis tools, such as strace and ltrace.

Example MIPS disassembly showing reading of /proc/pid/status file
Figure 4: Example MIPS disassembly showing reading of /proc/pid/status file

If this value is non-zero, the MIPS variant of P2Pinfect determines that it is being analyzed and will immediately terminate both the child process and its parent. 

read(5, "Name:\tmips_embedded_p\nUmask:\t002", 32) = 32 
read(5, "2\nState:\tR (running)\nTgid:\t975\nN", 32) = 32 
read(5, "gid:\t0\nPid:\t975\nPPid:\t1\nTracerPid:\t971\nUid:\t0\t0\t0\t0\nGid:\t0\t0\t0\t0", 64) = 64 
read(5, "\nFDSize:\t32\nGroups:\t0 \nNStgid:\t975\nNSpid:\t975\nNSpgid:\t975\nNSsid:\t975\nVmPeak:\t    3200 kB\nVmSize:\t    3192 kB\nVmLck:\t       0 kB\n", 128) = 128 
read(5, "VmPin:\t       0 kB\nVmHWM:\t    1564 kB\nVmRSS:\t    1560 kB\nRssAnon:\t      60 kB\nRssFile:\t    1500 kB\nRssShmem:\t       0 kB\nVmData:\t     108 kB\nVmStk:\t     132 kB\nVmExe:\t    2932 kB\nVmLib:\t       8 kB\nVmPTE:\t      16 kB\nVmSwap:\t       0 kB\nCoreDumping:\t0\nThre", 256) = 256 
mmap2(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77ff1000 
read(5, "ads:\t1\nSigQ:\t0/1749\nSigPnd:\t00000000000000000000000000000000\nShdPnd:\t00000000000000000000000000000000\nSigBlk:\t00000000000000000000000000000000\nSigIgn:\t00000000000000000000000000001000\nSigCgt:\t00000000000000000000000000000600\nCapInh:\t0000000000000000\nCapPrm:\t0000003fffffffff\nCapEff:\t0000003fffffffff\nCapBnd:\t0000003fffffffff\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t0\nSpeculation_Store_Bypass:\tunknown\nCpus_allowed:\t1\nCpus_allowed_list:\t0\nMems_allowed:\t1\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t92\nn", 512) = 512 
mmap2(NULL, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77fef000 
munmap(0x77ff1000, 4096)                = 0 
read(5, "onvoluntary_ctxt_switches:\t0\n", 1024) = 29 
read(5, "", 995)                        = 0 
close(5)                                = 0 
munmap(0x77fef000, 8192)                = 0 
sigaltstack({ss_sp=NULL, ss_flags=SS_DISABLE, ss_size=8192}, NULL) = 0 
munmap(0x77ff4000, 12288)               = 0 
exit_group(-101)                        = ? 
+++ exited with 155 +++ 

Strace output demonstrating TracerPid evasion technique

Updated evasion - disabling core dumps

Interestingly, the sample will also attempt to disable Linux core dumps. This is likely used as an anti-forensics procedure as the memory regions written to disk as part of the core dump can often contain internal information about the malware itself. In the case of P2Pinfect, this would likely include information such as IP addresses of connected peers and the populated BotnetConf structure mentioned previously. 

It is also possible that the sample prevents core dumps from being created to protect the availability of the MIPS device itself. Low-powered embedded devices are unlikely to have much local storage available and core dumps could quickly fill what little storage they do have, affecting performance of the device itself.

A screen shot of a computer codeAI-generated content may be incorrect.
Image 5

This procedure can be observed during dynamic analysis, with the binary utilising the prctl() syscall and passing the parameters PR_SET_DUMPABLE, SUID_DUMP_DISABLE.

munmap(0x77ff1000, 4096)                = 0 
prctl(PR_SET_DUMPABLE, SUID_DUMP_DISABLE) = 0 
prlimit64(0, RLIMIT_CORE, {rlim_cur=0, rlim_max=0}, NULL) = 0 

Example strace output demonstrating disabling of core dumps

Embedded DLL

As mentioned in the Static Analysis section, the MIPS variant of P2Pinfect includes an embedded 64-bit Windows DLL. This DLL acts as a malicious loadable module for Redis, implementing the system.exec functionality to allow the running of shell commands on a compromised host.

