Artificial Intelligence in Cybersecurity: New Solutions for New Threats

Introduction

The rapid development of artificial intelligence is one of the most important technological trends of recent years and the years to come. Nowadays, some see AI and neural networks as a universal solution to many technical and social problems. Others believe that nothing good will come of it. As usual, the truth lies somewhere in the middle. Artificial intelligence is a two-edged sword that can be used in different ways, depending on whose hands it is in. Today, we're going to talk about how AI is being used in cybersecurity—and the cyberattacks it's preventing.

The Evolution of Cyber Threats

Over the years, cyber threats have evolved from basic viruses that had just a few lines of code to complex attacks on vital infrastructures and sophisticated data breaches. Now, attackers use AI to create malware, analyse user behaviour, develop bots that collect personal data, search for vulnerabilities, find passwords, spoof identities, bypass security systems, and so on.

Cybercriminals are using new technologies to launch cyberattacks by identifying network defences and modelling behaviour to bypass security controls. With the use of language models such as GPT, the textual content of malicious mailings is becoming harder to distinguish from authentic human-written mails.

Deepfakes are another novel type of threat that has appeared with the popularisation of AI. Criminals use artificial intelligence to create convincing videos and voice recordings of people that are hard to distinguish from the real ones. The process only needs a few images and as little as a few seconds of voice recording.

The rapid development of deepfake technology has created an opportunity for tech-savvy criminals to cause serious financial and reputational damage. Attackers are actively taking advantage of deepfakes for online and offline identity theft, public misinformation, financial blackmail, fraud and automated cyber-attacks.

These new threats are forcing cybersecurity to urgently adapt and widely deploy appropriate AI algorithms and use it to monitor suspicious activity, find vulnerabilities in systems, assess risks, recognise AI-generated material and instantly respond to attacks.

AI-Powered Security Mechanisms

The importance and role of AI in cyber security cannot be overemphasised. Here are some of the most common applications of machine learning and deep learning algorithms in cybersecurity:

Anomaly Detection: Machine learning algorithms are now widely used to analyze network behavior to detect unusual patterns that may indicate a cyber-attack. Predictive anomaly detector is based on a neural network that predicts the current values of certain parameters. The prediction is compared with the actually observed behaviour, and alerts or takes action if they don't match. The detector automatically learns from historical log data and detects anomalies without any prompts from a human expert. Such a detector can timely identify and flag most of the threats, including previously unknown or undetected anomalies.

Automated Response: AI and ML can find unusual behaviours and patterns that may indicate cyber threats, process large amounts of data to identify trends and predict threats, be used to automatically detect and block malicious traffic, and automate the search and remediation of vulnerabilities in systems. This helps minimize response time and prevent potential damage.

Predictive Analytics: AI algorithms can be useful in classifying and clustering system data for various requirements, such as compliance with information security legislation, building attack and vulnerability profiles, analysing data in the context of cyberattack episodes, and for further forecasting and forming cyber defence strategies. Based on this well-processed historical data, AI can predict potential security breaches before they occur, allowing for proactive measures.

Systems Assessment

AI tools can help with the evaluation and optimisation of large-scale IT infrastructure upgrades in a company. This may be very useful when installing a new system over a new on-premises environment, moving to the cloud, implementing new technologies or integrating different systems, and so on. AI algorithms make it easy to analyse configuration and setup, verify system compatibility, performance and, most importantly, security. It is virtually impossible to achieve similar results with manual testing alone.

Case Studies: Successful Implementations of AI in Cybersecurity

Cybersecurity companies such as Darktrace, CrowdStrike, and Palo Alto Networks have been successful in incorporating AI into their security solution offerings. Darktrace fights back in real time from the point an instant threat is detected; CrowdStrike identifies and stops malware behavior from being carried out through AI.

In particular, banks use AI to detect and prevent fraudulent transactions in real time. AI helps protect sensitive patient data by identifying and mitigating threats quickly. Retailers use AI to safeguard customer information and prevent data breaches.

Telecom: AI in Classifying Encrypted Network Traffic

A Fortune 500 telecom applied Snorkel Flow to classify encrypted network data flows into application categories, allowing them to train their own AI and custom model with their network data to be adaptive to dynamically changing threats and network policies.

In a nutshell, AI is empowering cybersecurity: detecting threats more effectively, averting data breaches, and optimising the security operation process for companies in every industry.

Challenges in Integrating AI into Cybersecurity

Despite its benefits, integrating AI into cybersecurity is not without challenges:

Data Privacy Concerns: To function, any AI leverages wide-ranging data gathering, including personal and sensitive information. The threat of data breaches has increased significantly, AI systems are desirable targets for cybercriminals, so there’s a greater risk of potential misuse of confidential data nowadays. In particular, the use of biometric data, such as facial recognition in AI applications, is a unique challenge to privacy. As AI continues to develop, companies are required to ensure that standards of privacy are met. Companies can use AI and still adhere to the regulations set by GDPR regarding the protection of privacy rights of individuals. To reduce some of the risks identified, anonymization and pseudonymization enhance transparency with respect to data processing, frequent assessments of data protection impact, and embedding privacy in the development cycle of AI.

High Costs: Implementing AI solutions cannot be called budget-friendly. This makes it difficult for smaller organizations to adopt these technologies. Another problem small and medium-sized businesses face is lack of relevant data to train ML models. However, with the growing popularity of AI grows its accessibility, and many companies around the world are working on creating affordable customizable solutions for those who cannot hire a team of engineers.

False Positives: AI systems can sometimes generate false positives, leading to unnecessary alarm and potential operational disruptions. When handling threat detection, AI is a double-edged sword. It has been proved that AI helps drop the number of false positives and false negatives, but in order to work correctly, it requires proper training and constant human monitoring. AI is a “black box”, and the result of its work remains unpredictable and sometimes generates glitches.

The Future of AI in Cybersecurity

Looking ahead, the role of AI in cybersecurity is set to expand. Innovations such as quantum computing and advanced neural networks promise to further enhance the effectiveness of AI-driven defense mechanisms. Continuous development and ethical considerations will shape the future landscape of AI in cybersecurity.

Artificial intelligence is evolving rapidly and can already solve most cybersecurity problems faster, more efficiently and more accurately than humans. The security sector is widely adopting AI tools, as hackers have already mastered the technology and are using it for various types of attacks, from scam to viruses and targeted security breaches.

At the same time, artificial intelligence is helping organisations respond instantly to threats, alleviate cybersecurity talent shortages, address system vulnerabilities in a timely manner, and build effective security strategies.

As practice shows, the best way to implement AI tools in an organisation's information security is through custom development of security architecture and the necessary software. Such a project requires a truly professional and experienced team. However, only building a system from scratch can ensure decent security and take into account all special business requirements in the software. Needless to say, building such an architecture and dedicating resources for development and maintenance is a huge investment, but in the long run it can prevent significant financial and reputational risks.

It’s important to remember that AI is not always used for defense, cybercriminals also push all the resources in creating sophisticated, automated AI-powered threats. And soon we may have to face a new type of attack that targets the defense ML algorithms themselves. So far, such attacks are rare, as they are complex and require specific skills. But their number will obviously grow with the increasing role of artificial intelligence systems in our lives.

Therefore, AI is deemed to be a powerful ally in the fight against cyber threats. While certain challenges and uncertainties remain, the benefits of AI in enhancing cybersecurity are undeniable.