One of the biggest challenges for IT security managers is still the protection of sensitive company and personal data. It is irrelevant where this data is generated, processed or stored. The only thing that counts is the strategy of how data is recognized, classified and ultimately protected from unauthorized access. Unfortunately, the topic has still not gained a foothold in the most important levels of the hierarchy - the executive floors - as a survey conducted by the Allianz für Cybersicherheit in 2018 showed. Although laws such as the DSGVO, the IT security law and its extension to version 2.0 helped to create awareness, there is still a need to catch up

Headlines continue to ensure that the topic of data outflow is given priority in case of doubt. Too many security incidents with corresponding data loss have affected servers or databases that were previously insufficiently secured. Inside the darknet there is a lucrative black market that is only flourishing because cybercriminals know that many companies - especially those operating in multi-cloud environments - still have no idea where all their sensitive data is stored.

Avoid data outflow with data recognition

It is very difficult to completely avoid data breaches, but data leaks are preventable, and steps can be taken to strengthen an organization's security efforts. Knowing your data means having a good understanding of where sensitive data is located through data discovery and classification. Without this important foundation, organizations cannot know what content to protect, where it is located, who can access it, when it was created, and so on.

Once data is discovered or generated, it should be classified (identified and grouped) based on certain patterns and algorithms. This enables IT professionals to make informed decisions about security, data sharing, data access, digital transformation, cloud migration, and prioritization of protection. Data should not be treated as if it is all equally risky. Rather, it should be classified by its criticality - high, medium or low.

Risk analysis is the foundation of security and improves compliance

If data discovery and classification is followed by a risk analysis, a comprehensive and holistic security baseline can be established. Such analysis helps IT teams understand the sensitivity of data in order to classify the level of risk in the data. These capabilities also help organizations enforce data sovereignty and compliance with privacy and security regulations such as GDPR, PCI DSS and HIPAA.

Data discovery is essential for risk identification, minimization, and mitigation, enabling you to do things like:

Build a comprehensive, up-to-date data inventory
Analyze and classify data according to risk category to prevent security breaches
Utilize efficient scans to discover structured and unstructured data across an enterprise
Prioritize remediation using data encryption and tokenization products
Leverage discovery and classification tools that help compliance teams demonstrate fulfillment to auditors and regulators
Adhere to data security and privacy policies and regulations that if broken come with costly penalties

Across organizations, data discovery can reduce vulnerability for data breaches and theft, expensive non-compliance fines, and other unnecessary expense and risk.

Using Data Discovery and Classification to Reduce the Risk of Data Leaks

Data discovery and classification can reduce susceptibility to data breaches, unnoticed data leakage and costly fines for non-compliance.

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