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December 21, 2020

How AI Stopped a WastedLocker Ransomware Intrusion & Fast

Stop WastedLocker ransomware in its tracks with Darktrace AI technology. Learn about how AI detected a recent attack using 'Living off the Land' techniques.
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
Max Heinemeyer
Global Field CISO
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21
Dec 2020

Since first being discovered in May 2020, WastedLocker has made quite a name for itself, quickly becoming an issue for businesses and cyber security firms around the world. WastedLocker is known for its sophisticated methods of obfuscation and steep ransom demands.

Its use of ‘Living off the Land’ techniques makes a WastedLocker attack extremely difficult for legacy security tools to detect. An ever-decreasing dwell time – the time between initial intrusion and final execution – means human responders alone struggle to contain the ransomware variant before damage is done.

This blog examines the anatomy of a WastedLocker intrusion that targeted a US agricultural organization in December. Darktrace’s AI detected and investigated the incident in real time, and we can see how Darktrace RESPOND would have autonomously taken action to stop the attack before encryption had begun.

As ransomware dwell time shrinks to hours rather than days, security teams are increasingly relying on artificial intelligence to stop threats from escalating at the earliest signs of compromise – containing attacks even when they strike at night or on the weekend.

How the WastedLocker attack unfolded

Figure 1: A timeline of the attack

Initial intrusion

The initial infection appears to have taken place when an employee was deceived into downloading a fake browser update. Darktrace AI was monitoring the behavior of around 5,000 devices at the organization, continuously adapting its understanding of the evolving ‘pattern of life’. It detected the first signs of a threat when a virtual desktop device started making HTTP and HTTPS connections to external destinations that were deemed unusual for the organization. The graph below depicts how the patient zero device exhibited a spike in internal connections around December 4.

Figure 2: The patient zero device exhibiting a spike in internal connections, with orange dots indicating model breaches of varying severity

Reconnaissance

Attempted reconnaissance began just 11 minutes after the initial intrusion. Again, Darktrace immediately picked up on the activity, detecting unusual ICMP ping scans and targeted address scans on ports 135, 139 and 445; presumably as the attacker looked for potential further Windows targets. The below demonstrates the scanning detections based on the unusual number of new failed connections.

Figure 3: Darktrace detecting an unusual number of failed connections

Lateral movement

The attacker used an existing administrative credential to authenticate against a Domain Controller, initiating new service control over SMB. Darktrace picked this up immediately, identifying it as unusual behavior.

Figure 4: Darktrace identifying the DCE-RPC requests
Figure 5: Darktrace surfacing the SMB writes

Several hours later – and in the early hours of the morning – the attacker used a temporary admin account ‘tempadmin’ to move to another Domain Controller over SMB. Darktrace instantly detected this as it was highly unusual to use a temporary admin account to connect from a virtual desktop to a Domain Controller.

Figure 6: Further anomalous connections detected the following day

Lock and load: WastedLocker prepares to strike

During the beaconing activity, the attacker also conducted internal reconnaissance and managed to establish successful administrative and remote connections to other internal devices by using tools already present. Soon after, a transfer of suspicious .csproj files was detected by Darktrace, and at least four other devices began exhibiting similar command and control (C2) communications.

However, with Darktrace’s real-time detections – and Cyber AI Analyst investigating and reporting on the incident in a number of minutes, the security team were able to contain the attack, taking the infected devices offline.

Automated investigations with Cyber AI Analyst

Darktrace’s Cyber AI Analyst launched an automatic investigation around every anomaly detection, forming hypotheses, asking questions about its own findings, and forming accurate answers at machine speed. It then generated high-level, intuitive incident summaries for the security team. Over the 48 hour period, the AI Analyst surfaced just six security incidents in total, with three of these directly relating to the WastedLocker intrusion.

Figure 7: The Cyber AI Analyst threat tray

The snapshot below shows a VMWare device (patient zero) making repeated external connections to rare destinations, scanning the network and using new admin credentials.

