One of the most precious possessions any person could have is their sensitive personal information. It is only important to keep track of its whereabouts, keep it confidential, and keep it secured.
In this article, we will discuss the following:
- Data Classification
- What is Sensitive Data?
- Examples of Sensitive Data
- Data Discovery
- Sensitive Data Discovery
- Advantages and Benefits of Sensitive Data Discovery
- Importance of Sensitive Data Discovery
- Challenges when Detecting Sensitive Data
- Ad-hoc Discovery and Continuous Discovery
Before proceeding to what Sensitive Data and Sensitive Data Discovery are, one must know what Data Classification is.
Data classification refers to the methods and techniques that get used to categorize data. The goal is to make storing, managing, and securing data easier. Risk management, compliance, and legal discovery are just some of the tasks that data classification systems offer. Moreover, the Data classification system helps organizations get more value out of their information assets by improving the usefulness and accessibility of data.
The three fundamental aspects of information security, namely integrity, confidentiality, and availability, are also improved by data classification.
- Integrity: Data classification enables proper storage availability and access restrictions to avoid data loss, unlawful modification, or destruction.
- Availability: It establishes controls to allow authorized individuals easy access to data.
- Confidentiality: It authorizes and implements more stringent sensitive data security measures.
Data classification alerts the company to the sensitivity of its data, both overall and for each new piece of data, and allows it to apply the appropriate level of security management in that context. It is simpler to map, track, and handle data when it is classified.
What is Sensitive Data?
Sensitive data is private information that must be securely encrypted and out of the hands of anyone who does not have the authorization to see it. Data security and information security measures should be in place to limit access to sensitive data to prevent data leaks and intrusions.
All data, whether original or duplicated, is considered sensitive information. Below is a list of examples of sensitive data.
- Personal Data: This includes sensitive data that reveals ethnic or racial origins, genetic data, biometric data, financial information, and health data.
- Card Holder Data: To ensure security, organizations should know how to manage large credit card systems from card schemes.
- Education Records: This includes potential employers, publicly financed schools and universities, and foreign governments to access educational information and records.
- Protected Health Information: Any data about a person’s medical status, health care service, or health care payment developed or collected by a covered entity or a third-party affiliate that you may link to the person.
- Customer Information: Financial institutions must disclose how they share and protect their customers’ personal information.
The method of locating specific subsets of data from unstructured and structured data sources is known as data discovery. It is critical to determine what data gets stored in company repositories and where it is stored.
The method of categorizing different types of data depending on its sensitivity and vulnerability is called data classification. It goes hand in hand with data discovery. Sensitive data discovery and classification are separate processes that get required for identifying and protecting business-critical data.
Sensitive Data Discovery
Sensitive data has always been in danger of being hacked, exposed, and exploited. When businesses’ sensitive personal data is compromised, the results can be disastrous. This option is why it is crucial to understand where your personal information gets kept. A sensitive data discovery and classification tool aids in the discovery of sensitive data, its ownership, and the many data regulations that are being breached by storing sensitive data in insecure areas.
Advantages and Benefits of Sensitive Data Discovery
Every firm must establish that sensitive data discovery is a critical data security activity and is a must. Not only does it ensure trust and security, but it also comes with a lot of benefits.
- They identify every occurrence of sensitive data in a company’s data store.
- Data classification is made easier.
- It monitors sensitive data that has gotten disclosed or may get exposed due to a security breach.
- You are creating the foundation for the development of a comprehensive data management system.
- It facilitates the completion of data access requests.
Importance of Sensitive Data Discovery
The foundation of a successful business is dynamism, and data discovery is a key component of that adaptability. Sensitive data discovery provides corporate executives and their teams with a behind-the-scenes look at their processes, allowing them to identify better and manage any issues that may arise.
As more firms see their data as an asset, sensitive data discovery is becoming more common. Businesses may use the data they acquire about their consumers and operations to set themselves apart from their competition. Furthermore, sensitive data discovery enables them to use this insight into a competitive advantage through product development, improved customer engagement, or increased productivity.
Challenges when Detecting Sensitive Data
Just like any other security measure, Sensitive Data Discovery also has its challenges.
Goals are not Set from the Beginning
The aim is to collect more data from impacting decision-making, but the real decisions requiring more influence do not get considered early enough. As a result, one may get results that are not worth the time spent analyzing the data.
Sensitive Data Discovery is Client-Driven and is an Iterative Process
Tools that are not well suited for business professionals are typical errors in data discovery. Traditional tools in this field may confuse the user with a plethora of unrelated graphs and charts. Data fusion and unification skills across numerous internal and external company data sources are critical components of a successful data discovery strategy.
Sensitive Data Discovery and Classification Should not be Separate
Users will not be able to improve the data security and compliance status by simply locating and categorizing the data. When organizations utilize it in conjunction with other data security procedures, they will realize significant value.
On the other hand, a network-based method allows businesses to find all known and undiscovered personal data storage and processing. It also provides a comprehensive, frequently updated perspective of the undiscovered uses and categories of private data.
Sensitive data discovery and classification are useful and vital, but they should not get done in isolation. One will see the true value when the functionality with permissions analysis, client and item behavior analytics, and change auditing is working hand in hand.
Ad-hoc Discovery and Continuous Discovery
As data rises in quantity, so does its importance in commercial decision-making. However, for businesses to fully realize the value of data at any given time, it must be freely available, accurate, and current. Decision-makers will only be able to completely trust reports and analyses if they instinctively comprehend the story that their data is telling.
Ad-hoc reporting is a business intelligence technique for swiftly generating reports on demand. Ad hoc reports are typically produced on a one-time basis to answer a specific business challenge. Ad hoc analysis goes a step further, elaborating on a report’s objective facts to derive new insights. Ad hoc analysis allows business teams to connect not just what happened but why it happened as well.
Decision-makers need answers to important questions as soon as possible in today’s fast-paced business world. However, when time is tight, employees cannot always rely on their regular, static reports to provide business answers. Reports and analyses that take several days to arrive are frequently late. Ad hoc reporting and analysis is critical because it allows organizations to swiftly obtain answers to specific questions as quickly as they are raised, speeding up the judgment call process.
A separate team of product researchers and marketers conducts the discovery, which is then passed on to delivery teams to construct what needs to get built. Other companies give employees more authority, and product teams are in charge of both discovery and delivery. Some groups begin with a discovery phase, followed by a continuous delivery phase, while others execute constant discovery and continuous delivery simultaneously.
Continuous discovery refers to discovering a plan, a product, new features to develop, changing market needs, or economic expansion required to accelerate growth. Continuous Discovery is a technique that assists product teams in improving and polishing their ideas based on the demands of their customers to enhance the product’s value. It involves doing modest research activities with clients regularly to get the desired product outcome.
Sensitive Data Discovery with Satori
Using Satori, data is continuously classified as it’s being accessed. This means that even if new sensitive information is added to your data stores, it will quickly be mapped. You can also integrate your sensitive data locations with your data catalogs, as well as set security policies to apply automatically on your sensitive data. Read here about how we do it.
In addition, with Satori, you can also set custom business-specific sensitive data to be continuously discovered.
Personal data is indeed a top priority considering the amount of traffic of users that continue to rise. It is only important to take security measures seriously to avoid breaches and compromise. To not lose the public’s trust, every establishment should have security plans for the acquired data. Confidentiality is of top priority and should not get taken lightly.
Data gathering is also essential along with time. Research is important to have fact-based decisions to develop a more effective outcome. Ad-hoc discovery and continuous discovery make it possible to obtain sufficient and accurate data when time is limited.