04.23.2024
Why You Should Take Power BI Classes
Posted by Marbenz Antonio on March 30, 2022
When you consider data loss, stock price damage, and enforced fines from violations of the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other rules, data breaches are incredibly costly for businesses. They also have the potential to erode the faith of individuals who are victims of identity theft, credit card fraud, or other harmful behaviors as a result of the breaches. In 2021, the number of data breaches increased by 68 percent to 1,862 (the most in 17 years), with an average cost of USD4.24 million per breach. Healthcare data breaches alone affected 45 million individuals, more than double the number affected only three years before.
Sensitive data is secret information gathered from consumers, prospects, partners, and workers by businesses. Credit card numbers, personally identifiable information (PII) such as a home address and date of birth, Social Security Numbers (SSNs), corporate intellectual property (IP) such as product schematics, protected health information (PHI), and medical record information that could be used to identify an individual are all examples of sensitive data.
A data breach might influence every level of a business, from IT operations to red and blue teams to the board of directors. How do companies identify sensitive data at scale and prevent it from being accidentally exposed? Let’s take a look at four of the most serious threats to sensitive data and how to safeguard it.
Organizations might be surprised by the data discovery process, which can be unpleasant at times. Within your business, sensitive data might be found in unexpected locations. For example, without your knowledge, an employee may have saved a customer’s SSN on an unprotected Microsoft 365 site or third-party cloud. Data exposure events, which occur when sensitive data is left unprotected online, put around 164 million individuals at risk out of an expected 294 million persons attacked in 2021.
A robust data discovery procedure is the only way to verify that your sensitive data is appropriately preserved. Data scans will reveal those unexpected storage sites. Handling it manually, on the other hand, is nearly impossible.
This leads to the classification of data. Once you’ve found the data, you’ll need to give it value to use as a starting point for governance. The data classification process entails identifying the sensitivity and business effect of data so that you can analyze risks with confidence. This will make it simpler to manage sensitive data in ways that prevent it from being stolen or lost.
The following are the categories used by Microsoft:
Identifying data at scale is a major challenge, as is enforcing a process so employees manually mark documents as sensitive. Leveraging security products that enable auto-labeling of sensitive data across an enterprise is one method, among several that help overcome these data challenges.
You must protect material that has been classified as confidential or highly sensitive from malicious actors once it has been classified as such. The Chief Information Security Officer (CISO) and Chief Data Officer are ultimately responsible for preventing inadvertent data disclosure. They are in charge of safeguarding information and exchanging data through policies and routines that allow for security while not impeding workplace efficiency.
Data leakage protection is becoming increasingly important in the industry. The Allianz Risk Barometer is an annual analysis that analyzes the most significant risks facing businesses in the coming year. Allianz gathered information from 2,650 risk management specialists from 89 countries and territories for the 2022 research. For only the second time in the survey’s history, cyber events came out on top. Business disruptions scored 42 percent, natural disasters 25 percent, and pandemic breakouts 22 percent, while cyber events placed higher at 44 percent.
Data governance guarantees that your information is discoverable, accurate, reliable, and secure. To successfully manage the data lifecycle, you must store data for the appropriate time. You don’t want to keep data for any longer than is required since it increases the quantity of data that might be exposed in a data breach. You also don’t want to erase data too rapidly, since this might put your company in jeopardy of regulatory penalties. Personal data is sometimes collected by businesses to deliver better services or other economic value. Customers who wish to learn more about your services, for example, may provide you with personal information. When data no longer serves a function, it must be removed by the data minimization principle.
The consequences of failing to address these issues can be severe. Violations of rules or standards can have serious financial and legal consequences for businesses. In 2021, for example, a few well-known businesses were fined hundreds of millions of euros. One of the fines was for failing to comply with the GDPR’s personal data processing obligations. Another reason was that customers were not given enough information about data processing procedures in a privacy policy. Since January 28, 2021, the data protection authorities have fined a total of $1.25 billion for GDPR violations.
How do you safeguard sensitive data in light of the potentially severe consequences? As previously said, data discovery entails discovering all of the locations where your sensitive data is kept. Support for sensitive data types, which may identify data using built-in or custom regular expressions or functions, makes this considerably easier. We propose searching for a multi-cloud, multi-platform solution that allows you to use automation because critical data is everywhere.
We recommend establishing a plan through technology rather than depending on humans when it comes to data classification. People, after all, are busy, and they are prone to overlooking details or making mistakes. Additionally, because businesses might have hundreds of sensitive documents, manual data categorization and identification is impractical because the procedure is too slow and imprecise. Look for data categorization software that allows for auto-labeling, auto-classification, and classification enforcement throughout an organization. Using data examples, trainable classifiers detect sensitive data.
Some solution suppliers separate productivity and compliance and try to include data protection as an afterthought. To secure sensitive data, we offer a method that incorporates data protection into your existing procedures. When thinking about plan security, think about who has access to the data. Where should data be stored, and where should it not be stored? What can be done with the information?
Microsoft products provide auditing capabilities, allowing data to be observed and monitored without being stopped. It can also be overridden so that it does not obstruct commerce. Also, think about if you want to protect data or have standing access (identity governance). Data leakage prevention software helps safeguard sensitive records, which is critical since laws and regulations hold businesses accountable.
Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.
For more information, please get in touch with one of our course advisers today or contact us at training@coursemonster.com
