The risks of Data Analysis have been more evident over time, the more its consumption has increased. Invest in app security with the support of specialized companies like Codster and Veracode in Spanish.
It's easy to marvel at the possibilities that using data brings, but it doesn't guarantee uninterrupted progress. Improper handling in the collection, storage or use of big data can lead to significant risks. Understanding these risks is essential to mitigate them. Therefore, it is crucial to anticipate and be prepared.
In this article, we learn how data analysis risks are mainly classified and how Codster and Veracode can provide you with support in Spanish to solve them. The four main areas are: security issues, ethical dilemmas, malicious use by harmful actors (such as organized crime), and accidental misuse.
Security issues and risks of data analysis
The more data an organization collects, the greater the cost and complexity of storing it securely.
This is already a notable challenge. The Mid-Year Data Gap Report Risk Based Security revealed that in the first half of 2019 alone, 4.1 billion records were exposed. This underscores the importance of data security and the challenges entities face in protecting our information. The volume of data that a company handles increases the cost and burden of securing it, so the risks of data analysis increase.
This problem extends to privacy. Entities like governments, social media giants, insurers, and healthcare providers have unprecedented access to our information. Although data protection laws impose obligations on them (with significant financial penalties), the increase in large data breaches in recent years indicates the need for additional measures.
These organizations, especially those with advanced technology, have data about our homes, movements, expenses, among others. Given the increase in cyber attacks and having personal banking information and other sensitive data in your custody, it is necessary to protect yourself from the risks of data analysis with the support of Veracode in Spanish and Codster.
Ethical challenges associated with big data
Even when organizations manage to protect our information against hackers and cyber attacks, Several risks of data analysis persist, as there are agents who can use it inappropriately. Despite existing data protection regulations, ambiguities remain regarding the legitimate use of information.
Consider, for example, insurers and credit card issuers. It is well known that these entities adjust their rates and credit limits based on consumer behavior. Thus, if you have been involved in a vehicle accident, it is likely that your insurance premium will increase. The large amount of data empowers these companies to make more precise projections about future behavior, allowing them to develop increasingly detailed financial profiles.
Misuse of data by actors with bad intentions such as organized crime
Another risk of data analysis is unauthorized access to sensitive information by third parties. It is estimated that in 2020 we generate 2.5 quintillion bytes of data daily, an overwhelming amount and difficult to process or analyze even for large organizations. However, this vast amount of information is an attractive target for hackers and cybercriminals who seek to market it on the web.
Phishing, bank fraud and insurance scams are just a few examples of data analysis risks that can be maliciously exploited by organized crime networks. The scams of yesteryear, which promised large sums of money in exchange for banking information, have given way to much more sophisticated methods.
In addition, it plays a crucial role in app security, since malicious entities use big data to target advertisements or spread fake news with the aim of influencing our opinions, beliefs and even our electoral decisions. The success of these campaigns is due to their ability to directly target the population's fears, using the data collected. As the risk of data breaches continues to rise, this problem remains unsolved.
Unintentional errors in the use of big data
Not all data analysis risks are the result of malicious intent. The introduction of machine learning represents a significant advance in the analysis and processing of information, but also new areas that hackers can exploit.
Although these algorithms have the ability to learn autonomously, they must initially be programmed with instructions on how to learn, which can introduce human biases into the process. These biases, coupled with poor data management practices or simply poor data quality, can result in erroneous interpretations. These misinterpretations, when used to make critical decisions in financial or security areas, can have negative consequences.
Since data science is a relatively new field, we are still exploring how these issues will evolve.. The use of artificial intelligence is on the rise, but this introduces new risks of data analysis with this emerging technology.
Although it is unlikely that we will face risks from data analysis that we cannot bear in the near future, there are significant problems linked to artificial intelligence. AI can achieve impressive feats, but it has its limitations, such as a poor understanding of subtleties and lack of human intuition, which can lead to disastrous results, as demonstrated by the Uber self-driving car incident that resulted in a fatality in 2018. The accident occurred because the AI could not foresee that pedestrians might jaywalk.
To mitigate these new risks of data analytics, it is crucial to address systemic issues before technology adoption expands further. The most notorious cases of big data abuse include the 2016 US Presidential Election and the UK Brexit referendum that same year.
The unexpected results of both popular queries were linked to Cambridge Analytica, a data analytics company that used information illegally obtained from Facebook to influence both campaigns. The impact of these actions has redefined the international political landscape to date, so it is important to address the risks of data analysis.
To improve cybersecurity in your company, it is necessary to rely on the best providers of application vulnerability analysis, which can be prevented with the support of Veracode. Its analysis includes static source code analysis, dynamic application analysis, software composition analysis, mobile software analytics, integration and automation tools, and reporting and dashboards, you can request a consultancy with Codster to solve your doubts about it.
