As organizations with operational technology (OT) begin to embrace AI, security must be on the forefront of their strategy. The combination of AI significantly widens the attack surface—a surface already expanded by the convergence of IT and OT. Most OT breaches stem from IT connectivity, and OT devices, which regularly lack built-in safety features and patching capabilities, are inherently difficult to secure. The introduction of AI adds a brand new layer of complexity to an already difficult environment.
To navigate these challenges, security professionals must rethink their approach. The important thing to securing AI lies in leveraging AI itself—using the technology’s strengths to create powerful defenses.
Growth of AI adoption and accompanying security challenges
The adoption of AI applications by employees is rapidly accelerating, driving innovation across industries. Corporations are harnessing AI to realize a competitive edge, with employees leveraging tools like generative AI to streamline workflows and boost productivity.
Within the OT sector, the potential for AI is immense, and it’s already transforming operations. For instance, AI is empowering manufacturing and energy, with use cases like smart manufacturing and “machine-as-a-service” leveraging a brand new industrial IoT tech stack that fundamentally challenges the normal Purdue Model and air gapping. Smart buildings have gotten more efficient by utilizing AI to optimize energy consumption, enhance workforce experience, and automate routine maintenance tasks corresponding to monitoring HVAC systems, adjusting lighting based on occupancy, and detecting leaks in plumbing systems.
Moreover, AI-driven decision-making helps OT professionals automate complex processes like scheduling predictive maintenance based on equipment usage patterns, dynamically adjusting production lines to optimize output, and managing inventory levels in real time to stop shortages. By taking on these routine yet critical tasks, AI allows OT teams to give attention to more strategic, higher-value activities that drive innovation and efficiency.
That is already happening, and use cases are rolling out quickly. A recent report by MIT Technology Review Insights found that 64% of manufacturers surveyed had already began researching or experimenting with AI. In reality, in accordance with Gartner, as much as 75% of operational decisions could also be made inside an AI-enabled application or process by 2030.
Nevertheless, organizations must bear in mind that while AI-powered applications offer amazing opportunities, additionally they present recent issues for data security and enlarge the potential attack surface. As AI adoption soars, these systems turn out to be prime targets for cyberattacks.
AI applications corresponding to connected machines require machine telemetry to be collected from the sting on to IT and/or the cloud, which break the normal OT model and increase the threat surface. Often, OT (or shadow IT) can construct such a tech stack without the knowledge or sanction of the IT security team, which exposes industrial organizations with threats from many unsanctioned, external-facing applications and assets. This requires organizations to rethink their security strategies to guard these critical assets.
AI is increasingly being adopted by organizations as they realize its dual potential: reducing costs on the back end while driving greater profits through enhanced applications. Enterprises at the moment are integrating AI components into their application stacks to capitalize on these advantages. Nevertheless, this also introduces recent risks, particularly across the exposure of sensitive data, as AI systems depend on inference and training datasets. As AI becomes a more integral a part of business operations, safeguarding these datasets from potential threats is crucial to maintaining each security and trust.
Securing AI-powered applications with the precise AI-driven plan
The AI genie is out of the bottle. There’s no going back, which implies the one secure way forward is to take a powerful approach to securing these AI-powered applications. And, paradoxically, tackling AI-related security threats requires AI-fueled solutions. In a report by Palo Alto Networks and ABI Research, 8 out of 10 respondents said they believed AI can be essential for combating AI-fueled attacks.
A few of the ways in which AI may help with AI security are:
IT and OT security team collaboration: AI is transforming the best way IT and OT security teams collaborate by providing a unified view of security data that each side can leverage. As OT environments increasingly integrate IT technologies, AI helps bridge the gap by applying advanced analytics across each domains. This allows earlier threat detection, more accurate mapping of attacks to frameworks like MITRE ATT&CK, and automatic monitoring of anomalies. By enhancing communication and streamlining routine security tasks, stronger collaboration between IT and OT teams makes end-to-end, AI-enabled insight possible for higher detection and security.
Augmenting threat detection and response: AI is transforming the best way that manufacturers find and reply to threats, especially with respect to User and Entity Behavior Analytics (UEBA) applied to the numerous devices on the factory floor. AI tools use algorithms to set baselines for normal behavior and rapidly find irregularities that would signal a threat. Standard IT security tools won’t comprehend OT’s specialized protocols, so this AI capability is very essential.
Addressing the cyber skills gap: Globally, there’s an estimated shortage of 4 million expert cybersecurity professionals, per ISC2. AI may help by automating a number of the mundane tasks teams are grappling with and help newer team members cope with higher-level security operations. AI automation also empowers security staff to spend time on high-value strategic initiatives.
Looking ahead, several AI innovations are on the cusp of positively affecting OT security:
- AI-digital twin integration to create more practical security simulations
- Greater accuracy when it comes to threat detection, which lowers the variety of false positives
- Greater ability to evaluate operational risk
Securing AI, in fact, also requires following all of the very best practices for any security program, including investing in periodic training and awareness for workers, staying up so far on regulatory and compliance requirements, and conducting ongoing security inspection of OT processes and network traffic.
Making AI secure
The convergence of OT and IT has already expanded the available network and data security attack surface – and the introduction of AI has expanded it even further. As organizations and their employees quickly embrace AI, the technology brings each opportunity and recent risks, including using unsanctioned shadow AI.
Given AI’s undeniable utility, it’s here to remain, and its security ramifications have to be addressed now. To secure using GenAI and AI-driven applications, organizations must develop a comprehensive security plan that not only protects against potential threats but in addition harnesses AI’s capabilities to strengthen their defenses. The perfect practices noted above provide a framework for organizations to create or tremendous tune a method that permits them to maximise AI possibilities while effectively managing the associated risks.
