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December 7, 2021

Conti Ransomware Strategies in Modern Cybercrime

Uncover the strategies behind the Conti ransomware gang's double extortion methods and what it means for businesses facing cyber threats.
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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07
Dec 2021

In a previous blog, we outlined how the Ryuk ransomware strain developed by Russian hacking group ‘Wizard Spider’ has fallen into the hands of small-time cyber criminals.

Wizard Spider – who allegedly operate with support from the Russian government and remain under investigation by the FBI and Interpol – adopted Ryuk ransomware’s successor ‘Conti’ in 2020. Conti affects all Windows operating systems and has been involved in more than 400 incidents. Wizard Spider were soon rebranded in cyber press as the ‘Conti Ransomware Gang’, though the group does not necessarily see itself as a ‘gang’. It prefers to present itself as a business.

The ransomware bubble

Ransomware has become a multibillion-dollar industry – and the Conti Ransomware Gang reportedly made up 15% of it in 2020. With this scale of income, groups like Conti find themselves adopting some crude imitations of legitimate business practice. This corporate mimicry dictates that their victims be called ‘customers’, their extortion attempts ‘negotiations’ and their criminal peers ‘affiliates’. They even publish ‘press releases’ via a dedicated Dark Web site.

The gang’s Ransomware-as-a-Service ‘business model’ consists of employing affiliates, training them in Conti ransomware’s deployment and management, and then taking 30% of the profits themselves. With exact profits known only to the malware writers and not the affiliates, however, the percentage Conti takes is often much higher than the 30% they claim.

There may not be checks and regulations in place to address fraud in the cyber underworld, but one business complication which Conti have not been able to escape is that of the disgruntled employee.

Unhappy with the malpractice of their superiors, an underpaid affiliate leaked the Conti Ransomware Gang’s training materials and the IP addresses for their Cobalt Strike C2 servers in August 2021, declaring, “they recruit suckers and divide the money among themselves”.

Meanwhile, the US Government has also been taking action to try to disrupt the profit margins of groups like the Conti Ransomware Gang, going as far as to impose sanctions on cryptocurrency exchanges seen as facilitating ransomware transactions. However, leaks and legislation have proved far from fatal for Conti.

The reality is that these actions have not lost the Conti Ransomware Gang any of its so-called “customers”, and where there are customers there is profit. Any individual or organization entrusting their cyber security to conventional, rules-based measures is in their target market.

Darktrace’s AI recently detected a Conti attack conducted along the lines of one of the methods outlined in the August leak. The target organization – a US transportation company – was trialing Darktrace but, without Darktrace’s Autonomous Response set in active mode, the attack was allowed to go ahead. In examining how it progressed, however, it should become clear not only how threatening double extortion ransomware attacks like this one can be, but also how effectively they can be stopped by Darktrace at each stage of the attack.

Figure 1: Timeline of the attack

Conti Ransomware Gang diversifies the ransomware playbook

A single uninstalled Microsoft patch had left the target organization with dangerous ProxyShell vulnerabilities. Conti exploited these vulnerabilities, quickly gaining the rights to remotely execute Exchange PowerShell commands on the company’s server and steadily broadened its presence within the digital environment. This is a relatively new approach for the Conti Ransomware Gang, who previously relied upon phishing attacks and firewall exploits. By diversifying its approach, it stays ahead of patches and intelligence.

Two weeks after the initial breach, C2 connections were made to an unusual endpoint located in Finland using an SSL client which appeared innocuous but was 100% rare for the organization. Had Autonomous Response been set in active mode, Darktrace would have shut the connections down at this very early stage.

The IP address of this suspicious endpoint has since been identified as a Conti IoC (Indicator of Compromise), allowing it to be incorporated into rules-based security solutions. This would have done little good for the company in question, however, which was breached weeks before this intelligence was made available.

