Top 10 Healthcare Industry Cyberattacks

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Top 10 Healthcare Cyberattacks

Cybercrime targeting the healthcare industry is big business and affects organizations across the spectrum. In 2020, cyberattacks in healthcare doubled over those in 2019 by hackers trying to take advantage of operational chaos and increased demand for healthcare services during the COVID-19 global pandemic. Sensitive information found in medical records are a hot and valuable commodity on the dark web. As a result, healthcare firms have become a target for hackers because of the large amount of personal information in their possession.

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Top 10 Cyberattacked Industries in 2020

Share of cyberattacks by industry
Source: IBM Security XForce

According to IBM in its X-Force Threat Intelligence Index, the top 10 industries suffering the most cyberattacks in 2020 were:

1. Finance and Insurance

Most Common Type of Attack: Server access attack

Since 2016, the finance and insurance industry has been the most attacked industry. In 2020, attacks increased over 2019 by 238%. Hackers seeking to profit financially from attacks often hit this industry. Paralyzing banks is usually less of a goal with attacks on this industry, but accessing internal systems can yield hefty illicit returns. However, during the pandemic, hackers seeking to paralyze infrastructure including nation-state cyber criminals also hit banking institutions to cause chaos.

2. Manufacturing

Most Common Type of Attack: Ransomware

The attacks against manufacturers doubled in 2020 compared to 2019. 21% of all 2020 ransomware cyberattacks hit the manufacturing industry. However, this industry also saw 4X more BEC attacks than any other industry, and a significant number of data theft attacks. Hackers renewed interest in this industry, likely trying to take advantage of supply chain disruption and operational chaos caused by the global pandemic, as consumers saw (and continue to experience) shortages in manufactured goods.

3. Energy

Most Common Type of Attack: Data theft

35% of attacks in the energy sector involved data theft, while only 6% involved ransomware. This was likely indicative of hacker motivations to hit this industry including IP theft, customer data theft and extorsion. BEC and server access attacks were also notable in this industry.

4. Retail

Most Common Type of Attack: Credential theft

Attacks on the retail industry were actually lower in 2020 than in 2019. This was likely due to fewer retail transactions taking place during the 2020 (less room to hide as a hacker). Retail is typically a target because of the high volume of credit card and financial transactions.

5. Professional Services

Most Common Type of Attack: Ransomware

Professional services saw the highest percentage of attacks from ransomware attacks of any industry. Data theft and server access attacks were also common in 2020. These organizations are typically attractive to cyber criminals because they serve as a path to further victims and often hold confidential data about customers.

6. Government

Most Common Type of Attack: Ransomware

Government received the second highest number of ransomware attacks of the industries, totaling a third of the attacks that this industry faced. Yet, only 38% of state and local government employees have been trained on ransomware prevention. This industry also faced a large burden in moving operations to accommodate work from home environments, as much of this industry had all team members working on site, and was not equipped for moving a remote workforce. Data theft attacks were also notable.

7. Healthcare

Most Common Type of Attack: Ransomware

The healthcare industry suffered twice as many cyberattacks in 2020 than in 2019, likely due to hackers taking advantage of operational chaos caused by the global pandemic, shifts in workforce to cover emergency medical care while furloughing operational and administrative staff due to revenue challenges caused by elective medical services being put on hold. Hackers, including nation-states looking to disrupt and steal data from organizations in this industry, targeted those in medical research and development attempting to invent COVID-19 vaccines, as well as frontline providers.

8. Media

Most Common Type of Attack: Malicious domain name squatting

90% of malicious domain name spoofing attempts targeted the media. This sector includes telecommunications and mobile communications providers, as well as media and social media outlets that can play a critical role in political outcomes, especially during election years. The timing of 2020 being a U.S. presidential election year likely drove this type of attack in this industry.

9. Transportation

Most Common Type of Attack: Malicious insider / misconfiguration

Attacks against the transportation industry were much lower in 2020, likely because everyone was sheltering in place and travel bans existed across the globe during 2020. This industry ranked #3 in 2019 and fell to #9 in 2020.

10. Education

Most Common Type of Attack: Spam / adware

Education has historically been vulnerable to cyberattacks due to a large decentralized surface area of users hard to control with staff and students regularly logging into systems from home to complete and grade course work. However, the user and device based security measures that this distributed surface area drove for security protection before the pandemic, better equipped education for security operations during the pandemic than other industries accustomed to relying on firewall protection.


