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Data Classification and Handling: Best Practices for Data Organisation and Protection

07/09/2023by minal.metkari0Read: 6 minutes

With technological advancements and humongous data being created, the process of data classification and handling holds immense importance in the field of data management.

Organisations deal with vast amounts of data that require careful classification to ensure proper storage, security, and accessibility.

Hence, it is crucial to understand the significance of data classification and implementing effective handling practices can greatly enhance data management, decision-making, and overall operational efficiency.

In this comprehensive blog, we will delve into the world of data classification and handling, providing valuable insights, practical tips, and industry best practices for mastering these fundamental aspects of data management. We have also shared information and strategies to help businesses effectively classify and handle data, optimising the organisation’s data management processes and fortifying data security posture.

Data Classification

Data classification is the process of categorising data based on its sensitivity, importance, or other relevant factors to assign appropriate security measures and access controls. By classifying data, organisations can prioritise resource allocation and implement tailored security protocols, ensuring that highly sensitive information is safeguarded while allowing appropriate accessibility to less sensitive data.

Data Handling

Efficient data handling practices are essential for maintaining data integrity, confidentiality, and availability. It requires adopting standardised procedures for data storage, manipulation, sharing, and disposal. By adhering to these best practices, organisations can mitigate risks of data breaches, unauthorised access, and data loss, and ensure compliance with relevant regulations such as the Privacy Act 1988 (Privacy Act).

Privacy Act 1988 is Australia’s primary law governing the processing of personal information about persons. This involves the collection, use, storage, and dissemination of personal information in both the federal and commercial sectors.

Importance of Data Classification and Handling

Data classification and handling play a crucial role in ensuring the security and integrity of sensitive information. By properly classifying and handling data, companies can protect against unauthorised access and breaches, comply with data protection and privacy regulations, and improve data organisation, retrieval, and analysis.

  • Protection against Unauthorised Access and Breaches: 

Organisations may discover and prioritise their most important and sensitive information by categorising data. This enables them to put in place adequate security measures to prevent unauthorised access and potential breaches.

Companies can use data categorisation to decide who should have access to certain data sets and install stringent controls to keep unauthorised persons out.

  • Compliance with Data Protection and Privacy Regulations: 

Organisations can comply with different data protection and privacy standards by classifying their data. Companies may identify personal or sensitive information and guarantee it is handled in accordance with regulatory standards by categorising data. This involves installing Privacy Act-compliant principles such as encryption, data anonymisation, and access limits.

  • Improved Data Organisation, Retrieval, and Analysis: 

Data categorisation allows for more effective data organisation, retrieval, and analysis. Companies may readily identify and access certain data by categorising data into multiple classifications depending on its value, relevance, or sensitivity. This saves time and effort, which is especially useful when working with big amounts of data.

Furthermore, good data categorisation helps organisations to undertake more precise and relevant data analysis, resulting in useful insights and informed decision-making.

Information Classification and Handling Policy

The Information Classification and Handling Policy establishes the framework and approach to data security, confidentiality, and privacy management.

The Consumer Data Right empowers Australians to govern their personal data. This allows for the development of new products and services for those consumers. There are five governance needs and 24 information security requirements to participate as a data recipient. These are examined independently by authorised consulting organisations such as Anitech and incorporated in an assurance report for accreditation. 

Example Classification and Handling

The categorisation specifies the categories of data and the level of security that is used. When the phrase “sensitive” is used, it refers to material that is Restricted, Private, or Confidential.

Restricted

This is the most sensitive information, and it is only meant for “need-to-know” users. Its unauthorised disclosure within or outside the organisation may have a negative impact on the company, its customers, partners, and/or suppliers.

This includes:

  • Board reports.
  • Customer data that has been expressly identified as economically sensitive.
  • Strategic business plans.

Private

Private data is any data that refers to an individual person and may fairly be used to identify that specific person. Private data has varied degrees of sensitivity. The distinction between Private data and Restricted and Confidential data is that the data subject or person to whom the data relates determines the proper protection and use of Private data. Data can be both private and restricted or confidential.

This includes:

  • Personal information such as name, employee ID, credit card information, and bank account number.
  • Personal preferences, sexual orientation, and health issues.
  • Employee performance evaluations and employment contracts.

Confidential

This classification relates to any non-public business information that should be kept secure against unauthorised access.

This may include:

  • All customer data is not specifically tagged as commercially sensitive.
  • Customer and third-party contracts
  • Internal documentation related to company practices that are not approved to be public.

