The implementation of Artificial Intelligence, into cybersecurity, may become one of the popular AI trends in 2021 to have a proper defense against cyberattacks from malicious hackers and ransomware gangs. But, these ransomware gangs are improving their techniques to reach a whole new level of cyberattacks from behind the curtains. The ransomware gangs are utilizing virtual machines to hide these vicious cyberattacks. It is becoming impossible for victims to detect and trace the gang or hackers within a short period of time. It is one innovative trick to allow cyberattacks to run their payload inside these virtual machines after bypassing the advanced cybersecurity software.

The process of using virtual machines to achieve cyberattacks is being used by several ransomware gangs across the world. The tangible benefits of these virtual machines are becoming popular to go for blackmailing or phishing despite the strong cybersecurity of reputed companies.  The ransomware gangs having a small foothold on an infected host can download or install the virtual machine software easily. It will share the host computer’s storage space with a virtual machine for proceeding to encrypt confidential files from the virtual machine. The host’s antivirus software cannot reach these virtual machines to detect the execution of current ransomware. After finishing the encryption process, the virtual machine is discarded seamlessly. This is another popular benefit for ransomware gangs because the virtual machines discard an enormous volume of vital forensic evidence to prevent any further investigation.

An open-source virtual machine software, known as VirtualBox is popular among ransomware gangs in recent times. The investigators are failing to recognize the ransomware that is discovered running in a virtual machine. There are incidents where the investigators found out that a gang tried to run Conti and MountLocker ransomware on a host computer running Windows 7.  Some ransomware gangs used RagnarLocker in Windows XP. Multiple cyber-criminals use a pattern of naming multiple files such as aa51978f.msi or s3c.msi that usually end with .msi. They also create a file name like runner.exe and utilized the go-ps library for process enumeration.

This new technique is showing that cyber-criminals or ransomware gangs want to stay one step ahead of getting detected by high-end cybersecurity. They are using dual-use tools to stage cyberattacks on multiple targeted networks. These kinds of cyberattacks are an imminent threat to all types of businesses. Thus, it is recommended to follow the precautionary measures to avoid consequences— ensure not to view intrusion detection as an option, use security tools for monitoring all virtual environments and integrate hypervisor monitoring into the whole system.

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Organizations should consider democratizing data science to enhance its benefits to a broader audience.

Data science is the backbone of many organizations in the current scenario. Businesses are continuously competing to become data-driven to enhance agility and growth especially in today’s time of crisis. The pandemic accelerated digital transformation across the industries and data plays a huge role in achieving this transformation. According to BARC research, organizations leveraging Big data reported an average 8% increase in revenues and a 10% decrease in costs.

Data science for business is not a new strategy and thus is not unfamiliar. Artificial intelligence and data science go hand in hand to automate operations and deliver the best business insights. Data is a critical business asset and hence data science has a great impact on revenue, operation costs, risk management, customer experience, supply chain and logistics management, predictive analytics, and fraud detection.

To achieve business productivity and growth, many organizations are investing in data science by hiring data scientists into their systems. Data scientists are professionally qualified in studying data and deriving insights from them. However, due to the importance of data science, companies tend to centralize on data scientists rather, which decreases the awareness among the others.

Data science needs to be decentralized and democratizing data science in organizations should be an important business agenda.

Democratizing Data Science- As a Strategy

Extending the accessibility of data science across an organization needs a definite plan. For example, understanding and implementing data science for beginners might be difficult considering the lack of communication and awareness on the subject.

• Once they have data scientists, companies often forget the need to communicate the broad vision of data science to others. This scenario should stop. Enhancing communication and educating employees on the power of data and data science is a crucial step in democratizing data science in organizations. Employers should work towards upskilling their employees on data analytics and gaining business insights from it. This kind of collaboration within a work environment will increase the knowledge about data science and enable people other than data scientists to leverage data science. Involvement is the key.

• Unburdening your data scientists can be another way to standardize data science in your organization. Most of the organizations maintain their data science tools with the specialized data science team creating a silo by restricting other employees from accessing these tools. The data science silo needs to be broken so that these tools can be shared among others enabling them to act on issues that need basic training. Thus, the data scientist team can focus on tasks that need high-level expertise. Integrating AI and machine learning systems to automate repetitive tasks can also free up data scientists.

• Sharing tools are not enough since analyzing data requires specific skills. Organizations should make sure that they create a knowledge pool to share data science skills and train employees on the same. Once the employees have hands-on experience, it becomes easier to find solutions. When employees start understanding the language of data the business productivity escalates considering the huge capabilities of data analytics and science.

• Organizations should incorporate user-friendly tools like no-code or low-code automation, graphical user interfaces, drag and drop technology, etc. to enhance the accessibility of data analytics to the whole organization.

Data science in organizations should be accessible to all the employees rather than restricting it to a particular group. The strategy of democratizing data science in organizations will enhance the scalability, enable better innovations, fast-track productivity, and minimize costs.

An article by Jeff Feng, PM lead for Data at Airbnb reveals how the organization has embraced data science democratization by introducing Data University, which is ‘data education for anyone.’  He says, “Our vision is to empower every employee to make data-informed decisions. Our approach is unique since organizations offering data education typically focus just on their technical employees. Our approach is also intentional because we believe that every person at Airbnb should and can utilize data in his/her role to make better decisions. Thus, we designed the program to make it accessible and relevant to anyone at Airbnb.”

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