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AI in Cybersecurity

The Role of AI in Cybersecurity

AI in Cybersecurity, or protecting data from digital threats, is just as important as storing data in digital sources. Cybersecurity involves multiple layers of protection with different systems complementing each other’s security forces to create a mesh and trap any potential threat or attack. These attacks usually have the intention to access vulnerable data, extract money from the sources or stop necessary functions of the system.

We need to have strong cybersecurity as cyber-attacks cause identity theft, extortion of money and failure of critical operations in big industries. Cybersecurity works on three levels, namely, detection of a threat, investigation of possible damage and remediation.

Impacts of AI in cybersecurity

With AI development being used in every industry because of the flexibility in its applications, it is often considered a useful tool in cybersecurity. AI can form patterns and detect threats in a better way as compared to human beings. Here are a few positive impacts of AI in cybersecurity.

  • AI Biometrics– Biometrics has often been dubbed as a foolproof method against cyber-attacks. However, often there are security breaches due to 2D facial recognition, partial fingerprint recognition and so on. Introducing AI in biometrics is a novel way to learn about the user’s characteristics and ensure user-only access to data. This can be done by typing dynamics (the speed and method in which a user types into the keyboard), 3D facial recognition techniques, and gait recognition (where the way a user walks is checked). These methods form a more secure and sophisticated approach towards biometrics with the help of AI. 
  • Malware behavior recognition– Due to the creation of huge amounts of malware every day, it is difficult for conventional methods to keep track of the new malware created. Due to this gap between malware generation and updating the system with the new malware protection, the probability of an attack increases. Introducing AI can help in this case. When AI is taught about previous malware attacks, it studies the behavior and characteristics of the malware. When it detects a similar behavior in the system, it immediately catches the threat and attempts to isolate it from the system faster than traditional methods. 
  • NLP learning– Numerous platforms constantly research new techniques against cyber-attacks. It becomes impossible for people working in cybersecurity to read and understand articles, papers and news related to cyber threats and cybersecurity daily. With the help of AI, we can use its natural language processing feature to comb through the sea of information and find insights on detecting anomalies and building prevention strategies. This helps companies to be updated with the latest risks in the given time frame and up their security against any attacks. 
  • Multi-factor authentication– Multi-factor authentication is necessary for a company where security works in various layers because of different levels of access given to different users. Owing to this, there are times where security systems fail to identify the user or give a user more access than necessary. This is a major problem when especially vulnerable data is stored. So AI steps in and introduces multi-factor authentication which is dynamic and works in real-time. AI modifies access privileges based on the location and network of the user. It collects information on the behavioral patterns of users to provide access to different depths of data. 
Software Engineers - development team

Are there any downsides of using AI in cybersecurity?

Even though AI seems to be a boon to the cybersecurity industry, there are a few drawbacks to the application of AI. 

  • For AI to work accurately, it requires huge chunks of training data. This includes records of all previous malware attacks, malware codes, non-malicious codes, a behavioral anomaly in data, etc. Furthermore, the industry requires the memory size to store such data, computing power to detect useful data from non-useful data and forming data sets out of it such that the AI can learn. Such data collection takes a long time and a lot of companies can’t afford these resources because they can be very expensive. 
  • Another drawback is the counter development of malware using AI. Hackers can use AI to improve their malware and design it to pass undetected through the system. AI-proof malware can negatively affect on the industry as it can lead to potentially dangerous scenarios. The AI aided malware will constantly learn from AI aided cybersecurity and further improve itself to dodge detection. This in turn also has a backlash on systems that work without AI as they will become increasingly vulnerable to cyber threats owing to sophisticated malware attacks. 

These drawbacks can revert all the advantages AI has, thus in today’s scenario we need to have an intermediate approach. 

  • Combining AI with traditional cybersecurity by employing professionals who are aware of the nitty-gritty of cyber threats. Such professionals can back the AI while it learns to fight attacks. In case AI misses, a professional is always available to take over. 
  • Performing regular security checks after the addition of updated anti-malware software to ensure there aren’t any gaps where the data is vulnerable to theft and misuse. 
  • Installing malware scanners and firewalls, using URL filters for malicious data, and monitoring and restricting exit traffic at the time of potential threats.

We observe that AI has tremendous untapped potential in cybersecurity, but at the same time, hackers are trying to develop their malware to beat the AI aided cybersecurity. Until we reach a point where our AI can successfully restrict the malware completely, we must resort to practices that combine traditional methods along with AI aided ones. 

cto - Chris Gibas

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