A deeper look: How the 281 data breaches in Q1 2019 will impact companies
High-profile data breaches show no sign of slowing down. In the first quarter of 2019, there were 281 reported data breaches, exposing more than 4.53 billion records.
Companies have overlooked the fact that cybercriminals are becoming more sophisticated with automation, and these 4.5 billion exposed records will become 4.5 billion new opportunities for criminals to commit automated fraud online. Organizations are not where they need to be when it comes to protecting their online ecosystems against attacks and the reality of the situation is troubling. And as 2019 progresses, companies will continue to be impacted by these data breaches—even if they’re not directly involved—here’s how.
Data breaches are costly, even when an organization isn’t attacked
The cost of a data breach – regulatory fines, lawsuits, integrating a cybersecurity product to secure their attack surface, and heavily investing in new technologies to prevent the next occurrence – adds up quickly and can be detrimental. According to Ponemon’s 2018 Cost of a Data Breach Study, the global average cost of a data breach increased 6.4 percent year-over-year to $3.86 million. The average cost for each compromised record within a company also increased 4.8 percent to $148. The cost to an organization’s reputation and customer trust, perhaps even more significant, can’t be measured.
The implications of a data breach on a company are serious and need to be taken seriously. Organizations not directly involved in a data breach need to proactively secure their online ecosystems because cybercriminals can, and will, weaponize exposed data.
Investments in machine learning tools will increase
Cyber attacks continue to quickly evolve and organizations have failed to stay ahead of the fraud curve. Emerging technologies, such as machine learning, are vital to helping fight automated fraud. Machine learning can help companies gain valuable insights into an attacker—incoming traffic signals, user behavior, device fingerprinting, etc. Machine learning will help companies track, label and identify suspicious users at-scale across their entire attack surface.
And, that’s a good thing because the attack surface is growing. Organizations are increasing the number of portals present on a website, including account creation, user login and check out. By increasing the number of portals on a site, organizations are also increasing the opportunities for cyber attackers to gain entry. Machine learning will allow organizations to better monitor authentic and inauthentic traffic, identify what the incoming traffic looks like and act against the traffic if labeled as inauthentic to stop automated fraud before it happens.
Application security will become a priority for organizations
Organizations have long been focused on protecting their internal attack surface. The focus is quickly evolving, and organizations are prioritizing to improve the security of their applications against external attackers. In the wake of several high-profile data breaches, organizations are increasingly becoming mindful of application security and are now developing ways to protect against emerging sophisticated attack techniques.
However, organizations are continuing to leave the responsibility of application security in the hands of product and engineering teams, rather than the CISO and internal security team. This is a problem. As application security becomes top-of-mind, it’s important for organizations to hand the task over to the team most capable of preventing these ongoing attacks, as it will lead to a better protected attack surface.
Moving forward, organizations must have the mindset of when an attack occurs, not if, and learn from these attacks to shift the focus from mitigating fraud to preventing it. By taking the necessary prevention measures, a cyber attacker’s ROI will diminish and organizations will be more prepared to combat fraud.