The rapid digitalisation accelerated due to the pandemic has brought numerous benefits like improved business agility and customer experiences. But there have also been negative effects like increased vulnerability to cybersecurity threats for your data and applications.
A cyberattack is a malicious and deliberate attempt to breach the computer and information systems of an individual or organisation, disrupting the victim’s network for personal gain.
One of the biggest concerns with the development of Artificial Intelligence is the probability that attackers will weaponise AI and use it to expand and boost their cyberattacks. Cybercrime and cybersecurity landscapes are changing rapidly and boosting AI developments for enhancing cybersecurity will be a major gamechanger to protect against cyber-attacks.
Let us go through some of the ways AI is changing the cybersecurity landscape.
Cyber threat detection
Machine Learning (ML), a subset of Artificial Intelligence, enables real-time detection of cyber threats based on analysis of complete organisational data. Machine Learning algorithms continuously adapt and update themselves to identify threats to your system before its vulnerabilities can be exploited. These algorithms understand all aspects of an organisation’s infrastructure and network and the possible attack scenarios.
With learning, analysing data, and understanding behavioural patterns, the algorithms can detect anomalies in the system and thwart them before they can attack.
With constant training and updating, the machine learning algorithms will get better at predicting and eliminating cyber threats.
User behavioural analysis
Using AI and ML algorithms, user activity and behaviour on devices and web platforms can be analysed to understand their behavioural patterns. Any deviation or unusual activity will be then immediately flagged as suspicious.
The algorithms look for user patterns like employee working hours, the geographical location used for logging into the network, device IDs, typing or scrolling patterns on those devices, etc.
After setting a benchmark for normal user behaviour or network traffic for your enterprise, the algorithms can detect activities that do not follow this regular pattern and take further action.
Fraud detection
Traditional fraud detection techniques rely on pre-defined rules, which can lead to a huge number of false positives – blocking genuine transactions and increasing the possibility of losing customers. Machine Learning algorithms are being used to enhance this process with their capabilities of scanning large amounts of transactional data and individual patterns.
AI and ML are accelerating real-time fraud detection and greatly reducing the possibilities of false positives. AI-powered fraud detection is also able to implement root cause analysis and trace the origin of fraudulent transactions to their sources like credit cards or ATMs.
With the benefits of improved anomaly detection, AI is helping businesses and financial institutions build a trustworthy reputation and improve customer relationships.
Identity and access management
Passwords are a fragile security method, and they are often the only barrier between hackers and user accounts. When it comes to setting and updating passwords, most people tend to use the same passwords across different accounts, use the same passwords for many years, save their passwords in a note file on your computer, etc. This lax password management makes it easier for cybercriminals to get access to our different accounts.
While biometric authentication has been introduced as an alternative to weak passwords, cybercriminals have found a way to trick this system as well. Artificial Intelligence algorithms are being developed to strengthen biometric authentication.
To prevent suspicious access, AI algorithms create a model of the user’s face and identify key patterns and correlations. The algorithm will also work when the users are in areas of low lighting or wearing accessories like hats, making this a much better and secure method to protect your accounts.
Malware detection
Malware is intrusive software that includes viruses, trojan, ransomware, and spyware. It is usually delivered over emails in the form of a link or a file and is designed to cause extensive damage to your organisation’s data and systems.
The traditional ML approach used feature engineering to analyse and extract the features of the malware programme and compare against the default set to identify if it’s a harmful programme. But this method doesn’t take into consideration the evolving forms of malware.
Deep learning algorithms and complex neural networks are being developed to counter these increasingly sophisticated malware threats. Such algorithms will analyse and compare dynamic aspects of a malware program with respect to its static features to identify abnormalities and block the malware.
Vulnerability management
As the complexity of networks increases, Artificial Intelligence can lessen the increasing burden on enterprise vulnerability management operations with its combination of intelligent decision-making and automation.
AI can help with developing context-based vulnerability risk scores, proactively identifying vulnerabilities, and leveraging sentiment analysis to understand vulnerability exploitation trends. This would drastically reduce the human effort required in assessing risks and actively protect vulnerable assets.
Final thoughts
In addition to the listed domains, artificial Intelligence can also be used across endpoint protection, security recommendations, bot spam, and many more areas. The advancement of artificial intelligence solutions is aiding both sides in cyberspace – the attackers and defenders.
As AI continues to evolve, organisations need to continuously upgrade their cyber security tools and processes to proactively block threats and protect their systems and data. Choosing the right technology and building resilient security procedures will help you avoid crises and stay one step ahead of your potential attackers.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YS.)
