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July 14, 2021

Darktrace Detects Egregor Ransomware in Customer Environment

See how Darktrace managed to detect and eliminate an Egregor ransomware extortion attack in a customer environment without the use of any signatures.
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
Justin Fier
SVP, Red Team Operations
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14
Jul 2021

Ransomware groups are coming and going faster than ever. In June alone we saw Avaddon release its decryption keys unprompted and disappear from sight, while members of CLOP were arrested in Ukraine. The move follows increasing pressure from the US intelligence community and Ukrainian authorities, who took down Egregor ransomware back in February. Egregor had only been around since September 2020. It survived less than six months.

But these gangs aren’t going away – they are simply going underground. Despite ‘closures’, cases of ransomware continue to rise and new threat actors and independent hackers pop up on the Dark Web every day.

As malware actors lay low and resurface with new variants, keeping up with the stream of signatures and new strains has become untenable. This blog studies the techniques, tools and procedures (TTPs) observed from a real-life Egregor intrusion last autumn, which showcases how Self-Learning AI detected the attack without relying on signatures.

Egregor: Maze reloaded

150 companies
worldwide have fallen victim to Egregor.

Law enforcement authorities have been busy this year. Aside from Egregor and CLOP, actions were taken against Netwalker in Bulgaria and the US, while Europol announced that an international operation had disrupted the core infrastructure of Emotet, one of the most prominent botnets of the past decade.

All parties – from governments down to individual businesses – are taking the threat of ransomware more seriously. In response to this added pressure, cyber-criminals often prefer to shut up shop rather than hang around long enough to be arrested.

DarkSide famously closed down after the Colonial Pipeline attacks, only nine months after it had been created. An admin from the Ziggy gang announced that it would issue refunds and was looking for a job as a threat hunter.

“Hi. I am Ziggy ransomware administrator. We decided to publish all decryption keys.

We are very sad about what we did. As soon as possible, all the keys will be published in this channel.”

Take this apology with a pinch of salt. The players which have ‘closed down’ have not had a change of heart, they’ve just changed tack. Different names and new infrastructure can help keep the heat off and circumvent US sanctions or federal scrutiny. PayloadBIN (a new ransomware which cropped up last month), WastedLocker, Dridex, Hades, Phoenix, Indrik Spider… all just aliases for one single group: Evil Corp.

The FBI are becoming more aggressive in their methods of infiltration and disruption, so it is likely we will see more of these U-turns and guerrilla-style tactics. Temporary pop-up gangs are an emerging trend in place of large, established enterprises like REvil, whose websites also vanished following the attack against Kaseya. And there is no doubt we will continue to witness these ‘exit scams’, where groups retire and re-brand, like Maze did last September, when it came back as Egregor.

Darktrace detects malware regardless of the name or strain. It stopped Maze last year, and, as we shall see below, it stopped its successor Egregor, even though the code and C2 endpoints used in the intrusion had never been seen before.

30%
of ransom profits are taken by Egregor developers.

Egregor ransomware attack

Back in November 2020, Egregor was in full bloom, targeting major organizations and exfiltrating data in ‘double extortion’ attacks. At a logistics company in Europe with around 20,000 active devices, during a Darktrace Proof of Value (POV) trial, Egregor struck.

Figure 1: Timeline of the attack. The overall dwell time — from first C2 connection to encryption — was five days.

As a Ransomware-as-a-Service (RaaS) gang, it appears Egregor had partnered with botnet providers to facilitate initial access. In this case, the compromised device carried signs of prior infection. It was seen connecting to an apparent Webex endpoint, before connecting to the Akamai doppelganger, amajai-technologies[.]network. This activity was followed by a number of command and control (C2) and exfiltration-related breaches.

Three days later, Darktrace observed lateral movement over HTTPS. Another device – a server – was seen connecting to the amajai host. This server wrote unusual numeric exectuables to shared SMB drives and took new service control. A third host then made a ~50GB upload to a rare IP.

Figure 2: Cyber AI Analyst summarizes the initial C2 and unusual SMB writes in a similar incident, followed later by a large upload to a rare external endpoint.