Figure 8: Cyber AI Analyst investigates

Darktrace RESPOND: AI that responds when the security team cannot

Darktrace RESPOND – the world’s first and only Autonomous Response technology – was configured in passive mode, meaning it did not actively interfere with the attack, but if we dive back into the Threat Visualizer we can see that Antigena in fully autonomous mode would have responded to the attack at this early stage, buying the security team valuable time.

In this case, after the initial unusual SSL C2 detection (based on a combination of destination rarity, JA3 unusualness and frequency analysis), RESPOND (formerly known as 'Antigena', as shown in the screenshots below) suggested instantly blocking the C2 traffic on port 443 and parallel internal scanning on port 135.

Figure 9: The Threat Visualizer reveals the action Antigena would have taken

When beaconing was later observed to bywce.payment.refinedwebs[.]com, this time over HTTP to /updateSoftwareVersion, RESPOND escalated its response by blocking the further C2 channels.

Figure 10: Antigena escalates its response

The vast majority of response tools rely on hard-coded, pre-defined rules, formulated as ‘If X, do Y’. This can lead to false positives that unnecessarily take devices offline and hamper productivity. Darktrace RESPOND's actions are proportionate, bespoke to the organization, and not created in advance. Darktrace Antigena autonomously chose what to block and the severity of the blocks based on the context of the intrusion, without a human pre-eminently hard-coding any commands or set responses.

Every response over the 48 hours was related to the incident – RESPOND did not try to take action on anything else during the intrusion period. It simply would have actioned a surgical response to contain the threat, while allowing the rest of the business to carry on as usual. There were a total of 59 actions throughout the incident time period – excluding the ‘Watched Domain Block’ actions shown below – which are used during incident response to proactively shut down C2 communication.

Figure 11: All Antigena action attempts during the intrusion period across the whole organization

RESPOND would have delivered those blocks via whatever integration is most suitable for the organization – whether that be Firewall integrations, NACL integrations or other native integrations. The technology would have blocked the malicious activity on the relevant ports and protocols for several hours – surgically interrupting the threat actors’ intrusion activity, thus preventing further escalation and giving the security team air cover.

Stopping WastedLocker ransomware before encryption ensues

This attack used many notable Tools, Techniques and Procedures (TTPs) to bypass signature-based tools. It took advantage of ‘Living off the Land’ techniques, including Windows Management Instrumentation (WMI), Powershell, and default admin credential use. Only one of the involved C2 domains had a single hit on Open Source Intelligence Lists (OSINT); the others were unknown at the time. The C2 was also encrypted with legitimate Thawte SSL Certificates.

For these reasons, it is plausible that without Darktrace in place, the ransomware would have been successful in encrypting files, preventing business operations at a critical time and possibly inflicting huge financial and reputational losses to the organization in question.

Darktrace’s AI detects and stops ransomware in its tracks without relying on threat intelligence. Ransomware has thrived this year, with attackers constantly coming up with new attack TTPs. However, the above threat find demonstrates that even targeted, sophisticated strains of ransomware can be stopped with AI technology.

Thanks to Darktrace analyst Signe Zaharka for her insights on the above threat find.

Learn more about Autonomous Response

Darktrace model detections:

  • Compliance / High Priority Compliance Model Breach
  • Compliance / Weak Active Directory Ticket Encryption
  • Anomalous Connection / Cisco Umbrella Block Page
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compliance / Default Credential Usage
  • Compromise / Suspicious TLS Beaconing To Rare External
  • Anomalous Server Activity / Rare External from Server
  • Device / Lateral Movement and C2 Activity
  • Compromise / SSL Beaconing to Rare Destination
  • Device / New or Uncommon WMI Activity
  • Compromise / Watched Domain
  • Antigena / Network / External Threat / Antigena Watched Domain Block
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Slow Beaconing Activity To External Rare
  • Device / Multiple Lateral Movement Model Breaches
  • Compromise / High Volume of Connections with Beacon Score
  • Device / Large Number of Model Breaches
  • Compromise / Beaconing Activity To External Rare
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Anomalous Connection / New or Uncommon Service Control
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Compromise / SSL or HTTP Beacon
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Compromise / Sustained SSL or HTTP Increase
  • Unusual Activity / Unusual Internal Connections
  • Device / ICMP Address Scan

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
Max Heinemeyer
Global Field CISO

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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Jamie Bali
Technical Author (AI) Developer

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May 21, 2026

Data Center Security: Improving Visibility and Threat Detection Across IT, OT, and IoT

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What is data center cybersecurity?