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Aunalytics has been included in the 2021 BioCrossroads Book of Data and Organizations

Aunalytics has been included in the 2021 BioCrossroads Book of Data and Organizations directory of Indiana enterprises in the health-data intersection – it includes information on Indiana’s industry, government, health systems, academia, and digital health startups that have tremendous data and technology resources which are driving transformative healthcare and life sciences work locally and globally. The directory now includes snapshots of 40 organizations who control data assets – data sets, data talent, and/or data technology — as well as five cross-organizational initiatives, who collaborating for a specific data project or program.

Aunalytics, Inc. Profile

Role(s)

  • Aunalytics is a data platform company delivering answers for your business. Aunalytics provides Insights-as-a-Service to answer enterprise and midsized companies’ most important IT and business questions. The Aunalytics® cloud-native data platform is built for universal data access, advanced analytics and AI while unifying disparate data silos into a single golden record of accurate, actionable business information. Its DaybreakTM industry intelligent data mart combined with the power of the Aunalytics data platform provides industry-specific data models with built-in queries and AI to ensure access to timely, accurate data and answers to critical business and IT questions. Through its side-by-side digital transformation model, Aunalytics provides on-demand scalable access to technology, data science, and AI experts to seamlessly transform customers businesses.
  • Our healthcare industry revenue cycle management analytics focus provides AI and machine learning driven insights for reducing insurance reimbursement underpayments and streamlining billing to provide a data driven practice management solution.

Mission

We use data and technology to improve the lives of others.

History

Aunalytics began in 2011 when successful data center entrepreneurs in South Bend, Indiana partnered with data scientists from the University of Notre Dame to provide end to end business and IT analytic solutions backed by a cloud data center. Aunalytics aims to bring data and analytics to mid-market businesses, healthcare systems, and providers by our side by side model to provide clients with access to our data engineer and data science experts to harness data driven insights to better compete with large nationals. We provide healthcare industry specific data models and mine your data for insights to reduce operational costs and drive revenue.

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  • 250 team members in Indiana, Michigan and Ohio
  • Enterprise cloud, data analytics, data management and managed services offerings powered by our data platform
  • Industry specific data analytics – built for healthcare providers for revenue cycle management
  • HIPAA compliant enterprise cloud


AC3 Achieves Significant Efficiency Gains Through Automation and Data Enrichment Using the Aunalytics Daybreak Solution

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AC3 Achieves Significant Efficiency Gains Through Automation and Data Enrichment Using the Aunalytics Daybreak Solution

With Aunalytics, AC3 automates billing processes and provides revenue cycle insights to oncology providers for greater visibility into missing revenue opportunities.

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What are Smart Features?

Machine learning is a leading component of today’s business landscape, but even many forward-looking business leaders in the mid-market have difficulty developing a strategy to leverage these cutting edge techniques for their business. At Aunalytics, our mission is to improve the lives and businesses of others through technology, and we believe that what many organizations need to succeed in today’s climate is access to machine learning technology to enhance the ability to make data driven-decisions from complex data sources.

Imagine having the ability to look at a particular customer and understand based on past data how that individual compares in terms of various factors driving that business relationship:

  • Which of our products is this customer most likely to choose next?
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  • What is churn likelihood for this customer?
  • What is the probable lifetime value of this customer relationship?

Aunalytics’ Innovation Lab data scientists have combed through data from our clients in industries like financial services, healthcare, retail, and manufacturing and have developed proprietary machine learning techniques based on a solid understanding of the data commonly collected by businesses in these sectors.

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A SHAP (Shapley Additive Explanations) value chart for a remarkably accurate loan default risk model we developed shows which features have the highest impact on risk prediction.  

From this, we append insights gleaned from machine learning to data models. We add high value fields to customer records to reveal insights about a customer learned from our algorithms. Smart Features provide answers to pressing business questions to recommend next steps to take with a particular customer to deepen relationships, provide targeted land and expand sales strategies, provide targeted marketing campaigns for better customer experiences, and yield business outcomes.

Machine learning techniques enable more accurate models of risk, propensity, and customer churn because they represent a more complex model of the various factors that go into risk modeling. Our models deliver greater accuracy than simpler, statistical models because they understand the relationship between multiple indicators.

Smart Features are one way that Aunalytics provides value to our clients by lending our extensive data science expertise to client-specific questions. Through these machine learning enriched data points, clients can easily understand a particular customer or product by comparing it to other customers with similar data. Whether you want to know if a customer is likely to select a new product, their default risk, churn likelihood, or any other number of questions, our data scientists and business analysts are experienced and committed to answering these questions based on years of experience with businesses in your industry.

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