In this era of digital connectivity, we are growing increasingly reliant on cyberspace for our daily needs. From grocery shopping to our social bonding, all these non-digital activities are now reliant on the underlying network infrastructure. The number of platforms where you have entered our details is most probably beyond our comprehension. We are even dependent on it for sensitive activities such as banking. Apart from helping the individuals in our society, digital networking has immense applications for any organization, be it small-sized, mid-sized or large-scale corporations. They are the ones who have enabled their customers with such ease of service. The worldwide events post-2020 have forced organizations to increase their digital services for customers, employees and all other stakeholders. This has only fueled the dependency on cyberspace.
Like any technology, digital networking also has its pros and cons. With the ease and convenience this technology provides, comes vulnerabilities that threat actors can exploit. They can lead to identity and data theft or even financial fraud. It’s important to leverage this technology effectively while also securing our digitally connected world. This is where cybersecurity comes in handy. It secures our digital assets so that we can ensure we reap the best out of cyberspace.
We have incorporated Artificial Intelligence (AI) technologies into our expanding technology stack. Although artificial intelligence has existed for decades, recent advancements have made it mainstream and widely popular. Technologies like Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) which have taken the world by storm. This technology has helped individuals in ways previously unimaginable. If a person correctly utilizes LLMs, they own a virtual assistant that can perform tasks traditionally done by humans, from writing code to generating malware. The term AI was proposed by John McCarthy in 1956, who defined it as the science and engineering of making intelligent machines. AI can be defined as a technique that enables machines to mimic cognitive functions associated with the human mind. These cognitive functions include learning, reasoning, perceiving and problem-solving.
With technological advancements, AI has developed various sub-fields which can be used to improve cybersecurity. These include:
- Machine Learning (ML): ML based systems are trained on historical data to identify patterns. Users provide data inputs, which the ML system uses to match with these patterns and produce outputs or predictions. In supervised ML, the user inputs are “labelled” datasets, meaning some inputs are already mapped to the output. In unsupervised ML, the user inputs are “unlabelled” datasets, where the model acts on data without any supervision. ML allows AI to detect anomalies and suspicious activities in network traffic, user behaviour, or system logs.
- Deep Learning (DL): DL, a subset of Machine Learning, uses neural network technology. These interconnected nodes are designed to mimic the human brain. Neural networks can either be convolutional (CNNs) or recurrent (RNNs). Deep learning is often used to detect threats and analyze anomalies.
- Natural Language Processing (NLP): This technology allows AI to understand and process human language. Large Language Models (LLMs) utilize a type of neural network architecture known as transformers. NLP can be used to analyze text-based threats like phishing email detection.
AI is an efficient tool with the potential to influence cybersecurity as well. However, understanding how to effectively harness AI’s capabilities for protection is crucial.
