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The Future of AI-Based Security Solutions in the Cybersecurity Industry

Artificial Intelligence (AI) promises to improve the performance of every major industry in the world by automating tasks that were earlier done by humans. As more companies now prefer to keep their data on the cloud and as instances where hackers steal data of businesses and governments grow, there is an urgent need to revamp the existing solutions to keep the threats at bay.

AI-based security instruments using technologies such as Natural Language Processing (NLP) holds promise to the cybersecurity industry to develop new generation solutions to handle the threats. This article deals with the near future of AI-based security systems and tools that hold promise to transform the cybersecurity industry.




Analysing Potential Vulnerabilities Using Predictive Analytics

Predictive analytics is the practice of identifying patterns and trends from the existing data sets to guess future outcomes. It is used in various industries such as stock trading and weather forecasting to predict the events in the near future. Despite the audacity of many cyber-attacks, they follow specific patterns that could be easily discerned. Cybersecurity professionals are using predictive analytics tools such as regression analysis to analyse past attacks and predict future attacks. Guessing future attacks with a fair degree of accuracy will aid in finding potential vulnerabilities. In turn, by identifying these vulnerabilities, companies and security professionals will be capable of setting up the required defences before hackers can reach a network’s boundaries.

The use of advanced machine learning-powered solutions such as artificial neural networks can improve the predictive power of the cybersecurity industry in identifying potential vulnerabilities before they can cause a major data breach.

Related:
Malboard is a new sophisticated attack developed by security researchers at Israeli Ben-Gurion University of the Negev (BGU).
Malboard Attack Uses AI to Mimic Users and Evade Detection

AI-Powered Network Analytics

The efficient management of network infrastructure is vital to protect sensitive data from cybercriminals. Considering the widespread of the internet, virtually all organisations, starting from retailers to government institutions can become the victim of a cyber-attack. Cybersecurity firms that manage networks of their clients deploy an army of security professionals to monitor the network infrastructure all the time. Monitoring network infrastructure by humans has been an expensive task and is prone to errors. AI-powered network analytics is now coming to the rescue of the cybersecurity industry. AI algorithms can be trained to identify network failures before they open the doors to hackers. Infrastructure failure can also be predicted even before it happens by identifying bottlenecks and other shortcomings in the design of the network. AI-powered network analytics is also being deployed to solve network congestion issues that can ultimately lead to network failures.




After experiencing success in the management of network infrastructure using AI, cybersecurity is now moving from the deployment of simple time-series algorithms that can detect anomalies to specialised machine learning algorithms that can detect and act on network failures and data breaches in real-time. The key to making AI-powered network analytics success is feeding the algorithms with the required data to train them well. Many firms are now engaged in the task of identifying and harvesting data required for training AI algorithms.

AI-Powered Asset Management

Proper maintenance of both physical and software information technology (IT) assets are crucial for protecting a company’s assets from attacks. AI-powered tracking of IT assets will help in risk mitigation and redundancy planning through the analysis and simulation of data in real-time.

Legacy asset-management operations that were taken care of by human analysts such as the discovery of the need, dependency mapping, utilisation mapping, and monitoring of asset performance relative to their true potential will be automated using machine learning-based systems. Unlike human-based asset management, AI algorithms continuously improve themselves as new data is fed into them. There is no need to re-programme them periodically to improve their performance. This unique characteristic of AI algorithms mitigates the scope for error in a world where the ingenuity of cyber thieves is growing every day.

A few cybersecurity firms are merging the capabilities of the Internet of Things (IoT) with AI to effectively monitor the IT assets of organisations. IoT software can collect real-time data regarding the performance of assets and feed AI algorithms to assist in doing their maintenance work and suggest replacement wherever necessary.

Natural Language Processing for Identifying New Threats

NLP is a key area of AI that focuses on analysing large quantities of natural language data that is found in the text form. NLP is increasingly being used by the cybersecurity professionals to analyse companies’ public and private data and identify patterns. The analysis of the natural language data from magazines, newspapers, academic journals, online discussion forums, company logs, and specific studies on cyber threats will help professionals detect anomalies found in the private networks of computers and the new threats that are being faced by firms in general.

The cybersecurity industry is using insights from NLP analysis to build prevention strategies that could be of immense use for their clients. NLP analysis will also help cybersecurity firms stay updated on the latest trends, new types of attacks launched, and the timeframes available for responding to threats so that responsive strategies could be developed for the benefit of their clients.




Cheap availability of cloud computing power will further facilitate the use of NLP analysis by the cybersecurity industry to store and process large quantities of natural language data. Audio Data Analysis (ADA) using deep learning AI algorithms is the next frontier for the cybersecurity industry to analyse audio content available on mass media and social networks such as YouTube to predict and control emerging threats.

Related:
AI has already demonstrated endless potentialities in multiple forms across various industries like healthcare, education, manufacturing, and cybersecurity.
Combating Cybercrime with Artificial Intelligence

In a study conducted by the Capgemini Research Institute, nearly 69% of organisations opined that AI was required to respond quickly to cyber-attacks. Irrespective of the area in which AI is used, it can improve the efficiency of cyber analysts. While banking upon AI to increase their efficiency, businesses should also be aware of threats AI poses to their data and networks. AI can also be used by hackers to launch sophisticated attacks. Moreover, hackers can turn the AI algorithms deployed by the cybersecurity firms against them ‒ popularly known as adversarial AI. Overcoming these challenges will help the cybersecurity industry make the best use of AI in providing services to their clients.


About the Author: Ellie Richards

Ellie Richards is an online Marketing Manager for Original PhD, specialising in PhD Proposal writing help. She is passionate about researching and writing on various topics, including Education, Marketing, and Technology. Follow Ellie @EllieRi43718805

SensorsTechForum Guest Authors

SensorsTechForum Guest Authors

From time to time, SensorsTechForum features guest articles by cybersecurity leaders and enthusiasts. The opinions expressed in these guest posts, however, are entirely those of the contributing author, and may not reflect those of SensorsTechForum.

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