Fake News Redirects Businesses’ Security Strategies

Cybersecurity is top of mind for many small-to-medium-sized businesses (SMBs) and corporations. Rightfully so with the latest headlines being dominated by Facebook’s data breach scandal. According to Bloomberg, the fallout from the recent scandal has led Facebook to lose $73 billion in market value in just six trading days. That narrative, along with continuing concerns over fake news, are pushing companies to take action and travel down a new path to help combat fraud.

Security Mind Shift

The massive billion-dollar losses brought upon Facebook is proof of the financial implications from content abuse and data breaches. Businesses know that fraud directly affects their customers which influences their top-line growth and eventually trickles down to the bottom line, so proactive security measures are beginning to become the norm.

According to Forbes, with the rapid rise of transformational technologies, keeping on top of emerging security and privacy threats is more challenging—and more critical—than ever before. As companies collaborate with a wider network of partners and meet new demands for 24/7 operations and greater transparency with customers, cyber security risks multiply. The scope, scale and impact of cyberattacks will grow in concert with increasing digitization.

AI and Machine Learning Can Help

Machine learning (ML) is a type of artificial intelligence (AI) that refers to technologies that enable computers to learn and adapt through experience. It emulates human learning, based on experience and patterns, rather than by cause and effect. Machine learning allows machines to teach themselves how to build models for pattern recognition rather than relying on humans to build them.

ML and AI, with their sophisticated data analysis techniques, can help SMBs and large corporations maintain a positive customer experience without undermining security. In fact, over the last five years, there has been a rise in AI and ML technologies being used by businesses. Most of which can be attributed to advancements in computing power and the evolution of paradigms like big data and cloud computing.

Organizations are already beginning to use AI to strengthen their cybersecurity to protect themselves from sophisticated hackers. A real-world example of how ML works is this: Instead of adding many data entry fields on a form or more authentication methods at checkout, ML could pre-assess a customer’s risk level to the business based on previous behavior. By offering early warning signs to customers or making questionable email addresses and URLs more prominent, these tactics can help address fraud like the Business Email Compromise.

When you combine smart security personnel with adaptive technology, like AI and ML, that continues to change and become smarter over time, this provides a competitive edge to defenders that have primarily been absent from most cybersecurity technologies to date.

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