Increasing cyber threats and attacks have led modern organizations to focus on OT network monitoring, as it has emerged as a line of defense against cyber attackers. With the level of threats rising, the role of monitoring in OT (operational technology) cybersecurity can’t be overstated. Efficient monitoring provides organizations with critical visibility into the network traffic to detect anomalies, signs of cyber attacks, or equipment failure. This is even more critical across process control and ICS (industrial control systems) environments because failures here can be catastrophic.
Advanced OT network monitoring is changing ICS security with the use of adaptive tools like artificial intelligence (AI) and machine learning (ML). These technologies create behavioral baselines so you can detect anomalies in real time and react to threats quickly. Through continuous learning and adaptation, AI-based systems improve threat detection accuracy, reduce false positives, and ensure business continuity.
Threat intelligence combined with OT network monitoring adds more defenses. Proactive measures like predictive analytics and threat hunting let you safeguard your environment better. As OT networks are getting more and more attacked by ransomware and state-sponsored threats, planning for the future can’t be ignored. Spending on the latest monitoring technologies, encouraging IT and OT teams to work together, and adopting a security-first culture will be the recipe for securing the critical infrastructure in a world of more connected devices.
Critical role of network monitoring in OT cybersecurity
Industrial Cyber reached out to industrial cybersecurity experts to analyze how threats targeting OT networks have evolved in recent years and what role network monitoring plays in mitigating these risks.
Jeffrey Macre, industrial security solutions architect at Darktrace, told Industrial Cyber that threats targeting OT networks have significantly evolved, with a notable increase in targeting from Advanced Persistent Threat (APT) groups, which have become more sophisticated and skilled in their attacks. “Additionally, the creation of modular framework attacks has emerged, allowing attackers to reuse these frameworks to target a wide range of Industrial Control Systems (ICS) devices.”
Recent data found that the emergence of new OT/ICS native malware alongside the continued prevalence of ransomware groups targeting vulnerable industries poses increasing threats to OT networks.
“Network monitoring plays a crucial role in mitigating these risks by providing continuous visibility into network activities, detecting anomalies, and enabling rapid response to potential threats,” Macre said. “By monitoring network traffic and behavior, organizations can identify and address threats before they impact the systems, thereby enhancing the overall security of OT networks.”
“Major digital transformation trends such as smart manufacturing, machine analytics, remote operations, and SCADA modernization have reshaped OT networks from isolated air-gapped systems to dynamic hybrid environments with increased IT and cloud connectivity and sanctioned remote access,” Qiang Huang, vice president of product management at Palo Alto Networks, told Industrial Cyber. “This shift has significantly expanded the OT attack surface, with IIoT breaking traditional security models like Purdue. Meanwhile, adversaries are leveraging AI-driven polymorphic attacks that are faster, more scalable, and harder to detect.”
As these threats evolve, Huang added that network and security monitoring play an increasingly important role in continuously understanding OT asset inventory, communication patterns, and risks. “However, fragmented security tools and a lack of comprehensive coverage still create blind spots. Strengthening network monitoring capabilities helps bridge these gaps, improving real-time visibility, reducing risk, and enhancing response capabilities.”
Carlos Buenaño, chief technology officer for OT at Armis, said that threats targeting OT networks have significantly evolved over the past few years. “Historically, OT networks, which manage industrial operations such as manufacturing and utilities, were relatively isolated and operated in silos, making them less attractive targets for cyberattacks. However, with the rise of the industrial Internet of Things (IIoT) and the convergence of OT with IT networks, vulnerabilities have become more evident, allowing attackers to exploit these systems more easily.”
“Threats have transitioned from basic malware to highly targeted attacks, including ransomware, Advanced Persistent Threats (APTs) and zero-day exploits specifically aimed at compromising critical infrastructure,” Buenaño told Industrial Cyber. “Attackers are now employing more sophisticated techniques such as social engineering and supply chain attacks to gain initial access to OT environments. Also, the increased demand for remote access due to operational needs has further exposed OT networks to potential breaches. In this context, network monitoring plays a crucial role in mitigating these risks.”
