Understanding the Benefits of Data Classification and Labelling

You Cannot Un-Ring a Bell

You cannot get data back once it has been stolen or made public. The damage is already done.

The process of data classification involves categorizing information based on the sensitivity, significance, and confidentially. This allows companies to prioritize and identify their data assets. They can then implement appropriate security measures.

The Wrong Side of History

Data classification dates to the ancient civilisations who recognized that sensitive data needed to be protected. Handwritten and physical documents used to be marked with labels, stamps, or coloured folders in the past. This was done to show their importance or level of sensitive information. Manual sorting was the main method of management. To ensure confidentiality, folders, locked safes, and drawers were used. Yes, the bulky filing cabinets had some important used!

The early data labels laid the groundwork for today’s data classification techniques.

Data storage moved from cabinets to servers. Paper documents were replaced by digital files. The transition to digital files prompted the creation of labels and classifications for data.

Military and government agencies were instrumental in the development of classification systems that protect national security. The classification systems classified information according to its level of importance, including top secret, secret, and confidential.

Cyber threats and data breaches became more common with the introduction of the Internet and rapid data growth. Likewise, regulators like HIPAA and GDPR emerged. The compliance requirements forced organizations to adopt stricter classification and labelling practices.

Data classification and labelling has become much more complex. Metadata is now incorporated into data, providing additional context. The data is classified not just by its content, but also based on who can access it, how and where it is stored.

These are some common phrases that relate to the classification of data:

Top Secret: It is the highest classification. If disclosed, the information would result in “exceptionally serious damage”. Store it behind closed doors.

Secret: Very sensitive and confidential information which, if released to the public, could cause serious harm to national security. I am keeping my lips sealed.

Confidential: Important information that could be damaging if it were leaked. We will keep this between us.

Sensitive, but not classified: Information that is sensitive but does not require higher levels of classification. Use a large firewall and speak softly.

Unclassified: Information which has been released to the public. Spread the word.

Open Data: Any information that is freely accessible without restriction. Information is free.

Need-to-Know: Only grant access to classified information for approved purposes. They will not be hurt by what they do not know.

Compartmentalization: Restricting information access only to people with the proper clearance. Loose lips sink ships.

Security Clearance: To access sensitive information, you must undergo vetting for clearance. To play, you must pay.

There Is More To Data Than Meets The Eye

Many organizations use the terms “Public”, “Private” and “Confidential” but do you know what they mean?

Public: This type of information is accessible by everyone both within and outside the organization. This includes data that would not be harmful if it was disclosed to or accessed uninvitedly. Marketing brochures, news releases and non-sensitive material are examples. This is what we might refer to as ‘common information.

It is relatively easy to identify public data. It is usually information without any proprietary or confidential knowledge, no trade secrets and/or personal data. Do not forget that public data is available for free, but it should not be used recklessly.

Private: Information that is intended only for use internally falls under this category. This category includes data which, although not as sensitive or confidential as Sensitive Data, should still not be shared widely. Employee records, departmental or internal memos, and reports may be private data.

It takes a little more discretion to identify private data. This data is often labelled ‘For internal use only’ or Confidential. It is important to note that the information is not intended for general consumption and should only be accessible to people who need it.

Confidential: Information that falls into this category could be damaging to an organization if it were revealed. This includes financial information, trade secrets, intellectual property, and customer data. It is information which provides a competitive edge or that is bound by law to be confidential.

It is important to identify Confidential Data, which usually has labels such as ‘Confidential” or “Top Secret”. This data must be restricted and only accessible to those with a need to know.

Sensitive: The most sensitive data is in this category. It includes personal information such as medical or financial data, and any other data which falls under a regulatory standard, like GDPR or HIPAA or even PCI DSS (Payment Card Industry Data Security Standard) which is a set of security standards designed to protect credit card data.

Breach of these data could have significant financial and legal consequences.

To determine the classification level that is appropriate for an item of data, it is important to consider the following:

Data Sensitivity: Assess the impact that an unauthorized disclosure or alteration of data could have on your organization. Personal identifiable information (PII), intellectual property or financial data can be included.

Compliance: Does this data fall under any applicable laws or regulations?

Data Value: Consider the importance of data to the business, whether it is for competitiveness or strategy. It is important to prioritize the efforts for protecting information that has high value.

Value: What is the importance of this information to the organization?

Data Criticality: Consider the importance of data in relation to mission-critical operations. It can be used to determine the necessary level of redundancy and protection for the data.

Impact: If this information were lost, stolen or mishandled, what would be the result?

Information classification deserves more attention, even though it is often an afterthought. When done correctly, data classification lays the foundation for compliance and security.

