Garbage In / Garbage Out: Training AI Algorithms

In little more than a year Artificial Intelligence (AI) has drawn widespread attention to readily available tools like ChatGPT have demonstrated a wide range of personal and professional applications.

Indeed, the rapid advances in AI have revolutionised various industries, unleashing unprecedented opportunities for innovation and growth and this rapid advance has brought numerous benefits and innovations across various sectors. 

An understanding of the risks posed by AI technology, including the significant data security threats that are already having unintended consequences for companies is equally important. This is because AI systems often rely on vast data to train their algorithms and improve performance. 

This data can include personal information such as names, addresses, financial information, and sensitive information such as medical records and social security numbers. The collection and processing of this data can raise concerns about how it is being used and who has access to it. 

AI is driven by data and derives its intelligence from that data and then completes  tasks that it is trained to do. But, just like human beings, if it does not have and use the right information, the outcome could be useless.  

The more complete the data on which an AI is trained, the more valuable the output will be. An AI gains insights from the data and can make predictions, automate processes and perform other tasks that it is trained to do.
This means that the more data we can feed AI, the better. Data storage, which was previously thought a useless expense, is now a valuable asset. 

• AI-based algorithms are finding genomic markers for rare diseases, directing the discovery of oil and gas deposits, forecasting hurricanes and extreme weather events, alerting us to potential earthquakes or volcanic eruptions, anticipating failures in industrial equipment and alerting us to defects in manufactured goods. 

• If you ask a generative AI like ChatGPT to write a sonnet in the style of William Shakespeare, it can do a remarkably convincing job because nearly everything The Bard ever wrote is freely available on the Internet.

Recent research suggests that data storage has grown into a $77.5 billion industry, and the demand for cloud storage is only growing. In fact, the cost of data storage has been dropping dramatically in the last year, and the combination of declining storage prices and the availability of very high-powered CPU resources is promoting the boom in AI applications, most of which are running in the cloud.

When data is properly stored in the cloud, businesses can customise AI to their own specific needs for the technology, increasing or decreasing their cloud storage depending on their demands, making AI simple to adjust and implement for different aspects of the company. Nevertheless, once the technology is implemented it is critical that the used information is secure and untampered with, since tampered data can provide false responses, and finding and correcting the false information wastes precious time.

AI is a fast growing technology and as the technology grows so do the threats of cyber attacks. Consequently, organisations must constantly check for breaches and other threats. In particular, organisations should create and store an immutable copy of their data, so in case of a ransomware attack the backup copy is safe from the threat of a breach and cannot be tampered with.

Most ransomware attacks are caused by human error and companies must train their employees  about cyber security best AI practices, and also regularly perform hacker security and simulations.

Successfully incorporating AI will be a critical part of remaining competitive in the future. Those who do not properly adjust their technology and adopt AI risk falling behind. AI stands as an immensely potent tool that promises to revolutionise our decision-making processes, enabling unparalleled access to information at lightning speed. 

Cyber security professionals need to act fast to understand AI risks and ensure that they have the appropriate controls in place, and be flexible enough to adapt as new threats emerge - anything less will place valuable company data at severe risk.

Cloud Security Alliance:   I-HLS:     Brian Gray:    Forbes:       Economic Times:    RM Magazine:   

Image; DeepMind

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