According to Forbes, 2025 was a watershed moment for cybersecurity as the year that artificial intelligence (AI) became both shield and sword, reinventing how defenders and attackers engage on the cyber battleground. Attackers now leverage AI to launch adaptive, self-learning campaigns; defenders reply with machine-speed detection and response.
Less headline worthy but equally important: After years of languishing in “pilot purgatory,” digital transformation efforts took off in 2025, as cloud computing, AI, IoT and 5G connectivity reached maturity levels — and price points — conducive to driving affordable innovation at scale. Today, the convergence of OT, IoT and IT to power operational efficiency and just-in-time production is nearly ubiquitous among large industrial organizations around the globe.
Not coincidentally, 2025 was a tipping point for OT cybersecurity, in terms of risk consolidation under the CISO. According to a Fortinet survey, more than half (52%) of surveyed organizations assigned CISO/CSO responsibility for OT in 2025, up from just 16% in 2022. As owners of enterprise risk, corporate CISOs must deliver on holistic strategies designed not only to protect data integrity and availability but also to ensure cyber-physical resilience — and articulate progress to their boards. Regulatory pressures to consolidate cybersecurity risk have accelerated this trend.
Whether you’re a CISO newly assigned responsibility for OT and IoT risk, an analyst trying to make sense of OT and IoT alerts in a newly merged SOC, or a plant operator grappling with how to square new cybersecurity controls with safety and efficiency concerns, the industrial cybersecurity curve can be steep, especially since AI is changing the rules of engagement.
This article provides a primer on OT and IoT security, followed by three essential ways to leverage AI to build resilience:
- 100% asset visibility with endpoint and wireless sensors
- Holistic threat intelligence that connects the dots
- SOC efficiency that closes the OT security skills gap
OT & IoT: The Fastest-Growing Contributors to Enterprise Cyber Risk
Across industries, OT and IoT devices are a growing percentage of total digital assets. A 2024 survey found that OT, IoT and other specialized systems comprise 42% of enterprise assets — and account for 64% of mid- to high-level enterprise risk. In other words, the fastest-growing part of the enterprise attack surface is the part CISOs understand the least and have invested in the least, because IT cybersecurity tools don’t work in OT and IoT environments. This explosion is raising questions from key stakeholders.
Here are the differences that give rise to these questions.
How Is OT Security Different From IT Security?
If you’re coming to OT with a solid grounding in IT security, you’re probably tired of being told, “OT is different.” And if you’re an OT engineer or plant manager confronting IT-focused security people, you’re probably tired of explaining how. There are plenty of nuances, but here are the main ways that OT networks and devices are different and why IT security tools don’t work with them.
- Physical consequences. IT manages information. OT controls physical processes, including the crown jewels that drive revenue or provide essential public services. Often, they operate continuously. If OT fails or is attacked, the stakes are higher, especially for critical infrastructure.
- Cybersecurity goals. IT security values confidentiality, integrity and availability (the CIA triad). OT security values process uptime, safety (human and environmental) and reliability.
- Nothing is standard. IT solutions use standard operating systems, have frequent, automated updates and are upgraded or replaced every 3 to 5 years. OT assets are built to last 10 to 15 years and are “insecure by design.” Patches, if available, are infrequent, and updates must occur during maintenance windows.
IT security tools don’t work because they can’t read hundreds of proprietary OT protocols, can’t perform deep packet inspection (DPI) and can’t baseline normal behavior to detect anomalies. Moreover, IT endpoint agents that are standard for antivirus protection and patching don’t work on OT devices: they’re heavyweight, disruptive and aren’t trained on OT environments so detect the wrong threats.
How Is IoT Security Different From IT Security?
The Internet of Things is the ecosystem of internet-connected devices that collect, share and act on data to make the modern world hum. IoT devices are everywhere and, like OT devices, they’re typically insecure by design. Added challenges call for security approaches more similar to OT than IT. For example:
- Proliferation of diverse devices. An ever-expanding number and array of IoT devices, many wireless, use stripped-down OSs and disparate protocols — and are often deployed ad hoc.
- Unmanaged and insecure by design. Forgotten-once-deployed, internet-exposed devices with no encryption and unpatchable firmware are ideal pivot points to bypass perimeter defenses.
- Weak identity and access controls. Use of default passwords and lack of strong authentication procedures, including for remote access, make IoT devices easy to exploit.
The sheer volume of diverse IoT devices — many of them wireless — combined with weak or nonexistent security controls render traditional IT security tools ineffective.