He added that continuous network monitoring allows OT and security teams to detect anomalous behaviors and vulnerabilities in real time, enabling quick responses to potential threats. By utilizing advanced analytics, artificial intelligence (AI), and machine learning (ML), OT and security teams can enhance their visibility into OT environments, identifying suspicious activities that deviate from established baselines.
Daniel dos Santos, head of security research at Forescout, told Industrial Cyber that what was once the exclusive domain of state-sponsored actors deploying highly sophisticated OT-specific malware (e.g., Stuxnet, Industroyer, Triton), now includes cybercriminals’ ransomware and opportunistic attacks from hacktivists and botnets. “While attack volume has increased, targeting and sophistication has declined.”
He added that attacks almost always leave traces – whether during initial access, lateral movement, or impact – making network monitoring critical to defense. “Indications include exploiting known vulnerabilities, identifying noisy traffic from network scanners, communicating with suspicious IP addresses hosting a C2 server, executing risky firmware updates, and changing process parameters.”
Grant Geyer, Claroty’s chief strategy officer, told Industrial Cyber that the two primary threats that have impacted OT networks recently are nation-state-sponsored hacking efforts and ransomware, and each leverages different methods with unique objectives. “In both cases, monitoring at the networking and endpoint can serve to provide indications that an attacker is attempting to hack into the environment. As the TTPs of the attack types vary, it’s critical to establish a resilient security monitoring approach that leverages a variety of telemetry sources and intelligence.”
Geyer added, “this approach of a hacker side-steps a cyber tripwire, you can catch them with other cyber detection methods. The goal is to create enough tripwires, pressure plates, and motion detectors in the digital world that there is always some means of finding an anomaly and acting on it.”
Revolutionizing ICS security with OT network monitoring
The executives examine the most important technological advancements in OT network monitoring and explore how these innovations tackle the specific challenges faced by ICS environments.
Macre said that one of the most notable advancements is the application of multiple different types of AI, including supervised and unsupervised machine learning, for network monitoring. “These techniques have drastically improved the understanding and patterning of ICS device communications, helping to establish a pattern of normal activity. This deep understanding allows for highly accurate anomaly detection, enabling organizations to take proactive measures to stop anomalous network traffic. Historically, targeted response actions were challenging to implement safely in OT networks; however, with advancements in AI, critical infrastructure organizations can now quickly identify threats and respond confidently.”
He added that these innovations are crucial for the operational processes of industrial environments. Industrial systems have long been equipped with emergency shut-off valves and various physical safety controls. “With AI, we now have similar safety capabilities from a cybersecurity perspective, ensuring robust protection for ICS.”
“AI-driven network and security monitoring has transformed OT network visibility, addressing key challenges in ICS environments,” Huang said. “These advancements enhance asset discovery and identification, analyze communication patterns, and detect vulnerabilities and exposures. Additionally, they help identify behavioral anomalies, OT process deviations, and malicious activities, providing a comprehensive approach to threat detection and security.”
Huang observed that as OT networks grow more dynamic, continuous monitoring has become essential beyond traditional static asset inventory and segmentation. “Increasingly, these security monitoring capabilities are being integrated into mainstream security platforms, enabling a more unified approach across IT and OT environments and more proactive risk mitigation to protect hard-to-patch vulnerable OT assets. The ability to track activity in real time and detect anomalies reduces false positives and enables predictive threat detection, easing the burden on operators. This is particularly critical in ICS environments, where security compromises can threaten data integrity, disrupt operations, cause financial losses, and, at worst, endanger human safety.”
Buenaño said one of the most significant innovations is adopting cloud-based monitoring solutions, enabling centralized visibility and management of distributed OT environments. “These platforms not only provide scalability and flexibility but also improve collaboration among teams, facilitating faster response times to incidents and enhanced interoperability standards such as the adoption of the Open Process Automation (OPA) framework. This standardization allows for more comprehensive monitoring capabilities that can aggregate data from various sources, leading to more informed decision-making.”
Another innovation that complements the previous one is the integration of AI and ML into the network monitoring tools, which allows for real-time anomaly detection and predictive analytics.
