A Well-Equipped Security Team Could Save You Millions of Dollars a Year

Christian Wiens Director of Marketing

Christian Wiens is Director of Marketing at MixMode. He has 10+ years of experience as a cybersecurity professional. He has his BA from The University of California, Berkeley and resides in Austin, TX.

Data breaches are expensive. By now, most organizations are well aware of this fact. When it comes to resource planning, however, SecOps teams need concrete data to ensure adequate funding is available to handle a breach.

Taking a look at recent breaches and industry analysis can help.

The Financial Cost of a Data Breach Is Rising

IBM conducts an annual “Cost of a Data Breach” study as the basis for a global analysis of the cost impact of data breaches. According to the study, the average cost of a data breach in the U.S. is growing:

· 2017: $7.35 million

· 2018: $7.91 million

· 2019: $8.19 million

Between 2017 and 2019, the average financial impact of a data breach at a U.S. based company rose 10 percent. Companies that experience “mega breaches” involving millions of records can expect to pay anywhere from $40 million to $350 million to clean up the mess.

IBM expects these figures to continue climbing in the coming year.

What factors impact the cost of a data breach?

A data breach is not limited to a single incident to be mitigated in just a few days. IBM estimates that it takes companies an average of 280 days to fully recover from a breach. Responding to these breaches extends beyond addressing the root cause of the hack.

Companies must satisfy notification requirements, preserve affected documents and logs, and address potential PR concerns. If the breach involved PHI (protected health information) or identifying information like Social Security Numbers, the response becomes even more complicated. Most companies will need to hire outside legal consultants to ensure a proper response has taken place.

Beyond these immediate issues, companies that experience a data breach will face “long-tail” costs, those occurring beyond a year year after a breach. These costs include class action lawsuits, regulatory fines, and the potential loss of customers who have lost trust in the company. IBM estimates that lost business accounts for 36 percent of the average total data breach cost.

Proactive Companies Fare Better

Not only will the cost of a data breach increase, so will the odds that a given company will experience a breach.

Companies are more than 30 percent more likely to experience a breach in the coming years, according to IBM. The Herjavec Group estimates that a ransomware attack will affect a new business every 11 seconds by 2021.

The risk of a data breach is not a vague threat intended to scare companies into investing more in backend security response. The risk is simply the reality companies must overcome to protect their clients’ data and their own future success. Bad actors are here to stay, unfortunately, and they are becoming savvier all the time.

Still, companies can make proactive decisions to reduce the risk of a data breach. Key actions that can help include:

· Establishing in-house incident response capabilities

· Integrating advanced machine-learning AI into security platforms

· Increased cybersecurity education for all employees

· Creating DevSecOps teams who address data security from the start of the development process

IBM estimates that the presence of an in-house incident response team has a significant impact on reducing data breach costs. Using incident response teams can reduce the cost of a data breach by an average of 10.5 percent, a figure that can save companies hundreds of thousands of dollars.

Next Steps

Don’t wait until you’re in response mode to come up with a data security strategy. MixMode’s third-wave, machine-learning AI detects vulnerabilities before they attract bad actors, giving our clients the upper hand when it comes to cybersecurity.

Why is machine learning better?

Machine learning is a subset of AI that adds automation and intelligence to computer programs. A music platform that can predict which songs and artists a listener will likely enjoy is one example of machine learning at work.

MixMode takes the concept of machine-learning a few steps further. Not only could our context-aware AI make accurate song predictions, but it could also actually create original music compositions in the same vein.

While today’s hackers and cybercriminals are often well-versed in typical machine-learning AI, MixMode’s unique context-aware AI is a world apart.

Our platform takes a deep dive into your network to develop a baseline level of knowledge it will use to evaluate network anomalies. The result is at least a 12 percent reduction in the cost of detecting and responding to data breaches. That’s what happens when SecOps teams don’t have to wade through a mountain of false positives to address real issues.

Learn how MixMode can ensure your organization won’t become the next company to make the news thanks to a data breach. Reach out to MixMode today to set up a demo.