Artificial intelligence (AI) technologies have emerged in recent years as a transformative force in the manufacturing industry, driving efficiency and productivity, reducing operational expenses and optimizing quality control processes.
But as manufacturers increasingly integrate AI into their operations, there are growing cybersecurity concerns that cannot be overlooked. The value of AI can only be fully realized with connectivity between systems, networks and devices — and that introduces vulnerabilities that can be easily exploited by cybercriminals.
So, what measures can manufacturers take to protect their data while leveraging the advantages of AI?
How AI is changing manufacturing environments
The adoption of AI is steadily growing and reshaping how products are being created and delivered, enabling smarter, more efficient production.
AI can maximize output
AI-powered robots operate continuously and at a consistent pace on manufacturing floors, executing repetitive and routine processes with precision to reduce human error and maximize output. Advanced machine learning algorithms also conduct real-time analysis and adjustments in the production line to streamline workflows, as well as adapt to requirements of new products without manual updates or reprogramming.
AI can prevent downtime
AI enables predictive maintenance by analyzing equipment data and identify patterns that indicate potential failures. Manufacturers can conduct servicing proactively, preventing breakdowns and avoiding costly repairs while increasing the lifespan of the machinery.
AI can automate demand forecasting
With the ability to analyze mass volumes of data to forecast demand and inventory requirements, AI technology can automatically adapt production schedules in response to changes. This helps manufacturers meet customer expectations more effectively while reducing excess stock and lowering carry costs.
AI can support quality control
Automated visual inspections based on deep learning models can identify defects, irregularities or deviations from specifications during the manufacturing process that would be missed by the human eye. It can even identify issues and implement corrective actions before defective products are completed.
Cybersecurity threats arising from AI in manufacturing
The successful use cases of AI in manufacturing often rely on vast amounts of data and interconnected networks of machines and devices. These conditions make manufacturers prime targets for cybercriminals and exposed to:
Data breaches
There are countless examples of how data breaches targeting businesses have led to serious financial losses, legal consequences and reputational harm. The sheer volume of sensitive and confidential information of customers and employees that is being inputted for AI analysis raises significant concerns about potential attacks by malicious actors.
Intellectual property theft
Protecting intellectual property including proprietary designs, processes, technologies and trade secrets is extremely important for maintaining a competitive advantage within manufacturing. Those leveraging Internet of Things (IoT) devices in their operations are even more vulnerable with multiple entry points and opportunities for unauthorized access.
Data manipulation
Cyber attackers can inject false or misleading data used by AI algorithms. This data tampers leads to unreliable predictions or faulty decision-making that can result in products defects and errors — even safety hazards.
System failure
From equipment damage to malfunctions causing injuries, critical production machinery controlled by compromised AI could lead to serious consequences. Ensuring the security, regular maintenance and implementation of fail-safes in AI systems is vital to mitigating these risks.
Cybersecurity and risk management best practices for using AI in manufacturing
Take a proactive approach — implement protocols to prevent unauthorized access; identify and fix vulnerabilities; and secure the financial protection and expert resources you need in the event of a breach. These best practices include:
- Implementing multi-factor authentication and role-based permissions so access to critical systems and sensitive data are granted to only those requiring it for the performance of their jobs.
- Conducting routine updates and system patches.
- Encrypting sensitive data so it remains secure even if intercepted by cybercriminals.
- Conducting regular security audits and penetration testing to identify vulnerabilities before they are exploited.
- Continuously monitoring network activity for unusual behavior (AI-powered security tools can be used to identify threats in real-time).
- Consult your Acera Insurance broker for a thorough risk assessment and recommendations for coverage types and limits.
Replacement cost values
Your Acera Insurance broker will help you determine if the replacement cost values in your property and equipment breakdown policies are enough. Repairing and replacing machinery and equipment with AI capabilities are costly — avoid being underinsured by making sure coverage limits accurately reflect current market conditions.
Cyber liability insurance
The average cost of a data breach in Canada is now $6.32 million. Cyber liability policies are designed to help businesses minimize the impact in the aftermath of a breach by covering expenses for data recovery, customer notification, lost income, extortion demands, public relations experts, as well as costs for legal defence and any resulting settlements or judgments.
Rob Shearar is a Senior Client Executive, Commercial Insurance, and Partner with Acera Insurance. He brings 20 years of specialized expertise in custom insurance and risk management solutions for manufacturing, whole and distribution and commercial property across Canada.