Data classification is crucial to information management, enabling efficient retrieval, sorting, and storage. But what exactly is data classification, and how can it be achieved? This article will delve into the intricacies of data classification, including its various types, sensitivity levels, and more.
We will also provide 5 actionable steps to follow in the data classification process, helping you better manage and protect your valuable information. Join us on this informative journey as we demystify data classification and its importance in today's data-driven world.
Data classification is arranging data into different categories to facilitate easier retrieval, sorting, and storage. A well-structured system can help identify crucial data when required, which is crucial for risk management, compliance, and data security.
Tagging data during the classification process allows for efficient searching and tracking, avoiding duplicate data, decreasing storage and backup expenses and speeding up the search process. Although it may seem technical, every organisation's leadership should comprehend data classification's importance.
Data classification often involves various tags and labels defining the data type, confidentiality, and data integrity. Data availability can also be considered when classifying data. The level of sensitivity of data is often classified based on different levels of importance or confidentiality, which are then associated with the security measures taken to protect each classification level.
Three main types of data classification are seen as industry standards:
Content classification
Content classification inspects and interprets files to search for sensitive information.
Context-based classification
Context-based classification considers application, location, or creator, among other variables, indirect indicators of sensitive information.
User-based classification
The user-based classification relies on the end user's manual selection of each document. It depends on the user's knowledge and discretion during the creation, editing, revision, or distribution to mark sensitive documents.
Content, context, and user-based approaches can be good and bad depending on business needs and the data type.
Apart from different types of classification, organisations must assess the risk associated with various types of data, how they are managed, and where they are stored or transmitted. Data and systems are classified into three risk levels—low, medium, and high—to determine the level of security measures required.
If data is publicly available and has a low risk of being lost permanently, then it and the associated systems are likely to pose a lower risk than others. An example of such data would be content on a public website.
These data are not intended for public access or use but only for internal use by the organisation or its partners. They are typically not critical to the business operations and do not pose a high level of sensitivity or risk. Data that may fall into the medium risk category include business procedures, cost of goods, and some business documentation. Examples of such data also include emails and documents that do not contain any confidential information.
The high-risk category encompasses any remotely sensitive data crucial to operational security. It also includes data that would be extremely difficult to recover if lost. This category is reserved for all confidential, sensitive, and necessary data, such as financial and intellectual property.
Over time, data classification has greatly improved and is widely used for multiple purposes, especially to support data security initiatives. Organisations classify data for various reasons, including allowing for easy access, complying with regulations, and meeting other business or personal objectives.
Sometimes, data classification is legally required to ensure that data can be searched and retrieved within specific timeframes. Additionally, data classification is an effective tool for implementing appropriate security measures based on the type of data being accessed, transferred, or copied. This ensures that data is handled and protected in accordance with its sensitivity level, reducing the risk of data breaches and loss.
Data classification is vital in managing data throughout its lifecycle by assigning it to a specific category or group. This process ensures that an organisation follows its guidelines and follows compliance regulations. Companies in heavily regulated industries frequently use data classification workflows to aid with compliance checks and data discovery.
While data classification categorises structured data, it is particularly crucial for unstructured data. Data categorisation can identify duplicate copies and eliminate unnecessary data, leading to efficient storage use and maximisation of data security measures.
Following these five simple steps can be straightforward to start with data classification.
First, you must identify all the data within your organisation, including where it is stored, how many copies exist, and who has access to it.
Next, you need to categorise the data according to its type and determine which category each type belongs to.
Once you have categorised the data, you need to assess each type of data's sensitivity and risk level and assign it to one of three categories: high, medium, or low.
At this stage, you should determine the amount of storage required for each data type and whether additional security measures must be taken to protect sensitive data.
Data classification is an ongoing process, so it's important to monitor changes and new data regularly and adjust classification as needed to ensure that data is always properly organized and secure.
We excel in data handling and offer expert guidance and support to those in need. Whether you're uncertain about your next steps or require help with data classification, our team is ready to assist you. Reach out to us to arrange a consultation, and together, we can devise the optimal approach tailored to meet your specific requirements.
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Data classification is the process of categorising data based on its sensitivity and criticality to an organisation, and applying appropriate security controls to protect it.
Data classification is important to ensure that sensitive data is protected appropriately, and that security controls are applied according to the level of risk associated with the data.
The different levels of data classification typically include public, internal, confidential, and restricted. The specific levels may vary depending on the organisation and the type of data being classified.
Data classification is typically performed by assigning a classification level to each type of data based on its sensitivity and criticality. This is usually done by a data owner or a data steward who is responsible for the data.
Examples of data that may require classification include personally identifiable information (PII), financial information, trade secrets, intellectual property, and confidential business information.
Best practices for data classification include involving all stakeholders in the process, ensuring that policies and procedures are clearly defined and communicated, providing regular training to employees, and regularly reviewing and updating the data classification policy to ensure it remains effective.
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