How Predictive Analytics and Machine Learning is Redefining Mobile Privacy
In recent years, we’ve become so dependent on our smartphones that we’ve begun to favour our devices overusing desktops. As we conduct our business on our handsets, like make transactions, share our personal data with trusted companies, and communicate with clients at work, it’s important to remind ourselves of the importance of keeping our information safe whilst on the go.
Our mobile phones have become part of our lives now, but as the technology available to cyber criminals grows, it’s vital to be aware of the dangers of leaving valuable information exposed on smartphones.
According to statistics, in the past 24 months, we’ve begun to favour our smartphones over desktops for online browsing. The accessibility and speed of mobile data have made it far more practical for us to conduct our business online via our mobile devices. However, this also means that the amount of sensitive information that can be exposed to cybercriminals whilst on the go is on the rise too.
Data breaches and identity theft reports in the US have doubled in the space of five years, with increases also being reported across the world. As criminals gain access to more powerful tools to access our private data, it’s essential that we ramp up our security in response.
This is where the technologies of predictive analytics and machine learning helps to bring greater security to smartphone users around the world. Let’s take a deeper look at how this smart tech helps to keep our devices safe:
The Essential Role of Machine Learning in Mobile Security
For any type of technology, device or network, automated systems can be excellent at learning, remembering and analysing large volumes of data quickly. This is because algorithms can be altered and introduced that enable existing and new data sets to acknowledge and react to signals in new ways.
Baber Amin is buoyant over the potential of machine learning in mobile security: “If the mobile app has its own AI engine, then it can have the complete business logic and user interaction all within the app or its back end. Alternatively, the underlying mobile OS could provide privacy-enhanced anomalous signals to registered apps, he explained.
Although machine learning and the AI framework the technology relies on are still emerging in the mobile security landscape, they’re already part of use cases that display a greater ability to identify predictive potential attacks, as well as improve application development to better prevent potentially vulnerable applications.
For instance, we can already see evidence of ML improving usability on voice-based portable devices and we can see machine learning anti-malware solutions entering the mainstream.
We’re also seeing VPNs incorporate ML into their offerings for mobile security, with many market leaders utilising artificial intelligence to learn to act proactively in the face of new and emerging security threats.
The Necessity of Predictive Analytics
Although machine learning is helping to remedy the emergence of more sophisticated security threats, predictive analytics has been identified as the science that’s gaining momentum across a range of industries in helping companies to modernise and reinvent their approach to the business they conduct.
This emerging trend is growing in the field of cybersecurity, and is helping to determine the likelihood of attacks against organisations and agencies in the near future whilst helping to set up appropriate defences before they take place. The power of predictive analytics is such that many security vendors are adopting the technology as the core of their security offering.
Although security breaches are becoming more prevalent today as we continue our transition away from our desktops towards more mobile-based browsing, so too has the quality of technology at our disposal to keep us safe online.
Through the adoption of machine learning and predictive analytics, we will have the power to continue living our lives through our smartphones without the fear of prying eyes peering into our data.