Data risk management involves detecting, assessing, and remediating critical risks associated with data. We're talking about risks like exposure, misconfigurations, leakage, and a general lack of visibility. Left unattended, these data risks can mature into incidents that bog you down.
Basically, data risks can turn what should be your most valuable asset into a liability. According to IBM, in 2024, a single data breach could cost businesses an average of $4.88 million. Losses like these just aren’t worth the risk, which is why data risk management should become priority number one.
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What are the most common data risks?
Poor data security: According to Verizon, 14% of security incidents occur because of exploitable vulnerabilities, and 68% are down to human error. The moral of the story? Having lax data security controls is asking for trouble.
Non-compliance: GDPR, HIPAA, PCI DSS, CCPA…The list of compliance obligations is long, and don’t forget that the consequences of regulatory violations are severe. Even the smallest data governance and management slip can be costly.
Inadequate access controls: At any given time, there are hundreds, maybe even thousands, of digital identities roaming your cloud. If any of these identities have unnecessary access privileges, they could be hijacked to access, corrupt, or exfiltrate your crown jewel data.
Lack of visibility: Without complete visibility, your environments could house shadow data, which is basically data that your IT and CloudSec teams don’t know about. And who knows what dangerous risks are hiding there?
Misconfigurations: How much harm can a tiny misconfiguration cause? The short answer: a lot. Misconfigured databases, storage buckets, VMs, and CI/CD tools can create new data risks or make existing risks worse. Also, without a prioritized view of misconfigurations, it’s impossible to spot data risks like excessive exposure.
Multi-tenant challenges: You might think that multi-tenant architectures are the way to go. Yes, but they also pose data risks like exposure, comingling, and unauthorized access. If you’re working with multi-tenant SaaS and PaaS infrastructures, you have to pay special attention to cross-tenant data risks.
Sophistication of attacks: As if it weren’t challenging enough to deal with cloud-native data risks, cybercriminals now use advanced attack tactics that are difficult to counter. Also, with the rise in GenAI attack tools, there’s a lower barrier to entry for anyone looking to conduct cyberattacks. All it takes is someone with a laptop and a few GenAI attack tools.
What are the key components of data risk management?
1. Data identification and classification
If you want to tackle critical data privacy and security risks, you’ll need a comprehensive view of your data assets. Your first step in data risk management involves identifying data across your cloud platforms and repositories.
Once you discover your data, it’s time to assign classifiers. Typically, data classification focuses on criticality and sensitivity, and examples of common classifiers are “PII” or “PHI.” If you need more specific classifiers for unique use cases, you should custom-classify data instead of choosing the usual options.
2. Risk assessments
You’ve managed to build a comprehensive inventory of your data. What’s next? Thorough risk assessments. A data risk assessment involves analyzing how data moves within your network and interacts with other resources. If you want to uncover critical risks like misconfigurations, exposure, and overprivileged identities, it’s a good idea to conduct assessments with detailed cloud and workload contexts. That is, conduct assessments that reveal the risks that matter to you.
Since not all data risks are equal, spending time on non-critical risks is a waste of time and resources. Instead, identify which risks and attack paths lead to crown jewel and mission-critical data, and address those risks first.
3. Security controls
With the right data security controls, you can make sure that only invited, relevant, and legitimate users have access to critical data. This is a crucial aspect of data breach prevention. Typically, security controls are built around principles like least privilege and “never trust, always verify.”
Build the right security controls and you’ll notice increased strength across data privacy, security, compliance, and access governance pillars. In short, this means fewer data risks and guaranteed business continuity if incidents occur.
Unsure about where to begin with data security controls? Here are a few options to get you started:
Encryption
Data masking
Multi-factor authentication (MFA)
Virtual private networks (VPNs)
Firewalls
Anti-virus and anti-malware software
Automated backup mechanisms
Data loss prevention (DLP) solutions
4. Continuous monitoring
To keep up with the cloud’s fast pace, make sure that data risk management is a proactive and constant effort. Want a foolproof way to guarantee continuous monitoring of data assets? Use strong real-time threat detection tools and capabilities. It’s the quickest and most effective method to discover, validate, and remediate suspicious activities and access.
