The result? Companies are increasing the pace of investment in AI systems as a defence against the next generation of cyberattacks. Two thirds (69%) of companies recognize that they will not be able to respond to critical threats without artificial intelligence. With an increasing number of devices for end-users, networks and user interfaces as a result of advances in the cloud, the internet of things (IoT), 5G and conversational interface technologies, organizations face the urgent need to continuously expand and improve your cybersecurity system by using the best firewall .

More attacks, and much faster

“AI-based cybersecurity is essential today,” says the Capgemini study, and more than half (56%) of managers say their cybersecurity analysts are overwhelmed by the immense volume of information units they must monitor to Detect and prevent intrusions. In addition, the type of cyberattacks that require immediate intervention or that cannot be solved quickly enough by cyber-analysts has grown considerably.

Specifically, cyber-attacks affecting applications subject to temporary requirements are detailed. 42% answer that the incidents have increased. On average, the type of these incidents has grown by 16%; and automated attacks, at speeds that only one machine can reach, that mutate at a rate that cannot be neutralized by traditional response systems (43% declare an increase in attacks with this speed, and in turn, these offensives have grown a 15% on average

Given these new threats, a clear majority of companies (69%) believe that they will not be able to respond to cyberattacks without the use of AI, and 61% say they need artificial intelligence to identify critical threats. One in five executives has experienced a cybersecurity gap in 2018, of which 20% cost their organization more than $ 50 million.

Consequently, about half of the managers asked by Capgemini (48%) answer that the budgets for AI in cybersecurity will increase in 2020 by almost a third (29%). In terms of deployment, 73% are conducting use case trials for artificial intelligence in cybersecurity. Only one in five companies used AI in the cybersecurity area before 2019, but a meteoric expansion of its adoption is expected: about two in three (63%) organizations plan to deploy artificial intelligence in 2020 to reinforce their systems of defending.

However, there are important barriers to the implementation of AI on a general scale. The most important challenge for the implementation of artificial intelligence in the field of cybersecurity is the difficulty in transforming the cases of use of proof of concept into a complete deployment. at all levels of the organization. 69% of respondents admit that they are having difficulties in this area.

On the other hand, half of the organizations consulted also point out the problem of integration with their infrastructures, data systems and context of current applications. While most managers replied that knows what he wants to achieve with cybersecurity based on IA, only half (54%) identified data sets required for operating the algorithms of artificial intelligence.

Also read: Cyber ​​Attacks Generate An Impact Of 40,000 Million Euros In A Year

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As the firm says in a statement, this small circular device is partially introduced into the liquids you want to study. To perform the analysis and detection of the fluid, use artificial intelligence and a set of multi sensitive electrochemical sensors, each consisting of pairs of electrodes.

Complex liquids contain many different molecules and it is the combination of all of them that distinguishes them from each other. Therefore, in order to identify them, it is necessary to analyze their molecules as a whole.

Just as the sense of taste or smell does not have a receptor for each molecule of food, but reacts from a specific combination, Hypertaste sensors are able to respond simultaneously to different chemical compounds. Thus, through the combination of these different sensors, a holistic analysis of the set of components of a liquid can be performed and its ‘fingerprint’ can be found.

All the data collected is transferred through a mobile application to the cloud, where an automatic learning algorithm compares this fingerprint with a database with information on known liquids. The algorithm determines which liquids in the database are chemically similar to the liquid being investigated.

A key aspect in this whole process is the ‘training’ that the Hypertaste machine learning algorithm receives, which teaches you to recognize the characteristic pattern of voltage signals of a specific liquid, by multiple measurements of that pattern.

Among the applications of this new technology are the pharmaceutical and health sector, in addition to those related to the quality of river or lake water. It could also be used to verify the quality of certain products.

According to the creators, in the future, Hypertaste could even detect the fingerprint of other even more complex liquids. In the long term, it could, for example, take urine samples from a person and help to obtain an evaluation of the metabolic fingerprint, which can be understood as the sum of all the small molecules present in a living organism. As this chemical information is constantly changing (depending on factors such as lifestyle or nutrition) this metabolic fingerprint could help to have a ‘snapshot’ of a person’s health at a given time.

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