Disassembly of the Redis module entrypoint
Figure 6: Disassembly of the Redis module entrypoint, mapping the system.exec command to a handler

This is consistent with the previous examples of P2Pinfect, and demonstrates that the intention is to utilize MIPS devices for the Redis-specific initial access attack patterns mentioned throughout this blog. 

Interestingly, this embedded DLL also includes a Virtual Machine (VM) evasion function, demonstrating the lengths that the P2Pinfect developers have taken to hinder the analysis process. In the DLLs main function, a call can be observed to a function helpfully labelled anti_vm by IDAs Lumina feature.

Decompiler output showing call to anti_vm function
Figure 7: Decompiler output showing call to anti_vm function

Viewing the function itself, it can be seen that researchers Christopher Gardner and Moritz Raabe have identified it as a known VM evasion method in other malware samples.

IDA’s graph view for the anti_vm function showing Lumina annotations
Figure 8: IDA’s graph view for the anti_vm function showing Lumina annotations

Conclusion

P2Pinfect’s continued evolution and broadened targeting appear to be the utilization of a variety of evasion techniques demonstrate an above-average level of sophistication when it comes to malware development. This is a botnet that will continue to grow until it’s properly utilized by its operators. 

While much of the functionality of the MIPS variant is consistent with the previous variants of this malware, the developer’s efforts in making both the host and embedded executables as evasive as possible show a continued commitment to complicating the analysis procedure. The use of anti-forensics measures such as the disabling of core dumps on Linux systems also supports this.

Indicators of compromise (IoCs)

Files SHA256

MIPS ELF 8b704d6334e59475a578d627ae4bcb9c1d6987635089790350c92eafc28f5a6c

Embedded DLL Redis Module  d75d2c560126080f138b9c78ac1038ff2e7147d156d1728541501bc801b6662f

References:

[1] https://nvd.nist.gov/vuln/detail/CVE-2022-0543

[2] https://unit42.paloaltonetworks.com/peer-to-peer-worm-p2pinfect/

[3] https://unit42.paloaltonetworks.com/mirai-variant-iz1h9/

[4] https://openwrt.org/packages/pkgdata/redis-server

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
The Darktrace Community

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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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June 29, 2026

How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

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How Darktrace Transformed Cybersecurity at Our Health Center: A CIO’s Perspective

In my role as CIO, I bring years of experience leading IT for healthcare organizations. I’ve seen firsthand the unique cybersecurity challenges that nonprofit health centers face: limited budgets, small IT teams, and the constant pressure to prioritize patient care over technology investments. Yet, the threat landscape for health is relentless, and the stakes for protecting patient data and ensuring operational continuity have never been higher. It’s a balancing act.

The search for a better solution

Like many nonprofits, organizations I work at start with Microsoft’s security stack. The discounted pricing for nonprofits makes it an obvious choice, and Microsoft Defender provided a solid foundation for endpoint and email security. However, I quickly realized that relying on a single vendor, even one as robust as Microsoft, left gaps in our defenses. Cybersecurity is never one-size-fits-all, which is why my preference was to layer an additional solution on top of our native security to improve our security posture.

Teams needed a solution that could layer seamlessly on top of Microsoft, without adding complexity or draining limited resources. That’s when I found Darktrace. I had heard of their reputation after seeing how other organizations used Darktrace to secure their infrastructure and was impressed by their AI-native, agentless approach and agreed to a proof of value (POV).

Our goal was to elavate Microsoft with an additional layer of intelligence- one that could seamlessly integrate, operate autonomously, and support a small team without increasing overhead. We turned to Darktrace because its AI-native, agentless approach offered a fundamentally different way to detect and respond to threats, learning our environment in real time and filling gaps that traditional tools can miss. With a quick POV, we were able to validate how effectively Darktrace works alongside Microsoft to deliver a more complete and resilient security architecture.

Why Darktrace stood out

From the start, Darktrace differentiated itself in several critical ways:

  • Deep visibility: Unlike other solutions that rely simply on host-based monitoring with endpoint agents, Darktrace operates passively at the network layer and integrates via APIs for email and identity security. This gave full visibility into network traffic that we previously didn’t have, going beyond our existing endpoint-based tools without adding additional maintenance overhead for our small IT team.
  • AI-native from the ground up: Darktrace wasn’t just layering AI on top of an existing product; it was built with AI at its core. Their autonomous detection and response to threats immediately reduced the need for constant human supervision. In a world where cyber-attacks are increasingly sophisticated and subtle, having an AI that learns our environment and adapts in real time is invaluable.
  • Comprehensive coverage: We started with a POV focused on email security, but quickly expanded to full deployment across our entire infrastructure. Darktrace’s products now protect our email, network, and identity layers, providing visibility and defense against lateral movement and abnormal behavior that traditional tools often miss.

