IDC Names Securiti a Worldwide Leader in Data Privacy


8 Data Discovery Best Practices

Published July 16, 2023 / Updated November 21, 2023

Listen to the content

With data growing at an unprecedented rate, organizations need to know what data they hold for data security & privacy as well as global compliance. While Data Discovery solutions have been traditionally utilized to get visibility into sensitive data, they are not able to scale and offer effective detection at petabyte scale common in a modern cloud environment.

Common challenges include:

  • Data discovery requirements do not consider possible changes in data sensitivity or  volumes of data
  • Missing Data
  • Incorrect Data
  • Incoherent Data Management
  • Lack of Data Taxonomy
  • Missing Data Fusion

Best Practices in Data Discovery

An organization must have a plan and process in place to effectively manage personal data breaches. Timely and accurate disclosures to regulatory authorities and impacted data subjects can lessen the adverse impacts of a personal data breach. Besides, organizations can use such events to learn about their weaknesses and gaps, and improve their overall security posture to reduce the risk of personal data breaches in the future.

With the increasing use of technology and businesses starting to collect more and more personal data, there has been a growing concern for data privacy. Securiti’s PrivacyOps methodology enables organizations to implement efficient data discovery tools and breach management. Securiti offers the sensitive data intelligence solution that will help organizations enhance and improve their data privacy and security processes.

1. Discover & catalog shadow and sanctioned assets 
One of the most critical capabilities of any efficient data discovery solution is the ability to discover and build a central catalog of all data assets, including all sanctioned & shadow data assets in on-premises & multi-cloud environments. Keeping track of the data is the first step towards protecting it from malicious intent and minimizing the "blast zone."

2. Extract and catalog asset metadata

Sensitive data catalogs provide native connectors and REST-based APIs to scan and extract metadata from all data assets. These include data warehouses, cloud data stores, non-relational data stores, and many more. There are three types of metadata

  • Business metadata: Provides business context about the data such as ownership, location, etc.
  • Technical metadata: Provides context for privacy and security, including insights about data.
  • Security metadata: Provides insights into the security posture of the data asset and its associated data.

3. Detect sensitive and personal data

Once on-premises and cloud-based assets are discovered, security administrators need to know what sensitive data is stored in these assets. Few important categories of sensitive environment impacts most businesses:

  • Health information
  • Financial information
  • Educational information
  • Trade or business secrets
  • Personal information

4. Catalog, classify & tag sensitive data

A sensitive data catalog provides insights into sensitive data attributes and security and privacy metadata such as security controls, the purpose of processing, etc. A sensitive data catalog should be available by default in a good data discovery tool since it parses and organizes the content in a meaningful way. Data catalog capabilities include:

  • Searchability
  • Unified view
  • Policy-driven

5. Assess overall data risk posture

Sensitive Data Intelligence should provide comprehensive data risk assessments that include data sensitivity, data concentration, and instances of cross-border transfers.

A data discovery tool can use all these parameters to assess the overall data risk score, which can prioritize risk mitigation activities.

6. Built a graph between data and its owners

To fulfill DSR requests promptly, organizations should ensure SDI™ solutions can discover personal data and link discovered data with users' identities automatically.

Fulfilling DSR Requests are a requirement under global privacy regulations, and failure to do so can result in hefty fines.

7. Scale to petabyte volume with high accuracy

As data volume reaches the petabyte scale, the security and privacy risks associated with data increase.

Organizations need a product that can scale to large data volume and provide detection or scanning capabilities that can reduce their total cost of ownership (TCO) over time by minimizing compute resources required to find sensitive data within these assets.

8. Map data to compliance and regulations

In privacy regulations such as GDPR and CCPA, organizations must document and furnish a record of all their data processing activities or Article 30 reports.

With a robust data discovery tool, administrators can build a centralized catalog of their data assets and discover sensitive data stored in them. Using automated discovery mechanisms, organizations can ensure their data maps and Article 30 reports are up to date.

data discovery practices

The future of data discovery is here and Securiti has, and always will be the forerunners in enabling organizations. Request a demo today!


With data increasing and traditional data discovery methods not up to the par to survive in a hyperscale environment, organizations need to quickly start thinking of alternatives that will help them manage the growing data and also stay in compliance with privacy regulations. Automation is becoming more of a necessity than ever before and integrating automation within your business processes is now a requirement if your organization hopes stay abide by global privacy laws.

Frequently Asked Questions (FAQs)

Data discovery methods include data scanning, data mapping, data cataloging, and the use of data discovery tools and software. These methods help organizations identify where personal data is stored and how it is processed.

Strategies for data discovery involve creating a systematic approach to locate, classify, and manage data. This includes conducting data audits, engaging stakeholders, implementing data discovery tools, and documenting data flows and processing activities.

Key factors of data discovery include understanding data sources, data types, data processing activities, data ownership, and data access controls. Effective data discovery also considers regulatory requirements and privacy implications to ensure data protection and compliance.

Bedrock of your Privacy & Security

A Comprehensive Platform

Anas Baig

Authored by Anas Baig

Anas Baig is a Product Marketing Manager with a proven track record in the cybersecurity industry. He has been a prominent contributor to numerous esteemed publications, including Infosecurity Magazine, CSO Online, Tripwire, Security Affairs, Network Computing, Security Boulevard, and several other renowned cybersecurity blogs.His in-depth knowledge and extensive experience in the industry make him a trusted source for cutting-edge insights and information in the ever-evolving world of cybersecurity.

Join Our Newsletter

Get all the latest information, law updates and more delivered to your inbox


More Stories that May Interest You

At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks.


Gartner Cool Vendor Award Forrester Badge IAPP Innovation award 2020 IDC Worldwide Leader RSAC Leader CBInsights Forbes Security Forbes Machine Learning G2 Users Most Likely To Recommend