Protecting data is an extremely challenging task. Every day, an organization creates new data, most of which would be valuable to cybercriminals. A lot of data management policies are hence implemented to prevent data leakage and to protect information. However, with an organization having millions of documents, data leakage is unavoidable mostly due to lax cybersecurity policies, cyber threats, or malicious employees.
Classifying data would boost an organization’s cybersecurity posture since it would allow one to:
Risk Management
Conducting frequent risk management is an important factor underlying a successful cybersecurity program. It entails identifying security risks, analysing them, and eliminating them. In effect, data risk management is at the heart of security policies employed to enhance information management and governance. Effective data risk management practices can only possible if an organization uses effective data classification methods to understand. This means that once data has been classified according to the sensitivity level, say confidential, restricted, general use or public, the cyber risks specific to each category can be analysed and mitigated, hence ensuring assured data protection for business and specialised for specific files.
Besides, the overall data risk management exercise can be simplified. Despite the data classification method used to separate the various data sets, they are all concerned with identifying the data that can be freely shared with the public, internal data requiring minimal security controls, and restricted+confidential data that must be protected at all costs. As such, an organization can channel all of its data risk management efforts to data classified as highly sensitive.
Improve cybersecurity tools (DLP)
Cybersecurity tools such as data loss prevention (DLP) tools are very handy in loss prevention. DLP tools are technologies capable of inspecting content and analysing the context of data communicated through emails, via a corporate network, data currently in use on an endpoint device, or data stored in the cloud or local servers. The tools execute security responses based on the rules created to mitigate accidental data leakage or the use of unauthorized channel to send sensitive data. Such DLP tools can be more effective after the data to be secured has been classified according to its respective need for security, especially because every single day, new data is created.
A data classification process categorizes organizational data according to the sensitivity levels. Labelling data sets identifies the information requiring the highest levels of security controls thus paving the way for an organization to strategically integrate available DLP tools. Different DLP technologies are used to secure specific data types or to monitor all the platforms used to store and process data, as well as the channels used to share or communicate data. Some DLP technologies may however purport to automatically classify data but instead end up using regular terms to label the information. For instance, the DLP tools may classify data to be for public use through identifying a common term whereas information with the same word should be confidential (regular expression). Enterprise data governance dictates that an organization should implement high-level DLP tools to secure highly sensitive business data and classifying it properly would provide a clear picture of the most suitable DLP tools to use.
Data management policies
Data management policies govern the general management of data to ascertain its security, integrity, usability and availability. Data classification plays an integral role in formulating and enforcing adequate policies for managing data. For example, one of the main purposes of classifying data is to track all regulated information to ensure that its usage aligns with regulations like ISO27001, PCI, GDPR and HIPAA. Data classification can identify data whose usage must align to industry standard practices. The best practices must take into consideration the security levels of available information such that any data handling activity is as per the management policies.
Furthermore, data classification breeds an effective cybersecurity culture. Once employees are accustomed to handling classified data, i.e. handling confidential or highly sensitive information, they tend to observe the implemented policies for managing such data. The risks associated with employee ignorance regarding data management are mitigated, not to mention that data classification makes it easier to observe such policies. Besides, classifying information would help identify operational areas where data is exposed to more risks. Employees working in such areas can be equipped with additional training on the best security practices for handling data and also allow the organization to channel more cybersecurity resources.
Backups and obsolescence of information
Frequent data backups should be a priority of a sufficient cybersecurity program. Cyberattacks are known to be executed at any moment, and a backup ensures availability of critical business data in the face of a data breach. Data classification enhances a data backup policy since suitable security measures can be adopted for backups with sensitive information. Unavailability of critical data backup can be devastating when an organization has been breached. For this reason, data classification guides prioritize the policies to protect important backups. Such include using encryption to secure confidential data or multifactor authentication to only allow authorized individuals to access the backups.
Classifying data can further identify outdated backups, i.e. backups made a long time ago and are no longer useful, such that they can deleted to create more space for highly sensitive data and most important reduce cost of infrastructure. Notwithstanding, a data backup should be done in a manner that allows a user to access a specific data set. Instead of storing unstructured data that would lead to a tedious search for specific information, data classification categorizes and labels the data. This way, an organization can be able to quickly locate and retrieve compromised data. More so, data classification can aid in determining whether specific data backups should be stored in highly secured server or in a cloud environment without stringent security controls.
How Kriptos can help
A manual data classification can take forever, especially for huge organizations. For instance, an organization of 5.000 to 10.000 employees would have more than 10 million files. It would take 20 years to classify the data manually.
Kriptos is an automatic data classification software that use Artificial Intelligence to ensure accurate classification for your company. Based on the previous example, Kriptos understand the content and context of each file and could classify 10 million files in less than a week.
Request a free “Data Classification Assessment” here to discovery the risk your company is expose.