Public

Public information includes that which is already publicly available or has been approved by management for release to the public. This may include:

  • Quotations and proposal information
  • User guides and customer-facing system documentation.
  • Contact and company lists and public details.

Efficient Data Handling

Data handling is a broad practice that is critically important to protecting the security, confidentiality, integrity, and availability of data used by the company and its customers. The following practices should be applied to ensure effective data handling:

  • Only collecting data where there is a legitimate need.
  • Protecting the security and confidentiality of all data by default, unless known or approved otherwise.
  • Classifying, labelling, and verballing communicating the type of information in accordance with the categories above to ensure awareness by other users.
  • Applying encryption of sensitive data at rest and in transit over networks in line with approved cryptography protocols; and
  • Always store sensitive data in approved and secure storage locations.

Data Categorisation Techniques in Australia

In Australia, data categorisation strategies involve categorising and organising data in order to improve its administration, security, and compliance.

Techniques that are commonly used include:

1) Data Classification:

Data classification involves assigning labels to data depending on sensitivity levels (e.g., “confidential,” “public,” “internal use”) to help in access control and protection.

2) Tagging and Metadata:

It involves adding descriptive information and tags to data, allowing for simple search, retrieval, and comprehension of its context.

3) Automated Classification:

Machine learning algorithms are used to automatically classify data based on patterns, content, and context.

4) Role-Based Access Control (RBAC):

Data access is granted based on user roles and responsibilities, with sensitive information restricted to authorised employees.

5) Data Loss Prevention (DLP):

Data Loss Prevention (DLP) systems can be used to monitor and prevent unauthorised data transfers or leaks, as well as to protect sensitive information.

6) Encryption:

Encrypting sensitive data renders it illegible in the absence of adequate decryption keys, even if viewed without authorisation.

7) Data Retention Policies:

Creating strategies for data retention and destruction while adhering to Australian data protection requirements.

8) Data Masking:

Data Masking involves protecting sensitive information by displaying just partial or obscured data to users who do not require full access.

9) Audit Trails:

Keeping track of data access and modification records can help in tracking unauthorised activity and accountability.

10) Data Governance Framework:

Creating a complete data governance structure to control data classification, access, and use.

The CDR Perspective

The Information Classification and Handling Policy outlines the structure and strategy of data management that supports the lifespan of information assets.

According to the CDR, the accredited data recipient must document and implement processes related to the management of CDR data throughout its lifecycle, such as an information classification and handling policy (which must address the confidentiality and sensitivity of CDR data) and processes related to CDR data backup, retention, and, in accordance with Rules 7.12 and 7.13, deletion and de-identification.

Challenges and Best Practices in Data Classification

Effective data classification is crucial for organizations to protect their sensitive information and ensure compliance with data protection regulations. However, there are several challenges that arise when it comes to accurately classifying and handling data.

1) Handling Unstructured and Big Data

Managing unstructured and large data is one of the most difficult difficulties in data categorisation. Text documents, social media postings, and emails, for example, lack a fixed format, making them challenging to categorise. Big data, on the other hand, refers to the massive amounts of data that organisations create, which can be difficult to categorise and analyse.

Organisations may solve these problems by utilising modern data categorisation tools and software that employs machine learning algorithms. These programs can analyse and categorise unstructured and large data automatically based on specified rules, keywords, or patterns.

2) Addressing Human Error and Bias

Organisations may address these issues by adopting contemporary data classification tools and software that uses machine learning algorithms. These algorithms may automatically assess and categorise unstructured and vast amounts of data based on predefined rules, keywords, or patterns.

Organisations should implement standardised data classification techniques and provide sufficient personnel training and awareness to reduce these difficulties. Implementing automated data categorisation techniques can also assist in eliminating human bias and mistakes, resulting in more consistent and trustworthy outcomes.

3) Continuous Monitoring and Reassessment of Data Classification Strategies

Data categorisation is a continuous process that needs constant monitoring and revaluation. categorisation criteria may vary as data sets and business objectives grow, necessitating a frequent evaluation and updating of data categorisation procedures.

To discover any inconsistencies or abnormalities, it is critical to build a system for continuous monitoring and auditing of data classifications. Regular audits may assist in ensuring that data is categorised appropriately and consistently, lowering the risk of data breaches or compliance issues.

Thus, by tackling these difficulties and following best practices, organisations may improve their data categorisation processes and successfully manage their data assets.

Anitech’s experienced consultants can help businesses with efficient data handling and protection. With a robust management system framework and strategies, our consultants can help organisations secure data from external threats.

Call us today for assistance at 1300 802 163 or e-mail – sales@anitechgroup.com

minal.metkari

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