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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22
Jul 2020
Darktrace recently detected a case of industrial sabotage while deployed at a food-processing organization in the EMEA region. Like many more high-profile attack campaigns such as EKANS, Havex, and BlackEnergy, this attack started in the business’s IT infrastructure before pivoting to target the OT network.
Despite having a substantial OT network, the company was not aware of the extent of IT/OT convergence in their architecture. They initially chose to deploy Darktrace’s Enterprise Immune System, but not the Industrial Immune System assuming their OT systems were secure. As this attack moved from their IT systems and into OT, the additional visibility and ICS-specific models provided by Darktrace’s industrial offering would have delivered valuable additional context and further helped with threat remediation.
However, thanks to the Enterprise Immune System monitoring the events in real time, we can follow the threat as it moved through the corporate network over the span of three hours.
Timeline of incident
Figure 1: A timeline of events
Darktrace first detected a new device appearing in the “Computing” VLAN, which successfully connected to the “Industrial” VLAN using an admin RDP connection. The device then scanned the industrial network using OT ports 102 and 502, before appearing to call home to external locations using insecure HTTPS and making failed attempts to connect to external servers using OT port 502.
The device then appeared to make successful S7 and Modbus connections to other industrial devices, the nature of which could have been easily determined by the Industrial Immune System.
This new device was introduced directly onto the corporate network, bypassing traditional defenses that sit at the border. Any attempts made by the organization to segregate their IT and OT networks were insufficient in the face of the techniques used by the attacker.
Investigating at machine speed
Darktrace’s Cyber AI Analyst identified the breach device establishing a high volume of connections to unusual external IPs and transferring an unusually high volume of data with the internal WinCC server over port 3389. Simultaneously, the device was observed attempting to establish a high volume of internal connections over ports associated with ICS services. This activity suggests the breach device was conducting an internal scan.
Figure 2: A summary of the unusual data upload
Figure 3: A summary of the scanning activity
Figure 4: The device summary
The graph below details failed connections to external IP addresses made by the breach device when it joined the network (blue), and the mathematical importance of the activity (green), which reveals how statistically important this behavior is due to the size of its deviation from normal. Below that, Darktrace’s user interface surfaces every connection on the breach device over port 102.
Figure 5: The number of external connections made to closed ports
Figure 6: The Event Log for connections to S7 port 102 at the time of the incident
An immune system for industrial networks
Cross-examining and analyzing these multiple anomalies in real time, Darktrace identified this as a case of network reconnaissance. This is particularly suspicious as the device was only seen on the network for a two-day period. The unusual use of administrative credentials in the initial stages suggests the new device was attempting to control the WinCC Server, which allows Windows computers to communicate with industrial devices. Unauthorized access to this server could cause serious harm to the organization, as it would allow an attacker to learn about an industrial process, reconfigure multiple devices, or even fully sabotage the process.
The incident clearly demonstrates IT/OT convergence and the risks that entails, even – or especially – when businesses believe these systems are separate. Improper network segmentation makes ICS networks, particularly HMIs (human machine interfaces), an easy target for cyber-criminals or rogue insiders, making total visibility crucial in defending these systems.
This incident affirms that enterprise security needs to encompass OT security – the two can’t be treated as separate. The Industrial Immune System provides security analysts visibility across OT networks and subnets and defends against threats which might target industrial systems. Further, with AI learning the ‘pattern of life’ for every user, device, and controller, the technology can detect subtle deviations in behavior that evade other security tools, alerting security teams to potentially threatening activity in seconds. As attackers increasingly look to cause disruption and target industrial systems in their efforts, AI will be critical to keeping these systems secure and operational.
Thanks to Darktrace analyst Kendra Gonzalez Duran for her insights on the above threat find.
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.
Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems
Three shifts have reshaped what it means to defend an enterprise securely.
First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.
Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.
Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.
If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.
This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.
A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.
The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.
In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.
This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.
Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.
Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.
CVE
CVE Public Disclosure Date
Darktrace Detection Date
Days Between Detection of Exploitation and CVE Public Disclosure
CVE 2025 0994 (Trimble City Works)
2025-02-06
2025-01-19
18 Days
CVE 2025-24183 (Apache)
2025-03-10
2025-02-18
20 days
CVE 2025-10035 (Fortra GoAnywhere)
2025-09-18
2025-09-11
7 days
Identity is the real control plane
The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.
Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.
This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.
In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however, they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.
Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.
AI accelerates the threat
The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.
The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.
The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.
Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.
1. AI as an Attack Multiplier
In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].
Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.
What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.
Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.
AI as a trusted but dangerous actor
This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.
The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.
In these scenarios, the security challenge shifts from validating access to validating behavior.
This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.
Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.
Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.
The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.
For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.
Conclusion
Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.
In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.
Credit to: Daniel Levy, Threat Hunting Data Scientist
2026年6月12日、DarktraceはLiteLLM-Proxyという名前のAmazon Web Service (AWS) EC2インスタンスから暗号通貨マイニング発生中とみられるアクティビティを観測しました。このインスタンスはLiteLLMアクティビティをサポートしており、Amazon Bedrockリソースへのアクセス権を有するインスタンスプロファイルと関連付けられていました。
