ブログ
/
Network
/
November 27, 2023

Detecting PurpleFox Rootkit with Darktrace AI

The PurpleFox rootkit poses significant risks. Discover how Darktrace leveraged advanced techniques to combat this persistent cyber threat.
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
Piramol Krishnan
Cyber Security Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
27
Nov 2023

Versatile Malware: PurpleFox

As organizations and security teams across the world move to bolster their digital defenses against cyber threats, threats actors, in turn, are forced to adopt more sophisticated tactics, techniques and procedures (TTPs) to circumvent them. Rather than being static and predictable, malware strains are becoming increasingly versatile and therefore elusive to traditional security tools.

One such example is PurpleFox. First observed in 2018, PurpleFox is a combined fileless rootkit and backdoor trojan known to target Windows machines. PurpleFox is known for consistently adapting its functionalities over time, utilizing different infection vectors including known vulnerabilities (CVEs), fake Telegram installers, and phishing. It is also leveraged by other campaigns to deliver ransomware tools, spyware, and cryptocurrency mining malware. It is also widely known for using Microsoft Software Installer (MSI) files masquerading as other file types.

The Evolution of PurpleFox

The Original Strain

First reported in March 2018, PurpleFox was identified to be a trojan that drops itself onto Windows machines using an MSI installation package that alters registry values to replace a legitimate Windows system file [1]. The initial stage of infection relied on the third-party toolkit RIG Exploit Kit (EK). RIG EK is hosted on compromised or malicious websites and is dropped onto the unsuspecting system when they visit browse that site. The built-in Windows installer (MSIEXEC) is leveraged to run the installation package retrieved from the website. This, in turn, drops two files into the Windows directory – namely a malicious dynamic-link library (DLL) that acts as a loader, and the payload of the malware. After infection, PurpleFox is often used to retrieve and deploy other types of malware.  

Subsequent Variants

Since its initial discovery, PurpleFox has also been observed leveraging PowerShell to enable fileless infection and additional privilege escalation vulnerabilities to increase the likelihood of successful infection [2]. The PowerShell script had also been reported to be masquerading as a .jpg image file. PowerSploit modules are utilized to gain elevated privileges if the current user lacks administrator privileges. Once obtained, the script proceeds to retrieve and execute a malicious MSI package, also masquerading as an image file. As of 2020, PurpleFox no longer relied on the RIG EK for its delivery phase, instead spreading via the exploitation of the SMB protocol [3]. The malware would leverage the compromised systems as hosts for the PurpleFox payloads to facilitate its spread to other systems. This mode of infection can occur without any user action, akin to a worm.

The current iteration of PurpleFox reportedly uses brute-forcing of vulnerable services, such as SMB, to facilitate its spread over the network and escalate privileges. By scanning internet-facing Windows computers, PurpleFox exploits weak passwords for Windows user accounts through SMB, including administrative credentials to facilitate further privilege escalation.

Darktrace detection of PurpleFox

In July 2023, Darktrace observed an example of a PurpleFox infection on the network of a customer in the healthcare sector. This observation was a slightly different method of downloading the PurpleFox payload. An affected device was observed initiating a series of service control requests using DCE-RPC, instructing the device to make connections to a host of servers to download a malicious .PNG file, later confirmed to be the PurpleFox rootkit. The device was then observed carrying out worm-like activity to other external internet-facing servers, as well as scanning related subnets.

Darktrace DETECT™ was able to successfully identify and track this compromise across the cyber kill chain and ensure the customer was able to take swift remedial action to prevent the attack from escalating further.

While the customer in question did have Darktrace RESPOND™, it was configured in human confirmation mode, meaning any mitigative actions had to be manually applied by the customer’s security team. If RESPOND had been enabled in autonomous response mode at the time of the attack, it would have been able to take swift action against the compromise to contain it at the earliest instance.

Attack Overview

Figure 1: Timeline of PurpleFox malware kill chain.

Initial Scanning over SMB

On July 14, 2023, Darktrace detected the affected device scanning other internal devices on the customer’s network via port 445. The numerous connections were consistent with the aforementioned worm-like activity that has been reported from PurpleFox behavior as it appears to be targeting SMB services looking for open or vulnerable channels to exploit.

This initial scanning activity was detected by Darktrace DETECT, specifically through the model breach ‘Device / Suspicious SMB Scanning Activity’. Darktrace’s Cyber AI Analyst™ then launched an autonomous investigation into these internal connections and tied them into one larger-scale network reconnaissance incident, rather than a series of isolated connections.

Figure 2: Cyber AI Analyst technical details summarizing the initial scanning activity seen with the internal network scan over port 445.

