Whether we want it to or not, the world around us evolves into a giant blob of people and devices connected to each other at a rapid pace. I’d like to imagine the phenomenon of digitalization as a wave that sweeps over us more with each passing day- with each crash of a wave representative of yet another technological advancement.
Perhaps the most significant technological advancement made in the past decade or so has to be the introduction of artificial intelligence. In addition to AI’s popular applications (Apple’s Siri, Amazon’s Alexa)- the revolutionary technology of AI and Machine Learning (ML) has had a profound impact on the cybersecurity realms as well.
However, this works on both sides of the spectrum, since more and more cybercriminals are relying on AI for the execution of successful breaches, malware and phishing attacks As cybercrimes increase in sophistication as is the case recently seen with hackers deploying highly risky phishing attachments- enterprises and security analysts need to realize the promise of artificial intelligence, particularly the promise of advancing cybersecurity and devising better and more accurate threat analytics.
Bearing witness to the untapped potential that AI has in the realm of cybersecurity is the fact that technologies such as fingerprint print, retina and palm scans are based on an AI software, which works based on AI software. Moreover, when we take into consideration that most enterprises use the notoriously vulnerable cloud computing systems to store sensitive information such as passwords, social security, and credit card numbers- turning to AI becomes more of a security need rather than a ‘miscellaneous expense.’
When it comes to the integration and implementation of artificial intelligence and machine learning in the cybersecurity world, unfortunately, there’s been a lot of speculating and theorizing on what to do, instead of coming up with practical solutions that offer enterprises instant insights, along with providing them with apt security updates.
An often overlooked aspect of AI in the cybersecurity context has to be machine learning, which can turn the input it receives into algorithms that enable companies to learn how to react and respond to different cybersecurity threats.
The greatest advantage offered by the perspective provided by Machine Learning is that it significantly improves the steps enterprises and companies take to detect any malicious programs and vectors present within the cybersecurity infrastructure.
Up till recently, companies have relied on a signature-based approach, which means that the cybersecurity team employs a signature-based approach that targets even the slightest hint of any malicious program, and then proceeds to extract a unique fingerprint (signature) for each individual threat detected. After the detection process is dealt with, the IT team is tasked with monitoring all the devices on the network, and ensuring that the marked signatures do not appear again.
Another advancement that reveals itself at the horizon of cybersecurity has to be malware scanning, which works quite similarly to how machine learning and artificial intelligence operates. Unlike the signature-based approach, we’ve referred to above, this machine learning-based approach offers users with certain flexibility since the algorithms ‘think’ and train as per the situation that they are in. Another added benefit offered by a more artificial intelligence-centric approach is that the scanning tool targets a wider variety of malware when compared to the traditional signature-based approach.
In addition to the increased scope of a machine learning approach in malware scanning, an AI-based approach also makes for greater versatility and reliability, since the conventional signature-centric approach allows for a greater chance of hackers duping the malware scanning tool with a fake signature and permeate the network.
Furthermore, since AI’s are evolving at a rapid pace, technologies such as Deep learning, also stand as a shining beacon of light, since deep learning makes use of algorithms that and adapt to changes in a particular setting on-the-go. An example of this is the way that Google’s Gmail spam filter works- by scanning all incoming emails and detecting sketchy emails, and filtering them as spam.
In the current cybersecurity climate, the tools we’ve been employing so far have been inconsistent as far as their practical applications are considered. Perhaps the most significant underlying reason behind this is the lackluster understanding that most individuals have of cybersecurity tools.
With an AI-centric approach to cybersecurity, however, understanding the fundamental functioning behind the cybersecurity tools being used is much easier since the process of how an AI software or program works to come up with cybersecurity solutions can easily be broken down into four components, which are the following:
- Learn: The primary way that AI software helps propagate security is by learning and analyzing millions of structured and unstructured data sources or inputs. The AI software then improves its understanding of the threats and cybersecurity risks facing organizations.
- Reason: After the AI has ‘learned’ and analyzed data from an arsenal of data sources, the AI software begins the process of reasoning. In simpler words, based on the insights the AI has gained so far, it starts to join the dots and starts making connections between suspicious IP addresses, malicious bugs, and internal threats.
- Augment: The process of augmentation refers to the AI software performing a holistic analysis of all the threats and risks facing the organization. The complete analysis enables an organization’s security team to eliminate futile tasks and saves redundant expenditure on security in places where it is not needed. Moreover, cybersecurity analysts can also detect abnormal behaviors better by cross-checking the threat with the data stored on their systems.
- Authorize: After the AI software has completed the time-consuming processes of learning, reasoning and augmenting- the software then relies on an arsenal of factors to take user authorization to the next level. Instead of relying on a single-tiered approach to password authentication, the AI analyzes the keys used to access the network, along with the way the keys are being entered, amongst other factors.
At the end of the article, we’d like to reimburse the pitch we’ve been so far- using AI for the purpose of advancing cybersecurity. If it isn’t blatantly obvious by now, the security tools and techniques we rely on are in dire need of an upgrade; and AI and Machine Learning are the messianic figures the cybersecurity world needs right now.
Not only is the AI software versatile, and reliable- the holistic approach it takes makes cybersecurity practical, while virtually rendering it impossible for any hacker or malicious agent to permeate an enterprise’s cybersecurity infrastructure.