Much of the conversation surrounding the data center boom has focused on power generation, cooling efficiency and water resources, construction, and compute capacity. In addition, cybersecurity has quietly become one of the most critical operational concerns as modern data centers are becoming some of the most operationally complex networked environments.

The more connected data center environments become, the larger and more dynamic their attack surface grows. What makes data center security particularly challenging is that they no longer resemble traditional enterprise IT environments alone. Instead, they operate like critical infrastructure facilities

Challenges of securing data centers

What makes these environments complicated is that the technologies responsible for keeping them operational: power distribution, cooling systems, airflow management, environmental controls, surveillance, and physical access management, all rely heavily on Operational Technology (OT), Industrial IoT (IIoT), and IoT systems alongside traditional IT infrastructure.

Programmable logic controllers (PLCs), building management systems (BMS), energy management systems (EMS), surveillance cameras, access control platforms, virtualization infrastructure, engineering workstations, contractor laptops, and cloud-connected orchestration systems now coexist within the same environment. Many are connected through routable networks, managed remotely, and accessed by 3rd party OEMs or System Integrators.

Why modern data center infrastructure faces increasing cyber risk

The challenge is not simply that there are more devices. It is that these IT, OT and IOT systems and devices are now deeply interconnected in ways that blur the boundaries between operational and enterprise infrastructure.

OT systems responsible for cooling and power distribution communicate alongside enterprise IT infrastructure. IoT devices used for physical security sit adjacent to cloud-connected management platforms. Third-party vendors and contractors frequently require remote access to maintain operations and optimize performance. AI-driven automation platforms increasingly orchestrate workflows across multiple environments simultaneously.

Every additional connection improves efficiency and scalability, but every additional connection also creates new relationships between systems that adversaries may exploit.

How IT, OT, and IoT convergence expands the data center attack surface

Historically in critical infrastructure environments enterprise IT, and OT or industrial control systems ICS, have been often separated by a DMZ.

That separation has steadily disappeared in pursuit of efficiency and access to valuable data that lives within the OT networks such as how many widgets were produced today. This conceptually is commonly referred to as “IT OT convergence.”

Modern data centers increasingly depend on interconnected systems operating across multiple domains simultaneously and face a similar reality when it comes to IT OT convergence.  

This convergence creates efficiency and visibility benefits, but it also introduces structural security challenges that traditional approaches struggle to address.

Many of the OT systems were never originally designed with modern cybersecurity requirements in mind. OT devices often prioritize uptime and operational continuity over security controls. IoT and OT devices may have limited security hardening, are inconsistently patched, or insecure default configurations. Third-party connectivity introduces external dependencies that organizations do not fully control.

As environments converge the attack surface changes and grows, attackers may exploit weaker systems positioned adjacent to critical operations for initial access. For example, a compromised IoT device may provide access into broader infrastructure, or an exposed remote management interface may enable lateral movement into OT systems.  

For defenders, rather than forcing segmentation where it’s not possible, focus oversight and monitoring across interconnected systems and how this activity might create operational risk, gaining visibility across these systems will ensure better awareness of and protection across the cracks in your systems attackers look to exploit.

Why traditional data center security tools create visibility gaps

Many organizations still secure IT, OT, and IoT environments through separate tools, teams, and workflows. Historically, this made sense. The environments themselves were more isolated, and the operational priorities were different.

But convergence changes the nature of detection and response.