As Darktrace RESPOND was configured in human confirmation mode, it was unable to autonomously block these internal connections. However, it did suggest blocking connections on port 445, which could have been manually applied by the customer’s security team.

Figure 3: The affected device’s Model Breach Event Log showing the initial scanning activity observed by Darktrace DETECT and the corresponding suggested RESPOND action.

Privilege Escalation

The device successfully logged in via NTLM with the credential, ‘administrator’. Darktrace recognized that the endpoint was external to the customer’s environment, indicating that the affected device was now being used to propagate the malware to other networks. Considering the lack of observed brute-force activity up to this point, the credentials for ‘administrator’ had likely been compromised prior to Darktrace’s deployment on the network, or outside of Darktrace’s purview via a phishing attack.

Exploitation

Darktrace then detected a series of service control requests over DCE-RPC using the credential ‘admin’ to make SVCCTL Create Service W Requests. A script was then observed where the controlled device is instructed to launch mshta.exe, a Windows-native binary designed to execute Microsoft HTML Application (HTA) files. This enables the execution of arbitrary script code, VBScript in this case.

Figure 4: PurpleFox remote service control activity captured by a Darktrace DETECT model breach.
Figure 5: The infected device’s Model Breach Event Log showing the anomalous service control activity being picked up by DETECT.

There are a few MSIEXEC flags to note:

  • /i : installs or configures a product
  • /Q : sets the user interface level. In this case, it is set to ‘No UI’, which is used for “quiet” execution, so no user interaction is required

Evidently, this was an attempt to evade detection by endpoint users as it is surreptitiously installed onto the system. This corresponds to the download of the rootkit that has previously been associated with PurpleFox. At this stage, the infected device continues to be leveraged as an attack device and scans SMB services over external endpoints. The device also appeared to attempt brute-forcing over NTLM using the same ‘administrator’ credential to these endpoints. This activity was identified by Darktrace DETECT which, if enabled in autonomous response mode would have instantly blocked similar outbound connections, thus preventing the spread of PurpleFox.

Figure 6: The infected device’s Model Breach Event Log showing the outbound activity corresponding to PurpleFox’s wormlike spread. This was caught by DETECT and the corresponding suggested RESPOND action.

Installation

On August 9, Darktrace observed the device making initial attempts to download a malicious .PNG file. This was a notable change in tactics from previously reported PurpleFox campaigns which had been observed utilizing .MOE files for their payloads [3]. The .MOE payloads are binary files that are more easily detected and blocked by traditional signatured-based security measures as they are not associated with known software. The ubiquity of .PNG files, especially on the web, make identifying and blacklisting the files significantly more difficult.

The first connection was made with the URI ‘/test.png’.  It was noted that the HTTP method here was HEAD, a method similar to GET requests except the server must not return a message-body in the response.

The metainformation contained in the HTTP headers in response to a HEAD request should be identical to the information sent in response to a GET request. This method is often used to test hypertext links for validity and recent modification. This is likely a way of checking if the server hosting the payload is still active. Avoiding connections that could possibly be detected by antivirus solutions can help keep this activity under-the-radar.

Figure 7: Packet Capture from an affected customer device showing the initial HTTP requests to the payload server.
Figure 8: Packet Capture showing the HTTP requests to download the payloads.

The server responds with a status code of 200 before the download begins. The HEAD request could be part of the attacker’s verification that the server is still running, and that the payload is available for download. The ‘/test.png’ HEAD request was sent twice, likely for double confirmation to begin the file transfer.

Figure 9: PCAP from the affected customer device showing the Windows Installer user-agent associated with the .PNG file download.

Subsequent analysis using a Packet Capture (PCAP) tool revealed that this connection used the Windows Installer user agent that has previously been associated with PurpleFox. The device then began to download a payload that was masquerading as a Microsoft Word document. The device was thus able to download the payload twice, from two separate endpoints.

By masquerading as a Microsoft Word file, the threat actor was likely attempting to evade the detection of the endpoint user and traditional security tools by passing off as an innocuous text document. Likewise, using a Windows Installer user agent would enable threat actors to bypass antivirus measures and disguise the malicious installation as legitimate download activity.  

Darktrace DETECT identified that these were masqueraded file downloads by correctly identifying the mismatch between the file extension and the true file type. Subsequently, AI Analyst was able to correctly identify the file type and deduced that this download was indicative of the device having been compromised.

In this case, the device attempted to download the payload from several different endpoints, many of which had low antivirus detection rates or open-source intelligence (OSINT) flags, highlighting the need to move beyond traditional signature-base detections.

