There is currently a big debate raging about whether Artificial Intelligence (AI) is a good or bad thing in terms of its impact on human life. With more and more enterprises using AI for their needs, it’s time to analyze the possible impacts of the implementation of AI in the cyber security field.

We are living in a digital age where digital ecosystems form the backbone of our day to day lives. Cyber attacks are increasingly targeting the digital ecosystems. As advances in Artificial Intelligence (AI) and Machine learning (ML) move at breakneck speed, use of AI and ML is expected to grow at heartening pace in cyber defenses and will add tremendous intelligence and power to fight against cyber attacks.

Cyber security attacks keep growing at an alarming speed and keep getting more sophisticated with IoT attacks, data breaches, spam and phishing, crypto jacking, mobile malware and ransomware. Data losses and disruption due to these attacks continue to be significant for businesses and organizations, both in monetary terms and in damage to their reputations.

Here are few ways in which AI is helping companies become more secure:

Biometric Logins - Biometric logins are increasingly being used to create secure logins by either scanning fingerprints, retinas, or palm prints. This can be used alone or in conjunction with a password and is already being used in most new smartphones. Large companies have been the victims of security breaches which compromised email addresses, personal information, and passwords.

Detect Threats and Other Potentially Malicious Activities - AI can also be used to detect threats and other potentially malicious activities. Conventional systems simply cannot keep up with the sheer number of malware that is created every month, so this is a potential area for AI to step in and address this problem. Cyber security companies are teaching AI systems to detect viruses and malware by using complex algorithms so AI can then run pattern recognition in software.

Multi-Factor Authentication - AI systems can also be used in situations of multi-factor authentication to provide access to their users. Different users of a company have different levels of authentication privileges which also depend on the location from which they’re accessing the data. When AI is used, the authentication framework can be a lot more dynamic and real-time and it can modify access privileges based on the network and location of the user.

Prevention of Data Poisoning - Data poisoning is a possible challenge and threat in the use of machine learning. Here malicious users inject false, and misleading training data sets with the aim of confusing the algorithms and corrupting the trained models. Such data poisoning can skew ML algorithms and give incorrect results. There are several ways which can help defend against data poisoning issues. Detection and removal of outliers from training data sets is one of the ways to address the data poisoning attacks. Outlier detectors are trained independently and are not affected by poisoned data.

There are many start-ups focused on solving cyber security issues using AI and ML. Many are developing platforms which use unsupervised machine learning to analyse network data at scale and make billions of probability-based calculations based on the evidence that it sees. These platforms then, instead of relying on knowledge of past threats, independently classify data and detect deviations in patterns and flag them for attention. Some start-ups are using end-point sensors data for AI analysis, while others are working on mobile security and solutions to prevent automated web attacks.

As our lives become more linked with the digital world, cyber security will remain a constant. AI and ML will be some of our strongest allies in the fight, bringing a scale and speed of managing data.

Disclaimer - The views expressed in this article are my personal views derived basis my own experience and research and in no way indicate towards strategy of any organisation.