The Devil Is in The Details

Understand the Data: Take time to identify all the different types of information within the organization.

Classification Labels: It is easier to streamline access control by assigning labels based on the sensitivity of data. Label categories should be simple, clearly defined, and uniform.

Access Controls: This control determines who has access to what. Access permissions can be tailored based on classification labels. Only individuals with a need for certain data should have access.

Document the Process: Documentation that outlines classification and access controls should be concise.

Regular Audits: Information classification cannot be a one-time process. Regularly audit data to ensure that it is labelled correctly and the access controls are followed. Maintaining data security requires a continuous effort.

User Training: When it comes to protecting data, employees are your first line of defence. Make sure that all employees understand the significance of classification data and their role in protecting it.

Encryption: Use encryption as an additional layer of security for classified data. It is especially important for data in transit and in the cloud.

Incident Response Plan: Even with all the precautions taken, accidents can still happen. Prepare a plan for handling incidents. It will ensure that data breaches or other security issues are dealt with quickly and effectively.

Data Retention Policies: All data are not created equally. Data retention policies should specify the length of time that different data types should be retained and how they should be disposed.

Stay Updated: Information security is an ever-changing field. Stay informed on the latest standards in your industry and about best practices. It is important to stay up-to-date with the latest threats and technology.

Right On The Money

Not all assets or data is created equal, and organizations must prioritize their efforts to protect their most valuable assets.

While classifying & labelling data involves assigning categories or tags to data points to organize and describe the data, Identifying the most valuable assets is about determining which assets (could be data, information, products, customers etc.) are most important or impactful to the business or organization. Where the first enables the second in many cases. This involves assessing and prioritizing based on some criteria like revenue potential, strategic importance, level of differentiation etc.

Look at some of the most common types of data that require high levels protection.

Personal Identifiable Information (PII): Includes any data that could be used to identify a person, including names, addresses and financial information. Identity theft, fraud and other types of harm can result from unauthorised access to PII.

Intellectual Property (IP): IP includes any mental creations such as designs, inventions, or trade secrets. It is important to protect IP to maintain a competitive edge and ensure the success of an organisation.

Financial Data: Information about an organization’s finances, including bank account numbers and credit card details, as well as payroll information is included. Financial data that is not authorized can cause financial losses, damage to reputation or even legal implications.

Healthcare Information: Protected Health Information must comply with privacy laws and protect sensitive data about individuals.

In either case, CIA triad, must be considered to determine the need for higher-level of protection.

Smooth Seas Do Not Make Skilful Sailors.

As discussed previously, humans are the weakness link in protecting data. Human factors are often a barrier to the implementation of a program for information classification and security. The lack of tools, motivation and training can make it difficult for employees to apply labels accurately and consistently.

Instead of criticizing employees, address the root cause. Training and visual reminders can help reduce forgetfulness. They also provide clear instructions on the classification of different information types.

Encourage the correct classification with patience and empathy. The employees want to be good for the business, but they need help developing the correct habits.

Each mistake is an opportunity for incremental improvement. Create a learning culture, and not one that focuses on finger-pointing. Over time, information management will become second-nature.

Add Insult to Injury

Imagine your frustration when you learn that the personal data which was entrusted by an organization to a malicious actor has been leaked. First and most obvious is anger. This will be followed by feelings of disappointment and outrage.

Another common reaction to a breach of data is stress. It can be difficult to sleep at night when you do not know what your attackers will do with the data. You may be worried about financial loss, fraud on credit cards, or unauthorised access to your sensitive accounts. This is more than a nuisance; it is a real threat to your mental health.

We previously wrote about business leader facing anxieties and consequences of a data breach, but this also extends to the customers whose data is supposed to be protected.

Identity theft is one of the worst consequences that can come from a breach of data. Cybercriminals can impersonate anyone with your personal data, unleashing many troublesome problems, that has long-lasting consequences. From filing false tax returns to applying for loans under your name, identity thieves can cause you problems for many years.

A data breach can have a lasting emotional impact that goes beyond stress and anger. People may feel vulnerable and helpless. They may not have been able to protect their personal data, but it is out there. The loss of control may lead to feelings of anxiety.

After a breach of data, the trust in an organization is broken. Affected individuals question the competency of the company responsible for protecting their data. The erosion of trust may have long-term implications as people might choose to move their businesses elsewhere or require stronger security.

Data breaches are more than just cybersecurity incidents. They have an impact on people, leading to anger, anxiety, fear, identity theft and loss of confidence.

Go Labelling Window shopping

All data labelling methods are not created equal, that is why weighing up the pros and cons of various labelling systems to determine which one best meets an organization’s needs is important.