But proceed with caution; continuous monitoring isn’t just about identifying and responding to data security risks. You also have to make sure that data remains compliant throughout its lifecycle. To do that, conduct continuous assessments of data compliance.
Think about it this way: With continuous monitoring of data risks, you can remediate vulnerabilities before threat actors even get a glimpse of them.
Best practices for developing a strong data risk management strategy
In this section, we’ll cover some ways you can establish a resilient and long-term data risk management strategy.
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While there are many data security and compliance risks that all enterprises have to deal with, your organization will face a few that are unique to you. To mitigate those risks, you need to identify them first.
To assess your organization’s exact needs, take into consideration your industry, geography, and data-sharing practices. For example, if you’re in healthcare, your data risk management strategy will revolve around frameworks like HIPAA. Similarly, if your enterprise stores or ports data in different countries, your data risk management strategy must prioritize data sovereignty.
Prioritize data risks
With the amount of data flowing in and out of the cloud, it’s impossible to stay on top of every single data risk. Besides, some data risks just aren’t that important. Non-critical data risks, a lot of the time, are just noise.
Instead, you can use your DSPM tool to get a prioritized view of data risks in your cloud environments. This prioritized queue of data risks is based on organization-specific risk factors and the complete context of your cloud.
Use data risk management frameworks
Data risk management frameworks can provide rules, processes, and templates to mitigate data risks across the entire data lifecycle. By using data risk management frameworks, you base your risk management strategy on globally recognized guidelines and concepts.
Here’s a good starting point for data risk management frameworks:
Leverage the right tools and technologies
You need solutions that were built for the cloud to tackle data risks in the cloud. Positioning unified cloud security tools at the center of your data risk management strategy is the best way to break down risk management siloes, streamline risk mitigation processes, and enforce best practices.
There are countless options for cloud-native security tools, but keep an eye out for a unified platform with DSPM, CDR, CSPM, CIEM, and AI-SPM.
Pick the right key performance indicators (KPIs)
Without the right KPIs, you’ll never know if your risk management strategy is effective. Consider this: If data risk alert volume is a KPI but most of your alerts are for non-critical risks, you won’t be able to accurately evaluate your data risk management capabilities.
Common KPIs include mean time to detection and response (MTTD and MTTR) and the overall number of data security and non-compliance events. Also, regularly conduct security audits and penetration tests. The results of these tests are also important KPIs for your risk management strategy.
Elevate training, awareness, and democratization
For comprehensive data risk management, you need more than just CloudSec teams. It has to be a collective effort. Your first move? Embed self-service capabilities across software development lifecycles so that your teams can discover and remediate data risks fast.
Going beyond data risks, it’s also important to know what hurdles you might face en route to a new data risk management strategy. Common examples include:
Lack of visibility across an evolving data ecosystem
Siloed data management tools
Getting the buy-in of key stakeholders
Understanding multi-tenant environments
Navigating CSP shared responsibility models
How Wiz can optimize data risk management
Managing data risks might seem like a formidable task, but a strong cloud security platform can help you navigate even the most complex risks. With a holistic and agentless CNAPP platform like Wiz that integrates DSPM, CIEM, CSPM, AI-SPM, and CDR capabilities, you can discover and classify all your data, remediate critical data risks, and meet even the most complicated compliance requirements.
In particular, Wiz DSPM can be the foundation of your data risk management strategy. With a complete cross-cloud security view, Wiz DSPM can help you discover, classify, protect, and harness data better than ever before.
Get a demo now to see how Wiz can reinforce your data risk management strategy and mitigate enterprise risks across the entire data lifecycle.
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