As Conti continued to conduct internal reconnaissance and move laterally through the company’s digital environment, Darktrace detected further unusual activity. The suspicious Finnish endpoint then employed new ‘Living off the Land’ techniques, installing the usually legitimate tools AnyDesk and Cobalt Strike onto various parts of the environment.

A series of SSL connections were made to AnyDesk endpoints and external hosts, one of which lasted 95 hours, indicating an active remote session conducted by one of Conti’s affiliates. At this stage, Darktrace had 10 distinct reasons to suspect an imminent attack.

Conti News: Closing the deal with double extortion ransomware

Double extortion has become the Conti Ransomware Gang’s new favourite sales tactic. If you refuse to pay its ransom, Conti will not only take your most important files from you, but also exfiltrate and publish them using its dedicated ‘Conti News’ website, or sell them directly to your competitors.

Having expanded their reach across the transport company’s network, the Conti affiliate began rapidly exfiltrating large quantities of company data to Conti’s preferred cloud storage site, MEGA. Over four days, more than 3TB of data was uploaded, and then encrypted.

To avoid detection by a human security team, encryption was launched at close to midnight – Conti’s ‘business’ does not respect business hours. When the company’s security team returned to work the next day, they were met with a ransom note.

This attack was able to progress because Darktrace was only being trialed at this stage and was therefore allowed to detect threats but not to take action against them. With Autonomous Response employed in active mode, this ransomware attack would have ended in the very early stages, when Darktrace detected its first suspicious connections.

Nonetheless, the Cyber AI Analyst was able to investigate and connect the dots of the attack automatically, making the organization’s remediation efforts drastically quicker and easier than they would have been without even this partial Darktrace deployment.

Figure 2: Cyber AI Analyst generated this incident report following the initiation of data exfiltration

How the Conti Ransomware Gang evades cyber intelligence

Security systems that rely on human intelligence to detect threats fit Conti’s ideal customer profile perfectly. By adapting and diversifying their approach, moving from Ryuk to Conti, and from spear phishing and firewall exploits to this new ProxyShell approach, Conti stay ahead of regulations and hold on to their vulnerable customer base.

Even if the Conti Ransomware Gang is brought down by leaks or legislation, other groups will rise to fill the gap in the market, eager for their own cut of the illicit gains. If these groups are to be truly stopped, they must be made unprofitable.

The US government has tried to do this by imposing fines upon ransom payers, but companies still often consider the losses involved in not recovering their data too great. As I have argued previously, ‘to pay or not to pay,’ is not the question we should be asking.

If you’re deciding whether to pay or not to pay, you’re already too far down the line. Darktrace stops groups like Conti at the first encounter. As this case has shown, Darktrace’s Self-Learning AI is able to identify threats weeks before human analysts and threat intelligence can do the same, and neutralize them at every stage of an attack with Autonomous Response.

Thanks to Darktrace analyst Sam Lister for his insights on the above threat find.

Darktrace model detections:

  • Device / Long Agent Connection to New Endpoint
  • Device / ICMP Address Scan
  • Anomalous Connection / SMB Enumeration
  • Anomalous Server Activity / Outgoing from Server
  • Compromise / Beacon to Young Endpoint
  • Anomalous Server Activity / Rare External from Server
  • Compromise / Fast Beaconing to DGA
  • Compromise / SSL or HTTP Beacon
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Beacon for 4 Days
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / Suspicious Read Write Ratio
  • Anomalous Connection / Suspicious Read Write Ratio and Unusual SMB
  • Anomalous Connection / Sustained MIME Type Conversion
  • Unusual Activity / Anomalous SMB Move & Write
  • Unusual Activity / Unusual Internal Data Volume as Client or Server
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
  • Compromise / Ransomware / Suspicious SMB Activity
  • Anomalous File / Internal / Unusual SMB Script Write
  • Anomalous File / Internal / Masqueraded Executable SMB Write
  • Device / SMB Lateral Movement
  • Device / Multiple Lateral Movement Model Breaches

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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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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