By Jeff Crum

Data privacy has always been a hot topic but in today's world of data sprawl, data security threats, and increasing data regulations, the stakes have never been higher.

Bad actors, human error, ransomware, and other security threats pose risks to data every minute of every day. Plus a more stringent regulatory environment is forcing organizations to enforce compliance - or risk fines. And as data increases in volume and sprawls across applications and storage, these efforts become increasingly complicated.

Let's take a look at some key principles of data privacy:

  • Protection & Recovery: One important area in ensuring data privacy is making sure that your data is protected. This includes the ability to recover data in the event of accidental deletion - and in the case of a malware or ransomware attack. Making sure you can recover your data in these scenarios mitigates the impact of some of these worst-case scenarios.
  • Access & Prioritization: Once you have a protection mechanism deployed, ensuring that sensitive data and personally identifiable information (PII) is locked down tight is an important safeguard you can put into place. Prioritizing the protection of sensitive data, including PII, using technology such as AI/ML, reduces the risk of exposure. Additionally automation helps take the risk of human error out of the equation. Automation can also extend to detecting anomalous data access, raising the red flag so that you can step in and protect key data before it is compromised.
  • Compliance & Governance: Together, all of the actions above can appropriately address compliance with key data regulations and help define your overall data governance regime, taking global scope and regional nuances into account. Read about how South Tyrol Civil Protection Agency overcame their challenges with GDPR compliance here.

This may sound simple but getting data privacy right can be incredibly complex. Let's talk about some things you can do to make ensuring data privacy easier.

  • Manage data sprawl. Data growth and an increasing number of data sources and repositories continue to challenge enterprise data management, with no end in sight. But there are some things you can do to mitigate this, including looking at the number of tools that you're using to manage data across your environment, breaking down data silos in the process. This blog by Commvault's Don Foster goes into further detail about what data sprawl is and how to help solve for it.
  • Take a comprehensive approach to data management. The principles mentioned above require a number of different data management capabilities - including data protection, data security, data compliance and more, which make use of advanced techniques with AI/ML as well. Look for data management platforms and solutions that provide deep capabilities in these areas.
  • Simplify your environment. Let's face it-most companies have many, many vendors, some of which do the same or similar things, and all of which have access to your data. By scaling back the number of trusted vendors and streamlining deployments, you can capture efficiencies and mitigate risk. By introducing a single data management platform, you can simplify the user experience and reduce errors related to data privacy, and help allow your data to work for you instead of against you.

Data privacy is a deep and complex topic, but at Commvault, we can help you crack the code. For more information, visit www.commvault.com/data-compliance.

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Disclaimer

CommVault Systems Inc. published this content on 25 January 2022 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 25 January 2022 15:06:05 UTC.