Big data has arguably become increasingly influential in industries. It is not only useful in providing insights for critical decision making but also predicting future product development trends.
On the other hand, the Internet of Things has also played a role in driving more operations into the digital spectrum. Amid all these digital advancements, data security remains a primary focus for data centers and IT admins. In this case, artificial intelligence (AI) has played an instrumental role in improving more modern data security technologies.
In fact, the development of advanced machine learning algorithms has boosted the ability of computers to address network vulnerabilities and detect fraud.
SML vs. UML
Unsupervised machine learning (UML) and supervised machine learning (SML) form the two principal categories of modern artificial intelligence. The SML approach allows a fraud detention model to learn characteristics related to risky activities and read historical data.
In turn, the model utilizes these abilities to avert fraud through halting activities that follow similar patterns and analyzing real-time ones. Nonetheless, experts have observed that algorithms in SML only prevent those suspicious activities, which follow past data patterns. In reality, most new security incidences are less predictable.
Conversely, unsupervised machine learning (UML) can apply more sophisticated computational techniques to extra secure data networks. This attribute means that UML models can utilize complex computer systems such as fuzzy logic, deep learning, neural networks, and others. UML models can also process larger amounts of data.
Use of Artificial Intelligence in Data Security
Speculations from numerous data experts suggest that artificial intelligence will be an avoidable inclusion into cybersecurity.
The reason for such expectations is owed to the increased dependence on data analytics since humans are incapable of sifting through the numerous amounts of data as far as speed and accuracy are concerned. Another reason for the integration of artificial intelligence in information security is the speculation that potential cyber-attacks in the future may include similar technology.
Recent Case Studies
Currently, financial institutions are already implementing machine-learning procedures in a bid to avert transaction fraud. For instance, IBM’s fraud detection department utilizes machine learning to create a model to analyze real-time transaction activity and process financial data. Additionally, the IBM Cognitive SOC, which is powered by Watson, is another AI currently in use.
Artificial intelligence is undeniably a high-speed development field that has displayed its effectiveness in data security. Healthcare facilities and financial institutes make up some of the industries that have experienced first-hand the rewards of artificial intelligence in cyber defense.
For this reason, experts anticipate machine learning to be a significant aspect of security technologies in the future.
Source IT Chronicles