The New Asset Inventory: Don’t Overlook Wireless and Endpoints
Asset visibility. Asset inventory. Asset management. By any name, knowing what assets you have is the foundation of all cybersecurity frameworks, regulations or programs. It’s the starting point for effective anomaly detection, vulnerability management and, ultimately, risk management. Passive monitoring of wired network traffic via SPAN/TAP using DPI has long been the standard for OT environments. It’s no longer enough.
In 2026, you need a complete, automated inventory of wired and wireless OT, IoT and IT assets, with deep insights into their behavior and communications. That requires a combination of endpoint-to-air sensors, passive and active data collection, OT/IoT protocol fluency and third-party IT asset data — using AI to fill in missing details based on matching devices, so you understand their risk.
Wireless Security Sensors
Industrial organizations increasingly rely on wireless communications for logistics, autonomous transport and monitoring, yet they’re often the biggest remaining blind spot, even in mature organizations. Intermittent operation of wireless devices makes baselining normal behavior even harder. OT/IoT wireless sensors can read protocols such as Bluetooth and cellular but also LoRaWAN and ODID, and detect wireless threats such as a deauth attack, rogue wireless access point (WAP) or wireless network infiltration using compromised credentials.
Endpoint Security Sensors
In IT security, endpoint agents are ubiquitous for anti-virus protection and patching, but negative experiences deploying them on OT devices have led to scarce adoption. Purpose-built OT endpoint sensors overcome those objections, but they must do more than collect asset details. They should also detect who’s logged onto what machine when. Human interface devices (HIDs), human-machine interfaces (HMIs) and other devices are where people (including remote, third-party technicians) interact, and thus where suspicious activity happens.
AI-Powered Asset Matching
AI is indispensable for enriching asset profiles to achieve near 100% asset inventory accuracy. It can augment sensor-collected data by inferring asset types and roles based on traffic patterns and tapping a trove of details from matching devices to fill in missing data fields. Closing these gaps helps you zero in on essential information for identifying the riskiest assets, such as which ones have known exploited vulnerabilities (KEVs).
SOC Efficiency: Close the OT Cybersecurity Gap
If anyone needs an “easy button,” it’s the overwhelmed SOC analyst new to OT and IoT security. When AI infuses every aspect of your cybersecurity platform — from asset inventory to behavior baselining to threat and anomaly detection to vulnerability management — those capabilities culminate in the SOC, where the most critical information must be surfaced so analysts never miss a critical issue. Not just displayed but continuously refreshed, correlated and prioritized by risk, with drill-down access to more insights and what to do next.
Offloading the tedious tasks of reviewing, correlating and prioritizing thousands of data points to a tireless AI engine can potentially eliminate the need for Tier 1 SOC analysts and other junior positions altogether. No more sifting through the noise for a few alerts that matter. No more being stumped about what to do, not just about this one alert but to reduce the most risk overall.
Hello, Ask Me Anything About Your Environment
Digital assistants have taken off because they provide instant answers to plain-language questions that might otherwise require hours of research. No doubt SOC analysts are turning to commercial chatbots for help, but for reliable answers they need an AI assistant they can engage with that is OT and IoT aware and can respond contextually based on what’s happening in their environment. Instead of just asking a general compliance question about, say, NIS2 or IEC 62443 requirements, they can ask whether their specific environment is compliant and, if not, what to do in what order.
AI Is Broad. Leverage the Right Kinds for Every Use Case.
Cybersecurity breaches and other failures overwhelmingly stem from things that organizations can’t see, don’t know about or, worse, incorrectly assume are protected. Together, these blind spots increase cyber risk. Between expanding attack surfaces and unbridled AI-powered threats, preventing breaches altogether is increasingly unlikely, but the means to close gaps and build resilience are well within reach, using the AI-powered strategies and tools described here.
To be AI-powered, an OT/IoT cybersecurity platform needs more than a commercial chatbot and LLM bolted on with an API.
In 2025, widespread access to generative AI tools like ChatGPT and Copilot put the power of AI in everyone’s hands. However, it’s misleading to mistake this narrow subset of AI, which relies on large language models (LLMs) to “think,” with other models. To be AI-powered, an OT/IoT cybersecurity platform needs much more than a commercial chatbot and LLM bolted on with an API.
Threat actors are using many types of AI to launch sophisticated attacks faster than ever; cyber defenders must likewise use the right AI methodologies in the right ways to at least keep pace with them. To see what that looks like action in the Nozomi Networks platform, request a demo.