Integration and workflow: Smooth and simple

One of the most impressive aspects of Darktrace is how easy it was to integrate into an existing environment. For network security, it was as simple as plugging an appliance into our top-of-rack switch – no downtime, no complex configuration. For email and identity, API integrations meant we could be up and running in hours, not weeks.

This simplicity extended to day-to-day operations. Our IT team received regular security reports, and any time we had questions or needed to adjust policies, Darktrace’s support team was there with white-glove service. Their responsiveness- even in the middle of the night- gave us confidence that we had true partners, not just a vendor.

Real-world impact: Threats stopped, time saved

The results spoke for themselves. During the time with Darktrace, I did not experience any security incidents. The team slept better at night knowing that Darktrace was monitoring for anomalies and proactively blocking suspicious activity, alerting us even before we noticed anything was wrong.

A memorable example was during an Electronic Health Record (EHR) upgrade, when my team forgot to adjust the policy in advance. Darktrace’s autonomous response was so effective that it blocked our upgrade activities- proof that nothing, not even internal changes, could slip by unnoticed. This level of vigilance meant that ransomware, data exfiltration attempts, or insider threats would be detected and contained before causing harm.

While I can’t share specific ROI numbers, the value was clear: we’ve avoided costly breaches, reduced the time spent investigating alerts, and eliminated the performance drag of agent-based tools. With Darktrace layered on top of Microsoft, I’ve hit the right balance of maximum protection with minimal spending. The cost of Darktrace / EMAIL was competitive, especially when factoring in the included Managed Detection and Response (MDR) service, which provides expert human oversight on top of the AI.

Key differentiators over the competition

  • Extending visibility beyond the endpoint: Traditional host-based monitoring solutions, such as EDR, play a critical role in securing individual devices. By adding a network detection and response (NDR) layer, we gained visibility into activity across our wider digital environment, surfacing threats that move laterally, operate between devices, or bypass endpoint controls. Darktrace also stood out for its ability to learn our normal patterns of behavior and identify subtle deviations in real time, not just known indicators of compromise. Because this is delivered through passive, non-disruptive monitoring, we were able to strengthen our defenses without adding complexity or impacting performance.
  • Layered security without complexity: Darktrace elevated our Microsoft foundation without creating conflicts or requiring us to disable existing protections. This layered approach maximized our security posture without adding operational burden.
  • Expert partnership: Beyond technology, Darktrace’s team acted as true partners, guiding us through deployment, providing ongoing support, and helping us interpret findings. This partnership was as valuable as the technology itself.

Advice for other nonprofits

If you’re an IT leader in a nonprofit, my advice is simple: look for solutions that are easy to deploy, intelligent in their response, and cost-effective. Don’t settle for more endpoint based tools that overlap with what you already have. Seek out a layered approach that covers your blind spots – especially at the network and email layers- at a price point that suits your organization.

Most importantly, don’t be afraid to evaluate new solutions. Even if you’re inundated with vendor pitches, you owe it to your organization to explore options that could save you time, money, and sleepless nights.

For organizations I work at, combining Microsoft’s security stack with Darktrace’s AI-native, platform struck the right balance between protection and practicality. We gained enterprise-grade security without sacrificing performance or stretching our budget. In the end, that meant more resources for what matters most: delivering care to our patients. If you’re facing similar challenges, I encourage you to consider how Darktrace could transform your security posture, and give your team the peace of mind they deserve.

For the organization I work in, combining Microsoft with Darktrace delivered a clear step-change in our security posture. Microsoft provided the foundation, while Darktrace’s behavioral intelligence added visibility into the unknown, surfacing emerging threats based on deviations in real-time activity, not just known indicators.

The result was enterprise-grade protection without added overhead, allowing us to stay focused on patient outcomes, not security operations. For organizations facing similar pressures, this layered approach offers a smarter, more efficient path to securing modern environments.

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About the author
Mice Chen
Chief Information Security Officer
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