Cybercrime in manufacturing is expected to cost $10.5 trillion by the end of 2025. The sector has remained one of the most targeted industries for three years, now accounting for more than a quarter of all cyberattacks—up from just 8% in 2019.
Why? Manufacturers’ critical role in the global economy, the potential for supply chains disruptions, and the large amount of sensitive data involved make the industry a prime target for cybercriminals—not to mention its reliance on continuous uptime and hesitancy to halt operations for security upgrades. As cyber threats continue to evolve, manufacturers can no longer afford to rely on traditional security measures, and they’re looking for innovative ways to stay ahead of attackers. AI has emerged as one of the best lines of defense. However, the technology doesn’t come without risks. As manufacturers increasingly lean on AI tools to strengthen their OT security, taking a balanced approach focused on cyber resilience will be crucial.
Advantages of AI in Manufacturing Cybersecurity
1. Enhanced Threat Detection & Response Capabilities
The main benefit of AI-driven cyber solutions is their capacity for enhanced threat detection, real-time network analysis, rapid recovery, and predictive analytics. By analyzing network traffic patterns and equipment performance in real-time, AI can identify irregularities that may signal a security breach or signs of a system failure. This predictive capability allows manufacturers to proactively resolve issues, minimizing operational disruptions and strengthening cyber resilience.
Additionally, AI enhanced alerts and threat detection reduce the administrative burden on OT employees, allowing them to focus on strategic operations instead of repetitive tasks.
2. Reduced Unplanned Downtime
One of the biggest challenges impacting manufacturers today is unplanned downtime. Manufacturers face an average of 800 hours of equipment downtime annually, which amounts to about 15 hours per week and trillions of dollars in losses per year.
The good news is, in the event of a cyber-attack, AI-backed solutions can enable instant recovery with near-zero downtime—a necessity in OT and ICS environments. AI assists in identifying the optimal data recovery point, allowing manufacturers to restore only the necessary data rather than the entire set, reducing recovery time and minimizing unexpected production halts.
3. Seamless Scalability and Adaptability
AI-driven systems are also useful for future proofing manufacturing organizations. AI continually adapts to the changing landscape and can adjust defensive strategies based on emerging risks, ensuring the most up-to-date protection. As OT networks expand, AI systems can seamlessly scale alongside them, maintaining consistent levels of protection regardless of network size. Additionally, AI tools can learn from company data and be tailored to specific industry needs, further guaranteeing that a manufacturer’s cybersecurity strategies are both relevant and effective.
Risks of AI in Manufacturing Cybersecurity
1. Integration Challenges & High Costs
Despite AI’s ability to drastically transform the manufacturing industry, it is not without its drawbacks. For one thing, integration into OT environments can be challenging. Many manufacturing facilities operate on decades-old infrastructure that lacks standardized cybersecurity formats, making AI implementation difficult due to compatibility issues with legacy systems and data quality concerns.
On top of this, AI solutions often require hefty upfront investments. Acquiring tools, integrating them into existing frameworks, and training staff to use them can be costly. Additionally, in the long term, organizations may need to hire specialized personnel who are skilled in AI to manage and maintain these systems, further adding to the high costs.
2. False Positives & Unnecessary Alerts
AI enhances the speed and accuracy of threat response and highlights genuine threats that require immediate attention. Yet, AI models often generate false positives, triggering unnecessary security alerts that can overwhelm already swamped manufacturing teams. To avoid this issue, AI must be refined and trained by experts who can provide contextual insights that cover the unique nuances of OT security.
Manufacturers looking to improve AI’s accuracy and reduce false positives can implement a multi-layered approach across multiple data sources. By incorporating historical data, real-time monitoring, and third-party integrations, manufacturers can rest assured knowing they enhanced AI’s decision-making and their overall cybersecurity resilience.
3. AI-Generated Cyberattacks
Ironically, one of the biggest threats to manufacturers is AI itself. The technology is a double-edged sword, and while it can supplement a manufacturer’s cybersecurity strategy, it can also be used by attackers to exploit weaknesses, gain unauthorized access to networks, or disrupt operations. Attackers are now leveraging AI to automate breaches, develop adaptive malware, and identify vulnerabilities at breakneck speeds. After all, if we are using AI, so are bad actors.
It is clear that cyber threats will only continue to evolve, and so must our defenses. AI is a game-changer in manufacturing cybersecurity, offering both immense opportunities and significant risks. However, its true potential lies in its strategic application. Manufacturers need to continuously monitor their AI-driven systems while remaining vigilant against AI-powered threats. By embracing AI responsibly and addressing its challenges, manufacturers can reinforce their operations, protect their data, and foster cyber resilience in an increasingly threatening landscape.
