Introduction
Attacks against insurance companies demonstrate how the criminals choose the financial services company to get large amounts of money. CNA Financial, an insurance organization, became a victim of a malware crime in 2021 (Mehrotra & Turton, 2021). The perpetrators were not identified because they had used the malware, which was frequently used by the hackers and sold to other parties. The motive of the offense was to get a ransom for allowing the company to regain access to its data.
The method of the crime was ransomware, which was a type of violation when the victims encrypted a company’s information to be able to demand finances. The perpetrators focused on clients’ data, making the employees unable to access the information (Mehrotra & Turton, 2021). The case of CNA Financial demonstrates that the organization should educate the employees, use anti-virus programs, and design strategies to prevent such issues.
Analysis
The outcome of the investigation displayed that the criminal organization had used the malware sanctioned by the United States to blackmail the company. The roots of the crime lay in the Russian ransomware, which could have been sold to some other organizations deciding to attack the insurance company. The CNA investigation department determined that the group of criminals might be called Phoenix. The company explained that it had used all the necessary measures to prevent the negative impact of this offense on the financial system in the United States. Mainly, it shared the information and used the recommendations of the FBI to guarantee that they did not violate the law.
The first reaction of the organization was its refusal to cooperate with the offenders. Mainly, they attempted to restore their access to information using their internal methods and technology. However, they did not manage to cope with the hacked system, making them contact and cooperate with the perpetrators to restore the documents they needed. The company negotiated with the criminal group, paying a ransom, which allowed them to regain control of the system.
Recommendations
This case demonstrates that the company should use protective measures to guarantee that a similar situation will not cause future problems. The first method to prevent the crime is to educate users on how to protect the information. Mohammad (2020) suggests that the explanation of the significance of secure passwords and the appropriate attention of the employees to the data might prevent the company from such issues.
Second, anti-virus programs might guarantee that the malware and suspicious codes are detected by the computers (Humayun et al., 2021). Finally, organizations should be prepared for such situations to understand the algorithm of their actions. Mainly, the specific strategy with the identification of procedures might help prevent ransomware and make the employees aware of the particular opinion of the company concerning this situation.
Summary
In conclusion, such measures as training personnel, using anti-virus software, and creating strategies might help the company to avoid ransomware in the future. The crimes against insurance companies may continue because the hackers target the finances of such organizations. The case of CNA Financial demonstrates how the criminals may use encryption and ransomware to force the companies to cooperate with them and pay ransom in the end. Consequently, the organizations should focus on educating the employees on how to protect the data. Besides, they should use anti-virus programs that help to detect suspicious codes and programs. Finally, they should create a strategic plan to define how the organization copes with this problem.
References
Humayun, M., Jhanjhi, N. Z., Alsayat, A., & Ponnusamy, V. (2021). Internet of things and ransomware: Evolution, mitigation and prevention. Egyptian Informatics Journal, 22(1), 105-117. Web.
Mehrotra, K., & Turton, W. (2021). CNA Financial paid $40 million in ransom after March cyberattack. Bloomberg. Web.
Mohammad, A. H. (2020). Ransomware evolution, growth and recommendation for detection. Modern Applied Science, 14(3), 68. Web.