Artificial Intelligence in Cybersecurity

Companies around the world are working hard to find new solutions to fight and reduce cybercrime. AI is proving to be the most suitable technology for solving some of the most challenging problems in the cybersecurity industry. By using artificial intelligence and machine learning to automate threat detection, organizations can respond more effectively to cyber-attack threats than traditional software approaches.

Cybersecurity is the practice of preserving machines like computers, mobile devices, and other digital assets from malicious attacks. Over the past decades, cyber-attacks have become an evolving threat to businesses and individuals. The development of technology has resulted in greater interconnectivity of business systems and an increase in individual presence on the Internet. Cybercriminals take advantage of this change as there are more ways to access or destroy sensitive data. As a result, effective cybersecurity is becoming a necessity for businesses of all sizes and industries.

With its ability to understand and analyze a given context, artificial intelligence can help detect anomalies or abnormal behavior, revealing attacks, and thus strengthen protection, detection, response, and remedial tools: increasing the detection rate, early detection of attacks, improved adaptability to constant IT changes.

High cost of cybercrime

Imagine that more than 4,000 ransomware or viruses appear in the world every day, the number continues to grow, and around 80% of these new threats are used only once. Moreover, ransomware as a service is becoming more common on the darknet at often ridiculous prices. Such intensified activities of cybercriminals generate huge costs.

The costs of cyberattacks are increasing year by year. This is due to the data contained in both the “2019 Official Annual Cybercrime Report”. Some of the costs of cybercrime include data corruption and destruction, money theft, intellectual property infringement, theft of personal and financial data, misappropriation, fraud, and even loss of reputation. Cybercrime damage is expected to cost $ 6 trillion by 2021, compared to $ 3 trillion in 2015.

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Protection against threats is all the more difficult as companies, taking part in digital transformation, increasingly expand the potential field of attack. Without the AI analysis of behavior and side movements, we can no longer cope with the growing number of attacks every day, although more or less well-targeted.

The impact of COVID-19 on cybersecurity

COVID-19 shed light on the lack of preparation of companies to deal with cybersecurity issues. In addition, the shift to teleworking and the increase in digital channels have resulted in a growing number of cybersecurity problems.

Many companies and individuals are in a rush to refresh their systems and protect their data. This massive increase in threats has increased the need for security audits and vulnerability testing. Companies are frequently turning to AI to help overcome the growing dangers of cyberattacks. However, it is mathematically impossible for large corporate IT security teams to control all threats and sift through hundreds of thousands of vulnerabilities. This is why automation through artificial intelligence and the use of machine learning or ML algorithms are now actively used to eliminate cyber threats.

AI in attack and defense

The potential uses of AI in cybersecurity are numerous; therefore, it is necessary to invest in and interest in it (especially in research), and for the end-user who wants to protect his IT system, it will be more and more necessary to understand its potential, limitations, but also limits.

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One of the main reasons for the interest in artificial intelligence is that attackers are already taking advantage of its possibilities. The best-known examples are those related to deepfakes, generated by the latest generative antagonist network algorithms; these audiovisual recordings make it possible to make a person say anything literally. Hence the concern about the possibility of bypassing biometric access control systems.

AI is proving to be the most suitable technology for solving some of the most challenging problems in the cybersecurity industry. By using AI and ML to automate threat detection, organizations can respond more effectively to cyber-attack threats than traditional software approaches. In addition, by eliminating the need for human intervention, cybersecurity engineers can prioritize other aspects of protection that may deserve more attention. Artificial intelligence can also use data on current cyberattacks in different fields and industries worldwide to continuously improve performance and detection rates. Below are some examples of how artificial intelligence is currently being used in cybersecurity.

ANTISPAM

AI and ML enable the creation of more brilliant filters to detect and eliminate spam automatically. AI is relatively new to most of the email security market. However, as hackers continue to improve their attack methods, email protection solutions must adopt a predictive approach to threat detection. It is possible thanks to artificial intelligence.

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USER BEHAVIOR SAFETY PROFILES

By building personalized network personnel profiles based on user behavior, you can tailor security to your business. This model can then determine what the unauthorized user might look like based on an anomaly in user behavior. Specific characteristics, such as keystrokes, can form a predictive threat model. By exposing the possible effects of potential unauthorized user behavior, ML-oriented security can suggest recommendations for reducing vulnerable areas.

BIOMETRICS

Authentication techniques such as fingerprint, face, and iris scanners are increasingly used at work and home. AI helps to improve recognition accuracy and provides behavior insight to increase security.

BLOCKING BOTS

Bot activity can drain incoming website bandwidth. As a result, real users may experience website performance slowdown, resulting in loss of traffic and a loss of income from a business point of view. This is especially true in the e-commerce industry. However, you can block the bot network and effectively prevent their activity thanks to AI and ML security tools.

DETECTING THREATS

Advanced signature recognition can detect threats and viruses in real-time, strengthening security measures and enabling faster response.

NATURAL LANGUAGE PROCESSING

Artificial intelligence can draw information from articles and research to learn the latest security threats, hacking techniques, and prevention strategies.

Anticipating cybersecurity challenges in the coming years

AI will make a significant contribution to anticipating anti-regulatory challenges. Of course, it will not replace people in this, but it provides experts with relevant information and allows them to interpret content, including unstructured content, detect invisibility, weak signals, and automate many actions. There is no doubt that with technological development and the transition to digital channels, AI plays a more critical role in cybersecurity than ever before.

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