How Data Governance Automation Reduces Enterprise Security Vulnerabilities
Data governance and cybersecurity are closely connected. As a vital part of cybersecurity mechanisms, solutions, and actions, data can be the target of attacks, but it can also be the asset that helps security systems operate. After all, threat intelligence is often grounded in data. Behavioral analysis and AI-driven security involve interpreting data signals.
Given data’s essential role in the modern business landscape, organizations must implement good data governance practices. They need to treat data as a high-value asset, so that it should be vigilantly safeguarded. In this sense, it’s critical to ensure your databases are organized and managed in a manner that’s optimized to fend off cyber threats.
Organizations need to maximize data governance, particularly through automation and artificial intelligence. These practices enable the efficient management of potential attack surfaces and the minimization of vulnerabilities.
Full Data Visibility
Unknown data is ungovernable data. Organizations can only monitor and manage information if they are able to take control of it. One of the key elements of data governance is visibility, which is also critical to cybersecurity.
It would take forever to manually map out the data in an organization. On average, a person produces over 6 TB of data per hour. Not all of this data will be to be secured, but most of it should be scanned to determine which is relevant and which is not. Going through petabytes of data to find sensitive information is an exhaustive and resource-intensive task.
That’s why organizations need to leverage automation and artificial intelligence. The leading data governance applications are designed to ensure that all relevant data are accounted for automatically. This accounting of data is also done contextually with the help of AI.
The failure to have visibility over all the important data in an organization is dangerous, because it means there are potentially unprotected IT assets. These include devices that can be physically accessed and used to attack the IT infrastructure of an organization, user accounts that are still active and can be used to access online resources, old backup files that may contain sensitive information, user data, as well as intellectual property. Unmanaged, these resources can all be compromised and used against the organization.
Data Lifecycle Monitoring
The lifecycle of data can be monitored automatically with the help of a data governance platform. Data can be classified automatically to assign sensitivity levels to know how to deal with them appropriately in the context of data security and cybersecurity in general. The generation of metadata can be automated to make the process faster and less prone to errors. Additionally, data lineage tracking can be automated to make it easier to examine the source, transmission, and transformation of data to support data quality assurance and regulatory compliance.
When mapping out repositories, strategic sorting of data is important for efficiency. Not all information needs to be subjected to lengthy security procedures. The encryption and decryption of all data, for example, is extremely time-consuming, so encryption should only be applied to sensitive information. It is important to expedite the process of determining which data should be subjected to stringent security measures instead of implementing an all-encompassing data security blanket.
On the other hand, automated metadata generation is helpful in boosting threat detection. This makes it easier to identify data or datasets, especially when connecting these resources to automated threat detection systems. The metadata may also be updated automatically for unauthorized access attempts, which helps in identifying possible malicious actions.
Moreover, automated data lineage monitoring supports file auditability, which is useful in investigating security issues. It allows organizations to track the movement of files in great detail, from generation to transformation, to better understand the origins of a vulnerability and the ensuing attacks. This supports the fast diagnosis of problems, as well as the formulation of suitable solutions. Likewise, it helps organizations in addressing challenges in complying with data regulations.
Significantly Reduced Errors
Most of the mistakes in data handling are attributable to human actions. Employees may mistakenly input dates or numbers, truncate copied alphanumeric data, click on malicious phishing links, use easy to hack credentials, or repeatedly commit typographical errors without them realizing it. Some may accidentally delete files, which can mean permanent data loss. There are also instances of executives or managers who wrongly share sensitive data with unauthorized parties.
Using data governance automation, it is much easier to avoid these situations. When backups are automated, data is always copied precisely, while transformed data or those that have been merged with other data sources are virtually always accurate. This may sound trivial or irrelevant to security, but small details such as dates can be highly important information, because they can impact the application of security patches or software updates. They can also skew the analysis of log files and impact data retention and destruction.
More importantly, automation in data governance ensures that data security rules and policies are automatically carried over to new locations. For example, keys, passwords or other network secrets can be consistently kept encrypted with their decryption keys stored in a dedicated, secure vault, separate from the encrypted data itself.
Organizations can avoid the serious consequences of misconfigurations if these are automatically set and consistently applied. While some pundits argue that people should no longer be viewed as the least reliable component in cybersecurity systems, the reality is that human involvement continues to translate into more errors.
Organizations have to put in place various measures and solutions, including enhanced cybersecurity training, to make people less vulnerable to cyber attacks. People’s mistake-prone judgments and inconsistencies can easily counter security measures and weaken cyber defenses.
Automation Makes for Greater Data Protection
With the use of reputable data governance solutions and proper configuration, organizations can expect a significant reduction in their data-related security vulnerabilities. Automated data governance enables faster and more comprehensive data visibility, which is crucial in protecting data. It also supports the tracking of data throughout its different phases, enhancing threat detection capabilities and investigation. Additionally, it leads to a massive reduction in configuration and security-related errors.
Ultimately, governance reduces enterprise security vulnerabilities as it aligns with key concepts in cybersecurity – namely visibility, comprehensive and continuous monitoring, and the reduction of errors.