After two days, encryption began. This triggered multiple hosts breaches. On the final day, the attacker made large uploads to various endpoints, all from ostensibly compromised hosts.

Retrospective analysis

$4m
is the highest recorded cost of an Egregor ransom.

If the attack had not been neutralized at this point, it could have resulted in significant financial loss and reputational damage for the company. The two-pronged attack enabled Egregor both to encrypt critical resources and to exfiltrate them, with a view to publicizing sensitive data if the victims refused to pay up.

The affiliates who deployed the ransomware in this case were highly skilled. They leveraged a number of sophisticated techniques including the use of a large number of C2 endpoints, with doppelgangers and off-the-shelf tools.

The adoption of HTTPS for lateral movement and reconnaissance reduced lateral noise for scans and enumeration. The complex C2 had numerous endpoints, some of which were doppelgangers of legitimate sites. Furthermore, some malware was downloaded as masqueraded files: the mimetype Octet Streams were downloaded as ‘g.pixel’. These three tactics helped obfuscate the attacker’s movements and trick traditional security tools.

Ransomware attacks are occurring at a speed that even five years ago was unimaginable. In this case, the overall dwell time was less than a week, and part of the attack happened out of office hours. This highlights the need for Autonomous Response, which can keep up with novel threats and does not rely on humans being in the loop to contain cyber-attacks.

Gone today, here tomorrow

Egregor was busted in February, but we may well see it resurface under a different name and with modified code. If and when this happens, signatures will be of no use. Catching never-before-seen ransomware, which employs novel methods of intrusion and extortion, requires a different approach.

The endpoint in the case study above is now associated via open-source intelligence (OSINT) with Cobalt Strike. But at the time of the investigation, the C2 was unlisted. Similarly, the malware was unknown to OSINT and thus evaded signature-based tools.

Despite this, Self-Learning AI detected every single stage of the in-progress attack. No action was taken as it was only a trial POV so Darktrace had no remote access in the environment. However, after seeing the power of the technology, the organization decided to implement Darktrace across its digital estate.

Thanks to Darktrace analyst Roberto Romeu for his insights on the above threat find.

Learn how Darktrace stops Egregor and all forms of ransomware

Darktrace model detections:

  • Agent Beacon to New Endpoint
  • Agent Beacon (Long Period)
  • Agent Beacon (Medium Period)
  • Agent Beacon (Short Period)
  • Anomalous Octet Stream
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous SMB Followed By Multiple Model Breaches
  • Anomalous SSL without SNI to New External
  • Beaconing Activity To External Rare
  • Beacon to Young Endpoint
  • Data Sent To New External Device
  • Data Sent to Rare Domain
  • DGA Beacon
  • Empire Python Activity Pattern
  • EXE from Rare External Location
  • High Volume of Connections with Beacon Score
  • High Volume of New or Uncommon Service Control
  • HTTP Beaconing to Rare Destination
  • Large Number of Model Breaches
  • Long Agent Connection to New Endpoint
  • Low and Slow Exfiltration
  • Multiple C2 Model Breaches
  • Multiple Connections to New External TCP Port
  • Multiple Failed Connections to Rare Endpoint
  • Multiple Lateral Movement Model Breaches
  • Network Scan
  • New Failed External Connections
  • New or Uncommon Service Control
  • Numeric Exe in SMB Write
  • Rare External SSL Self-Signed
  • Slow Beaconing Activity To External Rare
  • SMB Drive Write
  • SMB Enumeration
  • SSL Beaconing to Rare Destination
  • SSL or HTTP Beacon
  • Suspicious Beaconing Behaviour
  • Suspicious Self-Signed SSL
  • Sustained SSL or HTTP Increase
  • Quick and Regular Windows HTTP Beaconing
  • Uncommon 1 GiB Outbound
  • Unusual BITS Activity
  • Unusual Internal Connections
  • Unusual SMB Version 1 Connectivity
  • Zip or Gzip from Rare External Location

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
Justin Fier
SVP, Red Team Operations

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May 12, 2026

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

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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.

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

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About the author
Irving Bruckstein
CEO CyberAIgroup

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May 11, 2026

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

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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

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About the author
Toby Lewis
Head of Threat Analysis
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