What is data security posture management?

• Classifying sensitive data: Identify the location of data within a company, including in cloud storage, and classify it in terms of sensitivity.
• Monitoring: Continuous vulnerability scanning and assessments of a company's data security to be aware of risks and threats, often using AI tools.
• Minimizing risks: Remove vulnerabilities at their source.
• Reporting on compliance: Inform stakeholders and other entities of vulnerabilities and solutions, compliance, and steps taken to maintain data security.

Effective DSPM combines AI and human-led vulnerability assessments and threat resolutions. While a company may not be able to plan for all types of data breaches, having a plan in place will allow it to respond effectively and minimize the damage to both the company and its reputation.

Why do companies need DSPM?

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Companies require an effective DSPM strategy to protect their sensitive data and to demonstrate to stakeholders and customers that data security is a top priority for the business. As an ongoing practice, DSPM also ensures that a company stays up-to-date in its security practices and integrates with new solutions as they become available.

More specific reasons include:

• Complexity: Companies store data in various environments and require a robust approach to securing it.
• Data volume: The amount of data in the world is increasing, leaving companies with the need to classify potential threats.
• Compliance: DSPM provides companies with evidence of compliance with national and industry regulations on data protection.
• Risk management: Effective planning and threat classification prevent alert fatigue and optimize resource use when resolving vulnerabilities.
• Evolving threats: Companies must stay on top of new threats and be prepared to respond to emerging ones.

With a DSPM in place that provides a clear and logical framework for managing risks to data security, companies can confidently expand and grow to adapt to changing market landscapes.

How and when is DSPM used? (Use cases)

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As the volume of data grows, so do the expectations of how to use it for products and services. As a result, teams may put a company's data at risk while performing their work. Usually, this is done inadvertently. Here are some examples:

• Unencrypted/insecure storage of data

Team members may use a temporary workaround for their data that they forgot about, which left data in insecure data stores. A DSPM that includes automated tools will detect these situations and alert personnel about insecure data.

• Shadow analytics

One team may decide to introduce shadow analytics into their processes without informing others. This puts the company at risk of breaching privacy regulations and legislation as well as potentially opening the company's data to external parties. DSPM will detect copies of data being used by third parties to evaluate them for risks properly.

• Data sovereignty issues

Legislation can dictate where data is to be stored, such as within specific geographical areas. DSPM provides tools to track where a company’s data is stored to ensure compliance with these rules.