The rapid digitization accelerated due to the pandemic has brought many benefits, such as business agility and improved customer experiences. But there have also been negative effects like increased vulnerability to cybersecurity threats for your data and applications.
A cyber attack is a malicious and deliberate attempt to enter the computer and information systems of an individual or organization, disrupting the victim’s network for personal gain.
One of the biggest concerns regarding the development of artificial intelligence is the likelihood that attackers will militarize AI and use it to expand and amplify their cyber attacks. The landscapes of cybercrime and cybersecurity are changing rapidly and spurring the developments of AI to improve cybersecurity will be a major change to protect against cyber attacks.
Let’s take a look at 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 the analysis of comprehensive organizational data. Machine learning algorithms are constantly adapting and updating to identify threats to your system before its vulnerabilities can be exploited. These algorithms include all aspects of an organization’s infrastructure and network and possible attack scenarios.
Through learning, data analysis, and understanding patterns of behavior, algorithms can detect anomalies in the system and outsmart them before they can attack.
With constant training and updating, machine learning algorithms will be better able to predict and eliminate cyber threats.
User behavior analysis
Using AI and ML algorithms, user activity and behavior across web devices and platforms can be analyzed to understand their patterns of behavior. Any deviations or unusual activity will then be immediately flagged as suspicious.
The algorithms look for user patterns such as employee working hours, geographic location used to connect to the network, device IDs, typing or scrolling patterns on those devices, etc.
After you set a benchmark for normal user behavior or network traffic for your business, algorithms can detect activities that don’t follow this regular pattern and take additional action.
Traditional fraud detection techniques rely on predefined rules, which can lead to a large number of false positives, blocking genuine transactions and increasing the possibility of losing customers. Machine learning algorithms are used to improve this process with their abilities to analyze large amounts of transactional data and individual models.
AI and ML speed up fraud detection in real time and dramatically reduce the possibility of false positives. AI-based fraud detection is also able to implement root cause analysis and trace the origin of fraudulent transactions to their sources such as credit cards or ATMs.
Through the benefits of enhanced anomaly detection, AI helps businesses and financial institutions build trustworthy reputations and improve customer relationships.
Identity and access management
Passwords are a fragile method of security, 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 on 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 access our various accounts.
While biometric authentication was introduced as an alternative to weak passwords, cybercriminals have also found a way to trick this system. 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 users are in low light areas or are wearing accessories like hats, making it a much better and secure method of protecting your accounts.
Malware is intrusive software that includes viruses, Trojans, ransomware, and spyware. It is usually transmitted by email in the form of a link or file and is designed to cause significant damage to data and systems in your organization.
The traditional ML approach used feature engineering to analyze and extract the malware’s features and compare them to the default set to identify if it is malware. But this method does not take into account the evolution of forms of malware.
Deep learning algorithms and complex neural networks are being developed to counter these increasingly sophisticated malware threats. Such algorithms will analyze and compare the dynamic aspects of a malware against its static characteristics to identify anomalies and block the malware.
As the complexity of networks increases, artificial intelligence can ease the growing burden on enterprise vulnerability management operations through its combination of intelligent decision making and automation.
AI can help develop context-based vulnerability risk scores, proactively identify vulnerabilities, and leverage sentiment analysis to understand vulnerability exploitation patterns. This would significantly reduce the human effort required to assess risks and actively protect vulnerable assets.
In addition to the listed areas, artificial intelligence can also be used for endpoint protection, security recommendations, bot spam, and many other areas. The advancement of artificial intelligence solutions helps both parties in cyberspace – attackers and defenders.
As AI continues to evolve, businesses must continually upgrade their cybersecurity tools and processes to proactively block threats and protect their systems and data. Choosing the right technology and having resilient security procedures in place will help you avoid crises and stay ahead of potential attackers.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)