The good, the bad and the future of generative AI
While these can’t wholly be attributed to the growth of generative AI, it certainly has a part to play, with programmes like ChatGPT being used to create worryingly convincing phishing emails. Manufacturers should ensure staff are suitably trained to recognise this latest evolution in phishing messages to avoid falling victim to an attack.
One of the main risks associated with generative AI is the potential for hackers to manipulate the algorithms used in the design process. This could result in the creation of flawed or malicious designs that could cause serious damage to equipment or even harm individuals. Additionally, hackers could use generative AI to create counterfeit designs, leading to intellectual property theft and revenue loss for manufacturers.
Another significant risk is the potential for data breaches. Generative AI relies heavily on data input, and if this data is compromised, it could be used to create faulty designs or even shut down production processes. This could result in significant financial losses for manufacturers and pose a threat to national security in industries such as defence.
However, cyber security isn’t the only challenge that generative AI introduces. Generative AI can sometimes produce designs that are difficult to interpret, making it challenging to identify potential flaws or vulnerabilities. To mitigate this, manufacturers should ensure that their AI systems are transparent and explainable to enable effective risk assessment.
To overcome these challenges, manufacturers and engineers must prioritise cyber security and data management. This involves implementing robust security protocols, such as encryption and multi-factor authentication, to protect sensitive data. Regular risk assessments and penetration testing should also be conducted to identify vulnerabilities in systems.
Cyber security is also becoming even more of a priority at machine-builder level, where industrial equipment is being designed with secure communication protocols, such as TwinCAT, in mind. This involves using encryption techniques to protect data transmission between equipment and other systems, as well as implementing secure authentication processes to prevent unauthorised access.
With an aging workforce and a lack of younger people entering the industry, it’s clear that the UK’s manufacturing sector will face an ongoing shortage of workers. Embracing technologies like generative AI could go some way in overcoming it, but it’s important that the correct measures are taken to ensure that the technology doesn’t leave manufacturers open to catastrophic security breaches.
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