Modern attacks increasingly move across domains as lateral movement and discovery techniques are pervasive amongst all the most well-known attacks to have disrupted OT. Adversaries may gain access through phishing or credential compromise, establish persistence in IT systems, pivot into operational infrastructure, exploit unmanaged IoT devices, and move laterally across cloud-connected environments.

Viewed independently, many of these signals may appear low priority or disconnected.

An anomalous login attempt, unusual device communication, changes in network traffic patterns, or abnormal behavior from an industrial controller may not appear significant on their own. The problem emerges when these activities are part of a broader attack chain unfolding across multiple systems simultaneously.

Siloed security models struggle to correlate this activity effectively because they lack shared operational context. Security teams may see isolated indicators while missing the relationships between them.

This creates a fundamental visibility problem that has discursive effects across security teams, leading to analyst overload, tedious alert investigations, and slower response times.

The issue is not simply detecting threats faster. It is understanding how activity across IT, OT, IoT, cloud, and remote access systems relate to one another in real time before operational disruption occurs.

Security measures to safeguard modern data center infrastructure

Rule-based systems, predefined indicators, and signature-driven approaches remain useful for identifying known threats, but they are less effective at identifying subtle behavioral deviations, novel attack paths, insider activity, 3rd party supply chain exploitation or attacks that move across operational domains.  

Darktrace’s Self-Learning AI approach is designed to operate across converged IT, OT, IoT, and cloud environments. Using multiple layers of AI models, Darktrace solutions come together to achieve behavioral prediction, real-time threat detection and response, and incident investigation, all while empowering your security team with visibility and control.

Because the models are environment-specific, they can adapt across highly diverse infrastructure including operational technology, physical security systems, enterprise IT, cloud workloads, and third-party connectivity.

This enables organizations to build a more unified understanding of activity across the data center.

Unified visibility across interconnected environments

Darktrace provides visibility across IT, OT, IoT, and cloud systems through a centralized platform. Security teams and data center operators can maintain live asset inventories, monitor data flows, identify vulnerable or end-of-life systems, and better understand how interconnected infrastructure communicates across the environment.

This becomes increasingly important in environments where unmanaged devices, transient contractor systems, and third-party connectivity continuously alter operational conditions.

Threat detection, investigation, and response

Darktrace applies multiple AI models to identify anomalous activity that may indicate known threats, novel attacks, insider activity, or cross-domain compromise.

By understanding how devices and systems normally behave within the environment, Darktrace can identify subtle deviations that may otherwise remain undetected in siloed environments.

Its autonomous response capabilities can also help contain threats during their early stages before they escalate into operational disruption. Meanwhile, Cyber AI Analyst provides explainable AI-driven investigations that help security teams understand the relationships between events, systems, and users involved in potential incidents.

Proactive risk identification

As data center environments continue to evolve, organizations increasingly need to understand not only active threats, but also where structural weaknesses may exist across interconnected systems.

Through capabilities such as attack path modeling and behavioral risk analysis, Darktrace helps organizations prioritize remediation efforts and identify areas where operational exposure may increase over time.

This supports a more proactive security posture in environments where operational continuity is critical.

Securing the future of interconnected infrastructure

As data centers continue to scale in size, complexity, and operational importance, their reliance on interconnected IT, OT, IoT, cloud, and AI-driven systems will only deepen.

The challenge organizations face is no longer simply protecting individual devices or isolated environments. It is understanding how risk emerges across interconnected systems operating together and detecting threats to these systems in real time.

This is ultimately what makes modern data center security different from traditional enterprise security models. The operational dependencies are broader, the environments are more heterogeneous, and the consequences of disruption and intent of adversaries are more like those in the critical infrastructure space.

Securing these environments therefore requires more than fragmented visibility across disconnected tools. Organizations increasingly need unified approaches capable of understanding relationships across systems, detecting threats early, and responding before operational disruption spreads across critical infrastructure.

As the infrastructure powering the digital economy continues to evolve, cybersecurity resilience will become increasingly inseparable from operational resilience itself.

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About the author
Daniel Simonds
Director of Operational Technology
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