Powering smarter protection for industrial systems

In industrial environments, security challenges are deeply operational. Whether you’re running a manufacturing line, a power grid, or a semiconductor fabrication facility (fab), you need to know: What risks can truly disrupt my operations, and what should I focus on first?

Teams need the right tools to shift from reactive defense, constantly putting out fires, to proactively thinking about their security posture. However, most OT teams are stuck using IT-centric tools that don’t speak the language of industrial systems, are consistently overwhelmed with static CVE lists, and offer no understanding of OT-specific protocols. The result? Compliance gaps, siloed insights, and risk models that don’t reflect real-world exposure, making risk prioritization seem like a luxury.

Darktrace / OT 6.3 was built in direct response to these challenges. Developed in close collaboration with OT operators and engineers, this release introduces powerful upgrades that deliver the context, visibility, and automation security teams need, without adding complexity. It’s everything OT defenders need to protect critical operations in one platform that understands the language of industrial systems.

Contextual risk modeling with smarter Risk Scoring

Darktrace / OT 6.3 introduces major upgrades to OT Risk Management, helping teams move beyond generic CVE lists with AI-driven risk scoring and attack path modeling.

By factoring in real-world exploitability, asset criticality, and operational context, this release delivers a more accurate view of what truly puts critical systems at risk.

The platform now integrates:

• CISA’s Known Exploited Vulnerabilities (KEV) database
• End-of-life status for legacy OT devices
• Firewall misconfiguration analysis
• Incident response plan alignment

Most OT environments are flooded with vulnerability data that lacks context. CVE scores often misrepresent risk by ignoring how threats move through the environment or whether assets are even reachable. Firewalls are frequently misconfigured or undocumented, and EOL (End of Life) devices, some of the most vulnerable, often go untracked.

Legacy tools treat these inputs in isolation. Darktrace unifies them, showing teams exactly which attack paths adversaries could exploit, mapped to the MITRE ATT&CK framework, with visibility into where legacy tech increases exposure.

The result: teams can finally focus on the risks that matter most to uptime, safety, and resilience without wasting resources on noise.

Automating compliance with dynamic IEC-62443 reporting

Darktrace / OT now includes a purpose-built IEC-62443-3-3 compliance module, giving industrial teams real-time visibility into their alignment with regulatory standards. No spreadsheets required!

Industrial environments are among the most heavily regulated. However, for many OT teams, staying compliant is still a manual, time-consuming process.

Darktrace / OT introduces a dedicated IEC-62443-3-3 module designed specifically for industrial environments. Security and operations teams can now map their security posture to IEC standards in real time, directly within the platform. The module automatically gathers evidence across all four security levels, flags non-compliance, and generates structured reports to support audit preparation, all in just a few clicks.Most organizations rely on spreadsheets or static tools to track compliance, without clear visibility into which controls meet standards like IEC-62443. The result is hidden gaps, resource-heavy audits, and slow remediation cycles.

Even dedicated compliance tools are often built for IT, require complex setup, and overlook the unique devices found in OT environments. This leaves teams stuck with fragmented reporting and limited assurance that their controls are actually aligned with regulatory expectations.

By automating compliance tracking, surfacing what matters most, and being purpose built for industrial environments, Darktrace / OT empowers organizations to reduce audit fatigue, eliminate blind spots, and focus resources where they’re needed most.

Expanding protocol visibility with deep insights for specialized OT operations

Darktrace has expanded its Deep Packet Inspection (DPI) capabilities to support five industry-specific protocols, across healthcare, semiconductor manufacturing, and ABB control systems.

The new protocols build on existing capabilities across all OT industry verticals and protocol types to ensure the Darktrace Self-Learning AI ^TM can learn intelligently about even more assets in complex industrial environments. By enabling native, AI-driven inspection of these protocols, Darktrace can identify both security threats and operational issues without relying on additional appliances or complex integrations.

Most security platforms lack native support for industry-specific protocols, creating critical visibility gaps in customer environments like healthcare, semiconductor manufacturing, and ABB-heavy industrial automation. Without deep protocol awareness, organizations struggle to accurately identify specialized OT and IoT assets, detect malicious activity concealed within proprietary protocol traffic, and generate reliable device risk profiles due to insufficient telemetry.

These blind spots result in incomplete asset inventories, and ultimately, flawed risk posture assessments which over-index for CVE patching and legacy equipment.

By combining protocol-aware detection with full-stack visibility across IT, OT, and IoT, Darktrace’s AI can correlate anomalies across domains. For example, connecting an anomaly from a Medical IoT (MIoT) device with suspicious behavior in IT systems, providing actionable, contextual insights other solutions often miss.

Conclusion

Together, these capabilities take OT security beyond alert noise and basic CVE matching, delivering continuous compliance, protocol-aware visibility, and actionable, prioritized risk insights, all inside a single, unified platform built for the realities of industrial environments.

[related-resource]

What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

• MITRE Attack
• Tactics, Techniques, Procedures (TTPs)
• Diamond Model for Intrusion Analysis
• Adversary, Infrastructure, Victims, Capabilities
• Threat Hunt Model – Six Steps
• Purpose, Scope, Equip, Plan, Execute, Feedback
• Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

• Hypothesis-based threat hunting
• Analytics-driven threat hunting
• Automated/machine learning hunting
• Indicator of Compromise (IoC) hunting
• Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

‍

References

1. https://spur.us/context/191.96.106.219

‍