Cybersecurity is one of the industries that has benefitted from the advancement in the field of artificial intelligence (AI). The use of AI has led to the development of new tools that have been able to stop cyberattacks faster than before and it has also led to the rise of cybersecurity players like Darktrace.
While the cybersecurity industry has benefitted from AI advances, the cyberattack or threat vector has also evolved at the same time. This has resulted in the need for companies to constantly analyse and improve their cybersecurity position and to reach there, companies need to adopt technologies that rely on more than human intervention.
In the age of information security, AI and machine learning have already become indispensable technology. Artificial intelligence is helping companies fight malware menace to phishing attacks. Here is a look at how AI is being used in the field of cybersecurity right now.
Detection of new threats
Artificial intelligence (AI) can be used to spot cyber threats and even malicious attacks. In order to spot evolving cyber threats, traditional software systems are no longer useful since they fail to keep pace with the number of malicious attacks and malwares being created each week. AI can complement traditional software with their ability to learn continuously.
With threat vectors evolving, companies are using sophisticated algorithms to train AI on detection of malware or detect the tiniest behaviour of malware or ransomware attacks. AI can do this before the threat vector enters a system and even run pattern recognition.
Cybersecurity as an industry is moving away from reactive approach to proactive approach and AI is central to success. AI can be used for superior predictive intelligence with natural language processing capable of curating data on its own. An AI algorithm can learn about new anomalies, cyberattacks, and even prevention strategies.
Cybercriminals are getting better at their act and with AI, cybersecurity companies are getting better too. With an AI-powered cybersecurity solution, companies get the advantage of having the latest knowledge about an industry-specific danger and formulate solutions to circumvent those attacks.
Respond to bots
Another area where AI becomes useful is tackling the challenge posed by bots, which make up a huge chunk of internet traffic now. From account takeovers with stolen credentials to bogus account creation and data fraud, bots are a real challenge and manual response is no longer sufficient.
AI and machine learning can be used to build a thorough understanding of website traffic and distinguish between bots like search engine crawlers and bots that are a menace. The ability of AI to analyse a vast amount of data rapidly makes it ideal for tackling bots using supervised learning and behavioural patterns.
We are in this phase of technology adoption where multiplication of devices is happening rapidly and seemingly every device is now connected to the internet. In this rapid multiplication phase of devices, AI can help by accurately determining the number of devices that are part of a network and can break these devices into users and applications.
With the help of this inventory, AI can then determine the threat exposure for each device or user and predict how and where the compromise could happen. This risk prediction can be useful in stopping the breach even before it occurs. AI systems also offer prescriptive insights to configure and improve controls and processes to reinforce cyber resilience.
Improved endpoint protection
The world woke up to a new reality in 2020 where people suddenly began working from their home, Airbnb, a cafe, or from their friend’s bedroom. This hybrid and remote work culture is here to stay and that means people will be working from devices that may or may not be authorised by an organisation and that means, there is a need for better endpoint protection.
AI helps with that too by making endpoints more secure by establishing a baseline of behaviour for each and every endpoint through repetitive training. This process is different from a traditional antivirus, which relies on digital signatures. If the definition of these signatures is not updated then the security of the device becomes questionable.
Everytime there is a new type of malware attack, an antivirus can protect an endpoint device only when its signature is updated. With AI, it is possible to flag an attack and even get support from a system administrator. The AI works endlessly to ensure that the endpoint is always protected against all the known threat vectors as well as those gaining traction.
Does AI have any disadvantages?
AI is truly a game changer for the cybersecurity industry and it only makes cyber intelligence more robust. However, that improved and assisted intelligence comes with a huge cost, in terms of financial investment as well as deputing resources.
One of the challenges companies face is accruing distinct sets of data required to train an AI system. Companies need access to malware codes, non-malicious codes, and anomalies to train their AI algorithm and deliver proactive cyber intelligence. The biggest downside being that even cybercriminals have access to AI and as they improve their threat vector, companies need to evolve their AI at twice the speed.
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