“Additionally, the development of advanced threat detection and early warning systems that utilize behavior-based analytics instead of traditional signature-based methodologies enables these tools to adaptively learn from the network behavior, making them more effective against sophisticated cyber threats that frequently target ICS environments, especially in legacy systems and proprietary protocols,” Buenaño said. “Lastly, a significant advancement is the implementation of zero-trust security models, which fundamentally alters how access controls and identities are managed. By assuming that threats can originate from both inside and outside the organization, zero-trust architectures enforce strict access controls and continuous verification, which is crucial for protecting sensitive OT networks from insider threats and external attacks.”
dos Santos said signature- and anomaly-based threat detection is the minimum for OT/ICS network monitoring solutions. Organizations should leverage additional innovations specific to ICS security, including OT/ICS threat intelligence, which should include indicators of compromise, lists of exploited vulnerabilities, and remediation information. This can be sourced from asset owners, ISACs, security researchers, or vendors.
He added that proactive risk management through OT monitoring brings deep insights into assets’ risk based on configurations, behaviors, and vulnerabilities. This improves security configurations and OT/ICS uptime. AI-driven incident detection and analysis help correlate massive amounts of data into digestible insights, guiding investigation analysts and reducing risk.
Geyer mentioned that one of the most important advancements is using machine learning to not look at OT assets as individual ‘atoms’ and instead to look at the assets involved in key critical business processes as ‘molecules.’ “By understanding these key zones, organizations can establish policies of what traffic is normal between zones and investigate any deviations from the policy. It’s another set of techniques to both understand what is most critical and observe network traffic that could represent malicious traffic.”
Enhancing OT network security with adaptive technologies
The executives explored the importance of establishing a behavioral baseline for effective anomaly detection in OT networks and analyzed how adaptive technologies, such as AI and ML, enhance this process.
“Establishing a behavioral baseline is crucial for anomaly detection in OT networks because it allows organizations to understand the normal communication patterns of their ICS,” Macre said. “By learning what constitutes typical behavior, organizations can quickly identify deviations that may indicate potential threats, whether they are external or insider attacks. This understanding empowers organizations to respond to these threats confidently, without disrupting operations or risking damage to expensive equipment and, most importantly, without compromising the safety of the workforce operating these systems.”
He added that adaptive technologies like AI and ML significantly enhance this process. They can enable continuous learning and adaptation to routine communications within ICS environments. “AI/ML technologies improve the accuracy of anomaly detection by reducing false positives and providing a deeper understanding of normal versus abnormal behavior. This allows for more precise and timely responses to threats, ensuring the protection of critical infrastructure.”
“AI and ML are increasingly used for device identification, behavioral baselining, and anomaly detection, enabling the detection of sophisticated attacks in OT networks,” Huang said. “Many IoT and OT devices exhibit predictable behaviors, making it essential to establish a baseline of normal activity. By understanding common behavior patterns, AI-driven solutions can quickly identify anomalies, misconfigurations, or malicious events that could compromise OT process integrity. These technologies also help operators respond to threats faster while addressing the cybersecurity skills gap.”
Buenaño said that establishing a clear understanding of the normal behavior of ICS equipment is essential for detecting anomalies that could signify cyberattacks, equipment malfunctions, or inefficiencies. “By analyzing historical data to define what constitutes normal performance, OT and security teams can create a baseline that encompasses typical usage patterns, communication protocols, and operational metrics. This baseline helps security professionals to rapidly distinguish between benign fluctuations and genuine threats.”
However, he added that the dynamic nature of OT networks, which often experience variations due to changes in production processes, maintenance schedules, or unexpected environmental factors, need these baseline measurements to be adaptable. This is where adaptive technologies such as AI and ML come into play; these methodologies enhance the understanding of operational behaviors and anomaly detection processes by automatically learning from ongoing data streams and continuously updating the behavioral models based on real-time information.
Buenaño added that AI/ML algorithms can process vast amounts of data more efficiently than traditional methods, recognizing subtle patterns and correlations that may not be readily apparent to human analysts. “As these algorithms learn from both normal and anomalous events, they become increasingly efficient at filtering noise from legitimate anomalies, which enhances their accuracy and reduces false positives.”
“Behavioral baselines allow defenders to identify unusual and malicious activity, such as changing programmable logic controller (PLCs) firmware or ladder logic,” according to dos Santos. “IT network anomaly detection is possible, but OT networks have less access to unknown IP addresses and more regular traffic patterns, making it easier to establish baselines. AI/ML helps correlate data, creating better models for ‘normal’ network activity. GenAI models also help simplify explanations of why something is anomalous in investigations.”
