If you’re in charge of protecting sensitive data, you know that the importance of robust Data Security Posture Management (DSPM) cannot be overstated. Data resides in more places than ever, and the challenges of protecting sensitive data in complex, cloud-centric environments are mounting.
While the term DSPM is only a few years old (Gartner coined it in 2022’s Hype Cycle), the need for deploying it in your organization is essentially irrefutable. We’d argue that businesses can already mature the technology.
What does this mean, and why does it matter?
Maturing a technology is the process of developing and improving the solution over time to enhance its effectiveness, efficiency, reliability, and usability. It’s about making sure the flaws are removed or at least reduced. Maturing a technology is clearly crucial for any business function, but when it comes to sensitive data, the stakes are even higher.
In this article, we’ll discuss the steps required to mature your DSPM and explore how, by fulfilling each step, your organization can reduce its risk to sensitive data.
First, let’s define DSPM. DSPM provides visibility into where sensitive data resides, who has access to it, and how it’s used. In today’s cloud-centric environment, DSPM is crucial because traditional data protection methods fall short.
When it comes to DSPM solutions, their key goal is to address data protection challenges by identifying sensitive data across the cloud, tracking data lineage, and remediating risks in real-time.
The best DSPM solutions offer a comprehensive overview of these capabilities and leverage AI for autonomous, semantic-based data discovery and risk identification. These solutions empower organizations to categorize data accurately and assess risks without relying on upfront rules or complex configurations.
The journey to mature DSPM begins with acknowledging existing gaps in an organization’s understanding of data risks. Let’s face it: too many organizations lack insight into the risky use, storage, or movement of their data.
Recognizing these blind spots is the first step toward developing a more secure data environment. As this awareness grows, the risk to data begins to decrease, paving the way for more targeted and effective data security strategies.
Here are the six steps:
Let’s walk through each step.
The first step in maturing DSPM involves recognizing deficiencies in an organization’s understanding of data risks. It’s important here to include acknowledging any lack of visibility into how data is used, stored, or transferred, which can pose significant risks.
You should also conduct an initial risk assessment to understand where the organization stands in terms of data security. This involves identifying sensitive data, understanding how it’s currently managed, and pinpointing potential vulnerabilities.
The goal here is to establish clear objectives for what the organization aims to achieve with DSPM. This could include compliance with specific regulations, protecting intellectual property, or securing customer data.
The next step is all about understanding where sensitive data resides and ensuring all data is classified accurately. Typically, discovery and categorization are best with AI-driven tools for autonomous discovery of sensitive data across various repositories. Why? Because legacy rule-based systems only look for content within data and not context around it, and leaving classification up to employees is time-consuming and costly.
With semantic-based categorization, organizations can accurately classify all data types, such as financial information, intellectual property, or personally identifiable information (PII).
In this third step, advanced tools are used to analyze risk patterns and behaviors, which includes assessing the likelihood and impact of potential data breaches or leaks.
To identify risky behavior, DSPM solutions leverage advanced data analytics tools to identify patterns in data usage and access that may indicate potential risks.
Risks that can be identified in this step include:
Identifying sensitive data and any potential risk to that data is only helpful if you can act to mitigate it. Here’s where remediation comes in — addressing oversharing, cleaning up access permissions and removing duplicate data.
Address oversharing: Identifying and rectifying situations where sensitive data is shared more broadly than necessary, both within and outside the organization. This happens more often than you think.
Permission cleanup: Following as close to the zero trust model as possible, reviewing and updating data access permissions to ensure that only authorized personnel have access to sensitive data is crucial for robust data security posture.
Duplicate data elimination (attack surface minimization or data minimization): Implementing strategies to identify and eliminate redundant or duplicate data, thus reducing the attack surface.
While the first four steps to maturing your data security posture fall under the active category, the final two are about being proactive. In the prevention stage, you want to ensure data categories align with the data classification schema and that DSPM is well-integrated with any Data Loss Prevention (DLP) tools, Cloud Access Security Brokers (CASB), and Security Information and Event Management (SIEM) solutions.
Security framework alignment: Align data categories with the appropriate security frameworks listed above to ensure that each type of data is protected according to its level of sensitivity.
Policy implementation: Develop and implement robust data security policies, including access controls and encryption standards.
Employee training: Conduct regular training sessions to minimize insider risk by educating employees about data security best practices and the importance of compliance.
Staying with the proactivity theme, the last step involves continual control improvements that play a critical role in strengthening data security posture. This can be accomplished by carrying out:
A governance program: DSPM should be part of a governance program, where it stands as a foundational technology to complement other technologies (DLP, CASB, etc.) that enable controls to be applied to the data.
Regular reviews: Conduct periodic reviews of the DSPM strategy to ensure it remains effective against evolving threats and aligns with changing business objectives.
Technology updates: Keep up with advancements in DSPM technologies and integrating new tools and practices as appropriate.
Feedback loops: Establish feedback mechanisms to learn from past incidents and continuously refine the DSPM approach.
Maturing your DSPM is a continuous journey that requires a strategic approach, leveraging the right tools and expertise. By understanding the challenges and solutions available, organizations can significantly enhance their data security posture, ensuring that sensitive data is protected against all types of threats.
Concentric AI is designed for the modern organization. While our solution autonomously handles the data security tasks listed in the six steps, our dedicated team will step in to fill any gaps to help ensure a robust security posture.
With our managed approach to DSPM, you benefit from:
Autonomous Action: Autonomously discover, categorize, and remediate potential security risks.
Human Expertise: Our expert team complements the product, addressing any complex challenges.
Reduced Liability: With our autonomous solution and experts to help fill in any gaps, organizations can confidently take action, reducing potential liabilities.
24/7 Security Assurance: With Concentric AI, you’re not just getting a product, you’re getting a dedicated security partner.
Want to see firsthand — with your own data — how you can quickly and easily deploy Concentric AI’s solution to mature your DSPM? Book a demo today and experience the freedom of protecting your data without rules, regex, or end-user involvement, and the peace of mind knowing you have a team of data security experts to back you up.
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