Things to know about Smart Data Discovery and Tools

Things to know about Smart Data Discovery and Tools

Data breaches are reported in the news almost every day. With the regular introduction of new compliance regulations such as the GDPR, data identification and classification is becoming an increasingly important task for companies of all types and sizes. However, data classification is difficult and often unreliable, especially when left to business users. Classification should be automatic and contain a detection section to find sensitive data so you can be sure that all confidential data is protected.

Here this article has about data discovery tools that will help you stay compliant and avoid dangerous data loss events.

Before that let us see!

What is Sensitive Data Discovery?

Sensitive Data Discovery and Protection (SDDP) is an integrated data protection service that offers functions such as detecting sensitive data, checking data security, identifying sensitive data, and detecting anomalies. SDDP enables you to meet the requirements for personal data protection compliance and security auditing in cloud computing as defined in the Baseline for Classified Cyber Security 2.0 Protection. Sensitive data exists in your database in various forms and includes valuable data such as customer data, technical data and personal information. Loss of sensitive data can cause serious economic and economic losses for your company.

With your authorization, SDDP scans your data on Cloud services such as MaxCompute, Relational Database Service (RDS), and Object Storage Service (OSS) and uses rules to detect confidential data to determine whether it is confidential.

Definition of Data Discovery?

In data discovery, sensitive or regulated data is identified and placed to protect it appropriately or to delete it safely. One of the biggest trends in business intelligence in recent years, data discovery, is a priority for many enterprise security teams because it is a key component of compliance. Disclosure of data involves reviewing sensitive or regulated information, including confidential or confidential data, as well as proprietary data such as identifying information (PII) or electronically protected health information (ePHI). By disclosing data, the security team can identify this information to protect it and ensure its confidentiality, integrity and availability.

What is data discovery classification?

Data recognition and classification go hand in hand. Data discovery scans your environment to determine where the data is located (whether structured or unstructured) – for example, in databases and on file servers which may contain sensitive and / or regulated data. Classifying data according to the data discovery process is more complex. This involves identifying data types in open data sources using a predefined set of models, keywords, or rules, and assigning labels to classify this data. For example, if you work for a health insurance company, you are using a medical ID model to look for sensitive health information.

Why is the discovery and classification of data important?

Simply put, if you don’t know what data you have and where it is stored, you can’t protect it effectively, which means your data is vulnerable. In addition, you will inform data classification about how you should treat and protect your data, including the guidelines you need to create around it, and guide your privacy priorities and risk mitigation activities. Lastly, it helps to identify the data covered by the regulation and implement the necessary controls to achieve compliance.

Requirement of data Discovery

In today’s era of remote workforce, business is often conducted in the cloud, where file sharing and storage is commonplace. This is a challenge for companies that need to know exactly where their sensitive or regulatory data is located. Given today’s business process networks, data is stored in multiple systems, applications, databases and shared files, which makes data security, authentication and protection a challenge for enterprises. Data disclosure is a solution for fully identifying an organization’s data and ensuring that appropriate controls are in place for best security practices and regulatory compliance measures.

Benefits of data classification

  • Provides support and expertise in executing special order programs that promote compliance as part of the corporate culture and demonstrate a commitment to compliance to regulators
  • Increase awareness about the company value and sensitivity of data
  • Helping companies comply with rules and regulations
  • Help make business processes more efficient
  • Lower costs – backup and storage
  • Prevent accidental or dangerous data leakage
  • Allow users to take ownership and control of data.

The best software for discovery sensitive data

Using Sensitive Data Detection software, organizations can find sensitive data such as Personally Identifiable Information (PII), Protected Health Information (PHI), Payment Card Industry Data (PCI), Intellectual Property (IP), and other important business data in several business systems, including databases and applications as well as user endpoint.

Organizations use sensitive data detection software to help locate their critical data, often to meet industry standards for data protection and confidentiality. This includes the General Data Protection Regulation (GDPR). The California Consumer Privacy Act (CCPA); The Health Insurance Portability and Accountability Act (HIPAA); Payment Card Industry Data Security Standard (PCI DSS); International Organization for Standardization (ISO) Standards; and others. Sensitive data discovery software is typically used and managed by the information security team, while the privacy team can request report data. This solution searches for structured, semi-structured and unstructured data stored in local databases, cloud storage, email servers, websites, applications and more.

Sensitive data can be detected in a number of ways, including manual surveys (managed by workflows) or automated detection tools. To fall into this category, the product must have an automatic data detection feature.

Sensitive data discovery software shares many kinds of tools, including data loss prevention (DLP) software, data-centric security software, database security software, and data protection software. In general, sensitive data detection is offered as the main function of this tool. Sensitive data discovery is different from data discovery software, which is part of business intelligence software and helps companies research their data to spot trends, identify emergencies, and visually analyze their trends. Sensitive data detection software also differs from eDiscovery software, which is used for litigation to collect and share data files from companies and individuals involved in pending litigation. Some of the data discovery tools are:

  • Egnyte
  • Nightfall
  • Data grill
  • IBM Security Guardium Data Protection
  • Azure information protection
  • Collibra
  • IBM Security Guardium Risk Manager
  • Manage Engine DataSecurity Plus
  • Sail point
  • Spirion
  • Varonis GDPR Patterns

Then!

What is unstructured data?

Unstructured data is data that is not stored in a fixed record-length format. Examples include documents, social media feeds, and digital photos and videos.

Why is unstructured data important?

With 80% of business data being viewed and processed on a daily basis, organizations must adapt to cope with the growing number of unstructured data.

Who is affected by unstructured data?

Internally, almost every company department uses unstructured data in some form. Externally, unstructured data is used to monitor and report on the movement of shipments and / or assets with sensors and others.

How can companies use unstructured data?

Unstructured data preparation and processing of data and options to add it to a recording or storage system for future use are available locally and in the cloud.

Conclusion:

Choosing the right tools is an essential part of a successful data discovery process. When selecting a tool, the project team should ensure that it provides easy and efficient connectivity to data sources and allows automatic data profiling for identified sources, automatic identification of sensitive elements for anonymization, and the creation of a representative subset of data from the entire data set and has capabilities. to accelerate your return on investment.