Big data has become more influential in industries, providing insights for key decision making and predicting trends for future product developments; the Internet of Things, as well, has pushed more operations into digital spectrums like the cloud. Amidst modern digital advancements, data security has been a core focus for IT admins and datacenters, and artificial intelligence has helped enhance more recent data security technologies. Thanks to developing high-performing machine learning algorithms, computer systems are now more capable of detecting fraud and addressing network vulnerabilities.
SML vs UML
Two main categories of modern AI are supervised machine learning (SML) and unsupervised machine learning (UML). A fraud detection model that uses the SML approach reads historical data and learns characteristics or features associated with risky activities. The model uses that to prevent fraud by analyzing real-time activities and halting those that follow similar patterns. In SML, algorithms are improved each time it “learns” from an activity; however, one flaw with SML which experts have pointed out is that algorithms are only preventing suspicious activities that follow previous data patterns, while in reality, many new security events are less predictable.
Unsupervised machine learning, on the other hand, can apply more complex computational procedures to more precisely secure data networks. For example, UML models can make use of sophisticated computer systems like neural networks, deep learning, fuzzy logic, and more. Moreover, UML models can process an even larger amount of events. Both UML and SML artificial intelligence technologies are being developed for data security.
The promise of AI for data security
Many data experts have speculated that AI will be an inevitable addition to cybersecurity. One reason is that as big data developments progress and more companies are leveraging data analytics, businesses are generally dealing with perhaps billions of datapoints. That amount of data is essentially impossible for humans to accurately sift through; and even if the task was feasible, it is impractical compared to the accuracy and speed of what machines can do.
Another reason AI is gaining more recognition in information security is that thought leaders have speculated that it’s possible future cyber attacks may incorporate AI. This means digital vulnerabilities might only be effectively addressed with machine learning. While an endpoint protection platform can help businesses address standard security issues, artificial intelligence can apply more advanced algorithms for digital defense. Standardization of technologies formed by leading tech giants can help alleviate security problems, but it’s hard to imagine that future without AI.
Current case studies
Machine learning techniques are already being implemented for financial institutions to prevent transaction fraud. For example, IBM’s fraud detection solution uses machine learning to process financial data and build a model to analyze real-time transaction activity. Activities are given a risk score, and transactions are stopped and investigated immediately if necessary. One of IBM’s clients is a large US bank that has reported a 15% increase in fraud detection, 50% decrease in false fraud alerts, and 60% increase in savings.
Another AI in live action is IBM Cognitive SOC powered by Watson. It’s designed to probe the net for unstructured data—places like blogs, websites, and forums—and to gather insight on the latest security information. It also analyzes large amounts of structured data, e.g. security events from multiple sources, and uses sophisticated logic to help companies prevent threats. To put its computing power into perspective, Watson cognitive security processes data from 75K+ documented cyber security events, 10K+ annual security research papers, and 60K+ monthly security blogs, then uses that data to build a highly advanced cyber defense. For the public, IBM’s latest security solution sheds light on how digital security is likely going to move forward with machine learning.
Artificial intelligence is a field in high speed development that has already shown its promise in data security. Financial institutes and healthcare facilities have experienced the benefits of AI in cyber defense. With big data and analytics a critical aspect of business operations, experts speculate that machine learning will be more core to security technologies in the near future.