The process of manually labelling is assigning data tags by humans. Humans can understand and adapt complex concepts. This allows for a higher level of accuracy. It can also be expensive and time consuming, particularly for large datasets. The subjectivity of the data owners can also introduce inconsistent labelling.

Automated labelling, which is also called rule-based labelling or algorithmic Labelling, uses pre-defined rules or machine learning to tag data. It is more efficient and cost-effective to use this method than the manual labelling, which makes it a good choice for large datasets. It may be less accurate and flexible because it is based on algorithms or rules that are predefined.

Semi-supervised labelling combines the advantages of both manual and automatic labelling. The model is trained using a set of data that has been manually labelled. This model will then automatically label all remaining data, and data owners can only review or correct the labels if necessary. The method is a great balance of accuracy and efficiency. It can be used for many applications.

Crowdsourcing labelling is the task of outsourcing the labelling by distributing to many people, usually through online platforms which allows for scalability. It may not be accurate, secure or reliable as it depends on the motivation and skills of crowd workers.

Overall, consider the following factors when data labelling systems are being evaluated:

Accuracy: What is the real meaning behind the labels?

Efficiency: What is the most cost-effective and efficient way to complete labelling?

Flexibility: Is the method of labelling able to handle evolving or complex requirements?

Scalability: Does the method of labelling work well with large data sets or tasks that require high volumes?

Reliability: What consistency can you expect from the labels created by this method of labelling?

Consistency is the Key to Mastery but Keep it Simple, Stupid.

It is not easy to ensure 100% correct classification & labelling, but it is possible with the right strategy.

Taxonomy: “Rules help us be accountable and responsible“.

The foundation for perfect labelling is a clear, well-defined, and consistent taxonomy. A taxonomy, in essence, is a hierarchy that organizes data in a logical way. Start by understanding the data of the organization. Find the categories and subcategories which accurately represent your data assets.

Standardize Labelling Conventions: Establishing standardized conventions will help to avoid labelling inconsistencies. Conventions are rules and guidelines that label data. A style guide should outline how to label data, the symbols and abbreviations that are preferred, as well as the preferred conventions for naming. This guide should be accessible to everyone involved with data classification.

Automate the Process Where Possible: Human error can lead to errors in manual classification. Automating the process reduces this risk. Use software that automatically labels items based on preset criteria. To maintain accuracy, make sure that automation rules are clearly defined and updated regularly.

Continuous Training and Awareness: Train employees regularly on how important it is to follow the style guide and its guidelines. Employees can benefit from awareness campaigns that encourage them to be more vigilant.

Audit and Quality Assurance: To maintain consistency in labelling, it is essential that regular audits and checks on quality are conducted. These checks are to ensure the rules of classification are followed. You can assign individuals or groups to review the data labels periodically to find and correct inconsistencies. The process is a way to ensure that any mistakes have not slipped by.

Feedback Mechanisms: Employees can report misclassifications and correct them using feedback. This creates an improvement cycle. Promote open communication, and make sure that any issues reported are promptly addressed. This will lead to fewer inconsistencies in labelling over time.

Stay Informed About Evolving Data: The laws and regulations governing data protection are constantly evolving. Keep up to date with the most recent developments within the region where data resides and the requirements for the industry. 

Periodically review and update the taxonomy and labelling conventions to adapt to the changing data types and be prepared to adjust as necessary as non-compliance with the regulations can lead to substantial fines.

Collaboration and Communication: Encourage collaboration among teams who handle data. Communication ensures everyone is on the same page. Discussions between different departments can identify problems and help find solutions.

Role-Based Access Control (RBAC): Use advanced access control features, such as biometric authentication and multi-factor authentication, together with authorization based on users’ roles, which can ensure that they only have access to the information they need to perform their job responsibilities.

Data Loss Prevention (DLP): Can detect sensitive data and automatically block it from being transferred through different channels such as emails, web apps, or removable storage devices. DLP provides an extra layer of security by enforcing rules and policies.

Incident Response Plan: A well-defined plan for incident response, including steps to contain and mitigate the incident, notify affected parties, and adhere to relevant laws regarding data breach notifications.

How Can ITM Help You?

IT Minister covers all aspects of Cyber Security including but not limited to Home cyber Security Managed Solutions to automated, Manage Threat Intelligence, Digital Forensic Investigations, Penetration Testing, Mobile Device Management, Cloud Security Best Practice & Secure Architecture by Design and Cyber Security Training. Our objective is to support organisations and consumers at every step of their cyber maturity journey. Contact Us for more information.