Artificial intelligence in cybersecurity: Pros and cons

What is artificial intelligence?

Artificial intelligence is intelligence exhibited by machines or other non-living entities, or the creation of cognitive functions in an artificial mechanism. AI systems can be employed in a wide range of areas ranging from robotics to AI-driven search engines, from economics to cybersecurity.

Subsets of AI

Here are a few essential concepts associated with AI technology.

Machine learning

Machine learning is the ability of a computer to identify patterns in data and to use those patterns to perform tasks and solve problems. Such a system learns from its algorithms and develops the ability to make predictions or decisions that haven’t been programmed. For instance, programs such as Voice AI use machine learning to understand and then reproduce complex human speech patterns, and chatbots such as the Replika chatbot use this technology to mimic the way people write and communicate by text. Similarly, machine learning helps Amazon Astro robot accomplish such advanced functions as navigating around the household and recognizing various people and objects.

Due to the ability to rapidly process large amounts of data, cybersecurity systems with integrated machine learning can help predict and prevent attacks more accurately.

Expert systems

Expert systems are programs emulating the decision-making process of human intelligence to solve various issues. They are designed to solve complex problems by reasoning through bodies of knowledge. An expert system usually has two subsystems — the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine uses the rules to deduce new facts.

Artificial neural networks

Artificial neural networks employ paradigms allowing a computer to learn from observational data. It emulates the biological processes of the human brain. Neural networks consist of artificial neurons that send signals. The neurons also have a weight that adjusts as the learning proceeds. The weight can increase or decrease a signal.

Artificial neural networks are used for system identification, quantum chemistry, facial and image recognition, sequence recognition, data mining, and other situations.

Deep learning

Deep learning is a subset of machine learning based on artificial neural networks and representation learning. It uses multiple layers to progressively extract higher-layer features from input data. So, deep learning can identify specific differences and aspects of data (for example, provide quite accurate image recognition).

Three different AIs

Today AI can be distinguished into three types.

Assisted intelligence

Assisted intelligence is the most basic level of AI. Its primary aim is to automate processes and help in decision-making by using the power of big data, the cloud, and data science.

Assisted intelligence is not self-sufficient, because it requires constant intervention from human users. It just improves the processes already running. It enables people to be more productive and efficient in the things they already do.

For example, navigation systems like Waze use assisted intelligence to speed up the processes of route finding and distance calculations. We could use navigation without AI too, but it would take much longer and require more effort.

Augmented intelligence

Augmented intelligence enables people to do things they couldn’t otherwise do. It is like a collaboration between machines and humans. Augmented intelligence platforms can process tons of complex data to provide experts with multi-angled information about an analyzed issue. It uses machine learning and predictive analytics not to replace human intelligence but to enhance it.

For example, augmented intelligence can be used in the medical field to reduce the possibility of human error or in financial services to calculate customers’ needs and risks.

Autonomous intelligence

Autonomous intelligence can operate without human input or intervention. It is the most advanced type of artificial intelligence. While this type of AI is no longer just a sci-fi fantasy but a reality, not all organizations completely trust AI-powered systems and implement them in their IT infrastructure.

So autonomous intelligence is usually employed as an adviser on which experts base their decisions.

AI in cybersecurity

Cybersecurity professionals can apply AI in several ways, but there are also unique challenges.

The uses of AI in cybersecurity

• AI can identify the device owner’s behavior patterns via machine learning. If it notices something unusual, it may suspect that someone else is using the machine and prevent it (however, this can also be used as a tracking method).
• Machine learning capabilities and large databases can also help to detect threats and vulnerabilities way more efficiently. AI for IT operations (AIOps) can monitor patterns in web traffic and alert the user or admin if it notices something suspicious. Machine learning can also help to accumulate extensive database for more precise pattern identification.
• AI surpasses human monitoring capabilities in terms of quality and quantity. It eliminates the human error factor, an operate 24/7, and can process massive amounts of data in a short amount of time. It’ll take lots of work off experts’ hands and enable them to concentrate on other tasks.
• AI can eliminate the need to use passwords and the dangers of them being snatched. Biometric identification systems use AI to identify the legitimate owner using facial recognition, fingerprint, or other similar techniques. For example, they can memorize even the tiniest details of your facial patterns. Thus, your device may identify you even with facial hair or headwear.
• AI can help maintain an accurate and detailed record of your devices, applications, and users with different access levels.
• Better endpoint security. AI is beneficial for protecting the endpoints of remote devices. AI activity is not based on signature, so it is more useful for discovering new malware types and preventing malware attacks. AI learns how to recognize behavioral patterns of malware or other suspicious processes, so it can quickly adapt and constantly build up its virtual muscles.

The challenges of AI in cybersecurity

• AI can also be used by hackers for malicious purposes and to initiate even more sophisticated and large-scale attacks. AI also can help to identify and exploit vulnerabilities more quickly and efficiently.
• AI-powered biometric recognition can be a threat as well. Advanced scanning techniques in apps like Lensa can provide very detailed data on your appearance to third-parties. They can also be used for surveillance, tracking and other breaches of our privacy. Authoritarian countries and regimes might use these techniques to monitor their adversaries.
• The cost of hiring a team of experts to implement AI solutions is currently quite high. Also, some of these technologies are still in their experimental stages, so relying on them is risky.
• Limited information accuracy can result in AI hallucinations. Artificial intelligence might misinterpret data or not understand the lack of it and provide inaccurate threat assessment to security systems.
• AI has enabled more data gathering and processing than ever before, enabling third-party entities to have even more data on us. It may introduce more privacy and security issues than it solves.
• Virus developers can also use AI. Those AI-powered viruses can potentially cause more damage than other viruses because they may be able to detect antivirus software, attack its code, and bypass it.
• AI can also be employed for social-engineering attacks. Scammers can use it to mimic human language or produce fake images or videos to trick users into sending confidential data. These techniques can be used for cyberbullying too.