Geyer said one of the things that favors the defenders is the cyclical nature of machine-to-machine communications in OT environments. “This makes the establishment of a baseline possible and is crucial for anomaly detection in OT networks to enable organizations to distinguish between normal and suspicious activities. Achieving this baseline requires a comprehensive understanding of asset discovery, user activity, connectivity paths, and asset communication patterns across the network. This multidimensional visibility is key to defining what ‘normal’ behavior looks like.”
He added that AI and ML technologies enhance this process by continuously analyzing network traffic and user behavior to identify deviations from the established baseline. These technologies can detect subtle changes that might be indicative of a threat, allowing for more accurate and timely responses.
Integrating threat intelligence for proactive OT security
The executives evaluate how integrating external threat intelligence into OT monitoring systems enables organizations to transition from a reactive to a proactive security strategy.
Macre said that integrating external threat intelligence into OT monitoring systems significantly enhances an organization’s ability to shift from a reactive to a proactive security approach. “External threat intelligence provides detailed insights into threat tactics and techniques, which helps teams improve their situational awareness. By understanding potential threats in advance, organizations can anticipate and prepare for attacks more effectively.”
“Improved situational awareness also enhances incident response capabilities, allowing organizations to respond more swiftly and accurately to potential threats,” according to Macre. “However, it’s important to note that organizations can adopt various other approaches to improve their overall security posture before integrating threat intelligence. These foundational steps can have a positive impact on their security measures, making the integration of threat intelligence even more effective when implemented.”
Huang observed that this essentially enables a ‘threat-informed defense’ strategy, shifting the focus from waiting for an attack to actively hunting for and mitigating threats beforehand.
“Asset owners can know in advance which threat actors and attacks are most relevant and active in their industry, allowing better mobilization of SOC resources to systematically assess the presence of threats in their environment,” Huang added. “Good intel also comes with mitigating guidance, so organizations are also better able to respond in a timely manner and even leverage automated security playbooks for further analysis and containment. Timely containment is, of course, paramount in managing risk, which in OT environments could escalate very quickly with the associated core industrial business operations at stake.”
Buenaño detailed that by incorporating external threat intelligence, organizations gain a comprehensive perspective of the threat landscape, including emerging vulnerabilities, tactics, techniques and procedures utilized by cyber adversaries targeting OT infrastructures. “This enriched data allows OT and security teams to anticipate potential attacks rather than merely respond to incidents after they occur.”
For instance, he added that by analyzing indicators of compromise from external sources, OT and security teams can fine-tune their monitoring systems to detect specific malicious activity associated with current threats, enabling early detection and response. Threat intelligence also facilitates risk assessment processes, allowing OT and security teams to prioritize their security efforts based on real-time data about ongoing threats that may impact their operations.
“Threat intelligence is traditionally equated with indicators of compromise used for threat detection and reactive incident response,” according to dos Santos. “But knowledge about currently exploited vulnerabilities and targeted device types can also help defenders focus attention on high-value assets and address risks before they get exploited. Long-term strategic intelligence can also help asset owners prioritize investments in network defenses.”
Geyer noted that while internal monitoring tools can detect known threats, external intelligence provides insights into tactics used by attackers when they ‘phone home’ to command and control servers. However, as an ounce of prevention – risk reduction – will always be better than a pound of cure – threat detection – one of the most important uses of threat intelligence is to remediate exposures and known exploited vulnerabilities in critical zones before they can be attacked.
Preparing for emerging OT network security threats
The executives focused on the trends anticipated in OT network security monitoring over the next three to five years and how industrial enterprises can prepare for upcoming challenges.
Macre said that threats to OT systems are rising in sophistication, speed, and scale – and this trend will continue over the next few years. For organizations to stay ahead of these threats, breaking down silos, integrating AI-powered tools, and shifting to proactive strategies will be critical.
