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May 9, 2018

Ransomware Tactics One Year After WannaCry

Learn about common infection vectors & how Darktrace Enterprise Immune System helps catch ransomware threats. Enhance your security strategy now.
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
Dave Palmer
Advisor
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09
May 2018

Ransomware continues to be one of the most serious and disruptive cyber threats. The business models, motivations, and infection techniques of emerging campaigns have diversified, and new strands of ransomware continue to outpace the release of decryption tools. By 2019, global ransomware damage costs are expected to surpass $11.5 billion per year.

The three most memorable ransomware campaigns of 2017 - Wannacry, NotPetya, and Bad Rabbit - were ground-breaking in their scope, spread, and destructive power, demonstrating that every business, industry, and country is a potential victim. Although the damage caused by these attacks highlighted the importance of good cyber hygiene, many companies have struggled to address even the most widely reported vulnerabilities. As prevention is better than cure, this article will discuss some of the most common infection vectors and how the Darktrace Enterprise Immune System can assist security teams in catching ransomware threats.

Motivations: financial gain or wreaking havoc?

Ransomware is traditionally linked with making a quick buck by getting the victim to pay a set fee to unlock encrypted files. The phenomenon of ransomware-as-a-service has made this easier than ever before, as it has allowed virtually anyone to purchase ever more potent ransomware distribution kits on the Dark Web. The recent growth in cryptocurrencies has also made maintaining anonymity much easier than before, resulting in a definite increase in financially motivated cyber-criminals.

Regrettably, the goal of ransomware is no longer just to make money. NotPetya and other campaigns such as Ordinypt were designed to purposefully destroy data instead. Even though NotPetya provided its victims with payment instructions, it had no way of identifying who had actually made the payment. The uncertainty surrounding the recovery of lost files and the possibility of being associated with funding malicious organizations have therefore deterred many victims from meeting the ransom demands.

No matter how much a business tries to safeguard their assets, incidents are inevitable, and ransom attacks are an increasingly likely choice of criminal action. But it is now possible to identify in-progress attacks and handle them before they become a crisis.

Case Study 1: Executable file download from a compromised website

Many prolific ransomware strands have been distributed by phishing emails, infected file downloads, compromised websites, malvertising, and exploit kits. In many cases, ransomware is often downloaded and installed without the victim’s knowledge. To illustrate the ransomware download mechanics, we will analyze the life-cycle of a GandCrab incident. In the case study detailed below, the Darktrace Enterprise Immune System flagged a customer device retrieving an executable file from a previously unmonitored location following a redirection from another rare site.

The file containing ransomware was downloaded from a website registered to a Polish domain. Shortly after downloading the file, the customer’s device began reaching out to two locations which had not been contacted by any other network devices, nomoreransom.bit and bleepingcomputer.bit. Both are command and control servers for GandCrab ransomware. Once contacted, the malicious virus proceeded to encrypt files on the SMB server, adding the .GDCB (GandCrab) extension as it moved through the folders.

The virus modified the original file extensions in the encryption process.

Within seconds of the virus appearing on the company’s network, the Darktrace Cyber Analyst team alerted the security team of the threat. Preventative action was then taken, which allowed the threat to be contained within a timely manner.

Case Study 2: Bruteforcing Remote Desktop Protocol access

In addition to devising clever ways of downloading ransomware onto victim’s machines, some hackers have turned to bruteforcing Remote Desktop Protocol (RDP) access instead (HC7 & Lockcrypt). Exposing Remote Desktop services to the Internet is risky, as attackers can force access into a network by guessing login information and remotely exploiting a range of possible vulnerabilities and administrative tools in order to infect other available machines.

In another particularly serious breach, Darktrace detected a series of suspicious activities indicating that a malicious actor had taken control of a key server and was using it as a pivot point in order to move laterally throughout the network and install Remote Access Tools (RATs) on multiple devices.

In the initial stage of the attack, the Darktrace Enterprise Immune System observed over 400,000 incoming connections on a port that was targeting devices with RDP turned on and immediately flagged the first signs of a bruteforce attack.

Over 400,000 incoming RDP from multiple rare external devices.

The attack was successful; a compromised server was then used to retrieve malware that granted backdoor access and scanned the network for devices with open RDP channel. The hacker subsequently tunneled through the intermediary, gained control over multiple other machines, and installed third-party remote access software to all available devices.

Although most RDP bruteforcing incidents the Darktrace Enterprise Immune System observes do not escalate this far, the Darktrace Cyber Analyst team are constantly flagging instances of publicly accessible remote management services. To prevent ransomware that specifically exploits insecure RDP configuration, businesses should move these critical services to a virtual private network. Moreover, with Darktrace Antigena, Darktrace’s autonomous response solution, businesses can benefit from an added layer of protection. In this case, it would have blocked any anomalous RDP connections to the server, thus preventing any lateral movement throughout the network.

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
Dave Palmer
Advisor

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO
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