How Artificial Intelligence is Changing Cybersecurity

The impact of AI on cybersecurity continues to be a hot topic of discussion within the information security industry. Cyber-threats are rapidly increasing in volume. In part, this is because the attack surface for cybercriminals is huge, and it continues to grow and evolve at a lightning pace. Every year billions of cyber-attacks are launched with a wide variety of motives, and new threats with more sophisticated tactics or methods are trialed to bypass existing security systems.

Traditional techniques used in cybersecurity are ineffective against more advanced attacks. With research finding that many mid-sized companies get alerted to over 200,000 advanced attacks daily, it is clear that the future of cybersecurity cannot rely on human intervention alone as a method of protection against threats.

Across several industries, many large and small companies are adopting artificial intelligence (AI) technology, and many more are looking to implement AI tools in the near future in the fight against cybercrime. It has been suggested that we are on our way to reinventing cybersecurity with artificial intelligence, and existing AI tools such as machine learning technology are becoming essential for cybersecurity. 69% of companies agree that the use of AI in cybersecurity is necessary for efficient response to cyber-attacks. The use of AI and machine learning for cybersecurity is expected to grow at a compound annual growth rate (CAGR) of 23.6% before 2027 and is predicted to reach $46.3 billion by this time.

However, the power of AI in cybersecurity is a double-edged sword; while businesses use it to strengthen their defense, cybercriminals similarly use automation advancements to their advantage. The result is a cyber-arms race between good and evil. The following will discuss artificial intelligence and its impact on cybersecurity, and how AI can both increase and decrease security risks.

Artificial intelligence and machine learning applied to cybersecurity

AI – how does it work?

Artificial intelligence is intellect or brainpower demonstrated by machines, as opposed to humans or other non-human animals. An application of artificial intelligence often used by security professionals is known as machine learning. Designed to imitate human intelligence and the way that we learn, machine learning technology makes use of data and algorithms to gather knowledge and gradually improve accuracy over time.

How is this applied to cybersecurity?

AI and machine learning can be used in cybersecurity for threat intelligence, often to recognize known threats, detect previously unknown patterns of behavior and flag anything suspicious, or detect behavior that is anomalous to the norm. Using AI-powered systems to improve network security and vulnerability management provides rapid insights and reduces response times to security risks.

The benefits of AI on cybersecurity

Sophisticated bot management

Bots make up 50% of today’s website traffic. Although some bots are beneficial to website performance, many bots present on your website have malicious intent.  Using machine learning techniques, sophisticated bot management solutions analyze website traffic for malicious activity to detect known threats, as well as flag up any suspicious or previously unknown user behavior. AI is essential to bot management since the sophisticated nature of bots can bypass traditional methods of security. As bot threats rapidly evolve and cybercriminals continuously find new methods of bypassing security infrastructure and infiltrating systems, machine learning allows businesses to stay one step ahead by identifying anomalous behavior.

Learn more about Netacea’s sophisticated bot management solution.

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Known threat identification and detection

Supervised learning is a machine learning model characterized by its use of labeled data, used to teach algorithms to classify data, or predict accurate outcomes based on the training data. This type of machine learning can improve security by detecting known threats and predicting if a known threat is going to occur based on existing data.

Detecting unknown and anomalous behavior

Unsupervised learning is a machine learning model used to analyze and group sets of unlabeled data. This type of machine learning can analyze data for previously unseen or undetected patterns, without being explicitly programmed or requiring any human intervention. Unsupervised learning allows us to detect suspicious behavior, or patterns of behavior relating to new or previously unknown attack vectors, by comparing the behavior of one data stream to others in the system.

Reducing pressure on cybersecurity workforce and skills gap

The cybersecurity workforce is currently facing a period of crisis, with not enough security workers available to fill open positions. The main reasons for this are an increase in resignations – due to burnout and increased workload – as well as a lack of individuals entering the field or looking to pursue a career in information security following mainstream education.

The use of AI technologies in cybersecurity helps reduce the workload significantly by processing copious amounts of data quicker than a human alone, as well as reducing the amount of time taken to respond to a threat.

The drawbacks of AI in cybersecurity

Workforce needed to build and maintain AI systems

While AI is certainly much more advanced than it was a decade ago, the application of artificial intelligence to cybersecurity is relatively new, and the ways in which it can be used optimally are continuously being developed. For this, a huge workforce is needed, and as previously mentioned there is currently a shortage of security experts to make up this workforce.

The use of AI in cyber-attacks

Cybercriminals are also making use of artificial intelligence to help build highly sophisticated automated attacks, bypass network security systems, and avoid detection.  The use of AI for malicious exploitation of other AI-boosted systems has been successful in the past. For example, cybercriminals were able to copy the machine learning model for Proofpoint Email Protection – following this, they were able to manipulate the system to allow malicious emails to bypass the security measures. The misuse of AI technology is a worrying trend within cybersecurity, but it highlights the importance of keeping your AI security system up to date and regularly checking the system is working optimally.

Human intervention is still required

AI-based cybersecurity systems cannot entirely replace security professionals.  The application of artificial intelligence to cybersecurity works best when there is an element of human intervention. AI systems like machine learning help security experts to detect and prevent malicious behavior occurring, which reduces security risk and data breaches.

Essentially, AI tools and cybersecurity teams should complement each other; the security team should support the AI technology by keeping it up to date with any known threats and regularly checking the system is working optimally. In turn, the AI security systems alert cybersecurity teams to any suspicious or malicious behavior, previously unknown or unseen patterns in the data, and any potential security vulnerabilities.

Is AI changing cybersecurity for the better?

With security challenges increasing, and the attack surface for cybercriminals continuing to grow, it is clear why AI is hailed as the future of cybersecurity. That being said, to stay ahead of security risks AI tools need to be maintained and monitored by security teams to ensure optimum success. As it stands, the impact of AI on cybersecurity is huge, and as technology continues to develop, the outlook on how AI continues to change cybersecurity remains significantly positive.

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