Figure 10: Cyber AI Analyst technical details summarizing the downloads of the PurpleFox payload.
Figure 11 (a): The Model Breach generated by the masqueraded file transfer associated with the PurpleFox payload.
Figure 11 (b): The Model Breach generated by the masqueraded file transfer associated with the PurpleFox payload.

If Darktrace RESPOND was enabled in autonomous response mode at the time of the attack it would have acted by blocking connections to these suspicious endpoints, thus preventing the download of malicious files. However, as RESPOND was in human confirmation mode, RESPOND actions required manual application by the customer’s security team which unfortunately did not happen, as such the device was able to download the payloads.

Conclusion

The PurpleFox malware is a particularly dynamic strain known to continually evolve over time, utilizing a blend of old and new approaches to achieve its goals which is likely to muddy expectations on its behavior. By frequently employing new methods of attack, malicious actors are able to bypass traditional security tools that rely on signature-based detections and static lists of indicators of compromise (IoCs), necessitating a more sophisticated approach to threat detection.  

Darktrace DETECT’s Self-Learning AI enables it to confront adaptable and elusive threats like PurpleFox. By learning and understanding customer networks, it is able to discern normal network behavior and patterns of life, distinguishing expected activity from potential deviations. This anomaly-based approach to threat detection allows Darktrace to detect cyber threats as soon as they emerge.  

By combining DETECT with the autonomous response capabilities of RESPOND, Darktrace customers are able to effectively safeguard their digital environments and ensure that emerging threats can be identified and shut down at the earliest stage of the kill chain, regardless of the tactics employed by would-be attackers.

Credit to Piramol Krishnan, Cyber Analyst, Qing Hong Kwa, Senior Cyber Analyst & Deputy Team Lead, Singapore

Appendices

Darktrace Model Detections

  • Device / Increased External Connectivity
  • Device / Large Number of Connections to New Endpoints
  • Device / SMB Session Brute Force (Admin)
  • Compliance / External Windows Communications
  • Anomalous Connection / New or Uncommon Service Control
  • Compromise / Unusual SVCCTL Activity
  • Compromise / Rare Domain Pointing to Internal IP
  • Anomalous File / Masqueraded File Transfer

RESPOND Models

  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Antigena / Network / External Threat / Antigena File then New Outbound Block

List of IoCs

IoC - Type - Description

/C558B828.Png - URI - URI for Purple Fox Rootkit [4]

5b1de649f2bc4eb08f1d83f7ea052de5b8fe141f - File Hash - SHA1 hash of C558B828.Png file (Malware payload)

190.4.210[.]242 - IP - Purple Fox C2 Servers

218.4.170[.]236 - IP - IP for download of .PNG file (Malware payload)

180.169.1[.]220 - IP - IP for download of .PNG file (Malware payload)

103.94.108[.]114:10837 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

221.199.171[.]174:16543 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

61.222.155[.]49:14098 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

178.128.103[.]246:17880 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

222.134.99[.]132:12539 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

164.90.152[.]252:18075 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

198.199.80[.]121:11490 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)

MITRE ATT&CK Mapping

Tactic - Technique

Reconnaissance - Active Scanning T1595, Active Scanning: Scanning IP Blocks T1595.001, Active Scanning: Vulnerability Scanning T1595.002

Resource Development - Obtain Capabilities: Malware T1588.001

Initial Access, Defense Evasion, Persistence, Privilege Escalation - Valid Accounts: Default Accounts T1078.001

Initial Access - Drive-by Compromise T1189

Defense Evasion - Masquerading T1036

Credential Access - Brute Force T1110

Discovery - Network Service Discovery T1046

Command and Control - Proxy: External Proxy T1090.002

References

  1. https://blog.360totalsecurity.com/en/purple-fox-trojan-burst-out-globally-and-infected-more-than-30000-users/
  2. https://www.trendmicro.com/en_us/research/19/i/purple-fox-fileless-malware-with-rookit-component-delivered-by-rig-exploit-kit-now-abuses-powershell.html
  3. https://www.akamai.com/blog/security/purple-fox-rootkit-now-propagates-as-a-worm
  4. https://www.foregenix.com/blog/an-overview-on-purple-fox
  5. https://www.trendmicro.com/en_sg/research/21/j/purplefox-adds-new-backdoor-that-uses-websockets.html
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
Piramol Krishnan
Cyber Security Analyst

More in this series

No items found.

Blog

/

AI

/

May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

Default blog imageDefault blog image

Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

---

Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

Continue reading
About the author
Irving Bruckstein
CEO CyberAIgroup

Blog

/

AI

/

May 11, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

Default blog imageDefault blog image

Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

[related-resource]

Continue reading
About the author
Toby Lewis
Head of Threat Analysis
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