“Many organizations today use a siloed approach, where IT and OT networks use separate monitoring tools,” according to Macre. “This method is increasingly proving ineffective, with organizations recognizing the challenges of having disparate tools sending an overwhelming volume of alerts to separate IT and OT Security Operations Centers (SOCs) or even the same SOC. Moving towards a unified platform across IT and OT networks will streamline detection and response processes, leading to more organized and efficient handling of cyber threats within critical infrastructure networks.”
In addition to a unified approach for IT and OT networks, Macre identified that “we will continue to see adoption of AI-powered tools to detect threats more quickly and accurately within ICS environments. Finally, the implementation of proactive response actions to stop threats immediately, rather than the traditional quarantine and assess approach, will become standard practice in ICS environments. This shift will enable organizations to mitigate risks more effectively and maintain operational continuity.”
“OT security monitoring is shifting from niche visibility tools to integration with mainstream network security controls like NGFWs to leverage the visibility to better enforce segmentation and risk mitigation,” Huang said. “AI is playing a larger role in detecting and mitigating increasingly AI-powered attacks, enhancing threat detection and response. Organizations are also prioritizing holistic exposure visibility to better understand risks across IT and OT environments.
He added that leveraging advanced analytics is becoming essential for gaining deeper insights and enabling proactive security measures. “By integrating these capabilities, enterprises can improve resilience, stay ahead of evolving threats, and drive more informed security decisions.”
Buenaño identified that one prominent trend will be the integration of AI and ML into security monitoring systems, allowing for real-time anomaly detection and automated response mechanisms that can adapt to new threats faster than traditional methods.
Additionally, as IIoT continues to expand, the number of connected devices in OT environments will increase, requiring enhanced visibility and control to manage potential vulnerabilities and data flow between devices effectively. Similarly, greater regulatory scrutiny around cybersecurity will compel industrial enterprises to adopt more rigorous security practices, leading to the implementation of zero-trust architectures.
“Industrial enterprises can prepare by investing in robust security training for employees at all levels to promote a culture of cybersecurity awareness, ensuring that personnel can recognize and respond to potential threats promptly,” according to Buenaño. “They should also prioritize the development and refinement of incident response and disaster recovery plans, incorporating drills and simulations to prepare for various attack scenarios. Implementing comprehensive asset management strategies allows organizations to maintain visibility and control of all devices on their networks, including software versions and configurations.”
He added that enterprises should also consider leveraging advanced security frameworks and collaborating with cybersecurity partners to enhance their threat intelligence capabilities to ensure a proactive rather than a reactive stance against emerging threats. “Regularly auditing and updating security policies and protocols can accommodate new technologies and threat landscapes to maintain an adaptive security posture. Finally, participation in industry collaborations and information-sharing groups can enable organizations to stay up to date on threat intelligence, emerging vulnerabilities, and best practices in OT security.”
dos Santos listed that OT monitoring will be integrated into the whole enterprise security ecosystem. “Organizations using OT network monitoring often also employ vulnerability assessments, endpoint protection, and configuration management databases for security. Integrating these solutions is crucial for sharing insights, detecting threats, and orchestrating actions across systems to defend against future threats.”
He added that as security moves from reactive to proactive, there’s a growing focus on compliance. This includes frameworks, regulations, industry best practices, organization-specific policies, and emerging guidelines, like post-quantum cryptography.
Lastly, to better leverage these use cases and technologies like AI/ML, asset owners are increasingly embracing cloud solutions. This mirrors a previous shift where asset owners recognized that certain active queries on OT networks can be performed safely while improving visibility.
dos Santos also noted that to overcome challenges like OT/IT convergence and advanced cyber threats, organizations must have the fundamentals: visibility across every device type, continuous risk and compliance assessments for devices, well-segmented networks that limit lateral movement, and robust threat detection to correlate signals across security ecosystems for faster threat response and remediation.
Geyer said that the biggest trend “we should expect to see is the dissolution of the Purdue model in many industries, as organizations look to benefit from cloud-based emerging technologies and capabilities. Without the application of a zero-trust model for OT, this will lead to an expansion of the attack surface area – enabling attackers to gain easy access to critical OT environments and assets.”
He concluded that while AI and ML will undoubtedly better support threat detection, cyber defenders’ most important action is to establish purposeful strategies to lock down user-to-machine, machine-to-machine, and cloud workload-to-machine communications to limit exposure to attackers.
