Innovations in R&D: How AI Is Transforming Industrial Cybersecurity Operations

Industrial control systems are becoming more complex as evolved cyberattacks threaten industry functions. Traditional network security measures are no match for today's breach landscape. Fortunately, advancements in artificial intelligence (AI) are proving to have powerful potential for improving industrial cybersecurity operations.

Importance of cybersecurity in industrial operations

As the industrial sector becomes increasingly connected to the Internet of Things (IoT), cutting-edge AI cybersecurity is more critical than ever. The Better Buildings Solution Center of the U.S. Department of Energy says 34% of all cyberattacks target manufacturers, while 95% of breaches are typically avoidable with minor security updates.

The industrial sector is essential to the global economy. As such, cybersecurity risks are a severe threat, with the potential to disrupt supply chains, induce significant financial loss and ruin a company's reputation. Savvy hackers might also take over industrial control systems, which are critical operational infrastructure and services.




Integrating AI cybersecurity measures has already been effective in safeguarding industrial processes. However, it has yet to become widespread enough across industries. New IRS reporting criteria for Research & Development (R&D) Tax Credits will help streamline the process with 80% qualifying research expenditures limited to 50 business components—this is less than before.

Companies utilizing AI cybersecurity technologies will be able to save time and resources filing for the R&D Tax Credit, incentivizing innovations and adoption of the technology in industrial cybersecurity.

Integrating AI into industrial cybersecurity

R&D of AI technologies is occurring as rapidly as its utilization across industry operations. While many sectors, from health care to manufacturing, have implemented AI and machine learning to automate tasks, many have also used it to enhance network security and prevent cyberattacks.

1. Threat detection
Conventional security uses fixed rules and patterns to prevent cyber-related threats. However, its static nature hinders its ability to stay abreast of evolving hacker tactics and vulnerabilities. AI is known for its ability to collect and synthesize vast amounts of data and identify patterns indicative of breaches.

Machine learning, in particular, is vital to threat detection. It uses algorithms to analyze historical insights and predict potential network intrusions. Information technology and security analysts can then employ the data to assess risk and develop a sound preventive strategy.

2. Real-time automated analysis
AI solutions do not just aggregate large sets of information—they do it in real time. These highly advanced algorithms analyze network traffic, operations and online behaviors as they occur, allowing cybersecurity professionals to identify hacker attacks within moments.

The algorithms can also isolate any breached systems, ensure the threat is contained and activate automated responses to mitigate the problem without human intervention. This approach is part of Industry 4.0—the industrial Internet of Things (IIoT). IIoT's primary goal is to automate the supply chain in its entirety using AI-powered tools, including security. This can ensure more efficient operations and less downtime associated with breaches.

3. User behavior analytics and authentication
A new study by Cybersecurity Insiders commissioned by Securonix says the number of companies reporting insider attacks rose from 66% to 76% from 2019 to 2024. An insider attack refers to someone with legitimate access to the network.

The industrial sector has deployed AI to study user behaviors and detect unusual patterns. Using a baseline reference, algorithms can look for variations suggesting insider threats and unauthorized access.

Security analysts use a kill chain to automate threat detection—this means they look for atypical user actions, even if they are unsure of the exact risk. The kill chain also highlights security gaps, enabling specialists to make adjustments.

4. Automated response
AI automation can respond to attacks after threat detection and analysis before experts can address the situation. This R&D AI advancement lessens the response time standard of conventional security and enables teams to concentrate on more essential incidents.

Of course, automation tools do not disregard human intervention entirely. Instead, the industrial industry should find a balance between manual and AI-related cybersecurity to aid understanding and informed decision-making.

Challenges and best practices for deployment

Industrial operations face several challenges in implementing AI cybersecurity on a broader scale. For one thing, AI is vulnerable to cyberattacks as much as industrial processes. Compatibility issues with existing security measures may also present problems.

Considering the age of information in which the world lives and works, dynamic AI security developments must accommodate a greater data volume and more complex workloads.