Emerging Trends and Innovations in AI Threat Response
Artificial intelligence is seen as a driving force for disruptive innovation, paving the way for new, ground-breaking products and services. It’s also seen as a disruptive force in cybersecurity, with more enterprises prioritising AI and machine learning due to the growing volume of data that needs to be analysed, as well as the need to combat cyber threats. As such, AI and its related technologies have become a crucial component in addressing cybercrime.
As AI continues to evolve, it’s highly likely that we’ll continue to see the influx of emerging trends and innovations in the application of AI in cybersecurity, particularly in threat response. This article sheds light on emerging trends and innovations that could further influence the role of artificial intelligence in cybersecurity.
The Future of AI in Cybersecurity
It’s estimated that the cost of cybercrime will grow to USD 10.5 trillion by 2025. To combat this ever-growing threat, organisations are looking to leverage AI-based cybersecurity systems. Because of AI’s capability to learn, adapt and detect patterns in real time, this piece of disruptive technology can speed up critical processes such as detection, containment and response.
Because AI is capable of learning and therefore gets more intelligent with training and time, it’s likely that AI-based cybersecurity solutions for threat response will only get more advanced and effective in the future. In addition, as more organisations embrace this technology, it’s likely that such solutions will become more accessible in the future.
Emerging Trends and Innovations in AI Threat Response
In this section, we’ll look at how certain advancements in the areas discussed below can influence the role of AI in cybersecurity.
Natural Language Processing
Natural Language Processing (NLP) can be used in cybersecurity to bolster breach protection from cyber threats like phishing. It can also be leveraged to identify keywords used in telemetry for the crosswalking process, making it more accurate. Additionally, NLP can be used for language analytics in cybersecurity, particularly for purposes like semantic parsing, intent detection and fake news detection.
Predictive Analytics
Considered one of the biggest benefits of AI, predictive analytics allows AI systems to detect potential vulnerabilities before they become actual attacks. Furthermore, it’s capable of triggering proactive measures to prevent cyberattacks.
Autonomous Response Systems
AI’s autonomous response capabilities can help streamline cybersecurity processes. It enables AI-based cybersecurity systems to react to and contain threats at faster speeds. Furthermore, autonomous response capabilities give AI models the ability to make decisions in an instant, therefore giving their human counterparts more time to make big-picture decisions.
Potential Impact of Technologies on the Future of Cybersecurity
The continued development of technologies, such as quantum computing and federated learning, can potentially influence how AI is used in cybersecurity, as well as impact the future of cybersecurity overall.
Quantum Computing
Quantum computing is an emerging technology that uses quantum mechanics to solve problems that are deemed to be too complex for classical computers, which typically use binary computing to process data. As quantum computing continues to be developed, it could potentially change the way data is processed and protected, according to a report by AI expert, Prof. Ahmed Banafa. Moreover, when applied to cybersecurity, quantum computing may be used to develop quantum algorithms designed to detect and respond to cyberattacks, as well as predict future cyber threats using historical data and trends, according to the same report by Prof. Banafa.
Federated Learning
Federated learning is a method of training AI models, following a decentralised approach. It’s a way of processing data at the source and can be used to tap raw data from sources such as satellites and smart devices. In cybersecurity, federated learning, which is considered a “privacy-aware machine learning model”, can be used to secure the IoT environment, according to Ghimire and Rawat (2022). Moreover, one advantage to federated learning is that it’s capable of collecting and utilising data from multiple sources while keeping it secure and private.
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About the author
ProtectCyber is a leading Australian cyber security firm dedicated to safeguarding businesses and individuals from digital threats. Our expert team, with decades of combined experience in the field, provides insights and practical advice on staying secure in an increasingly connected world. Learn more about our mission and team on our
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