A Guide to Data Risk Management

Learn how to protect your most valuable business asset from costly breaches.

Poor data protection can seriously hurt your business operations.

You need to understand and measure your risks to protect against them.

Good data risk management helps you make smart decisions and keep your sensitive data safe.

We’ll show you what data risk management is, how to assess your risks, common threats to watch for, and proven strategies to protect your data.

What is data risk management?

Think of your data as your company’s crown jewels. Just like you’d protect valuable assets, you need a strategic approach to safeguard your information.

The goal is simple: keep your sensitive information secure while ensuring authorized users can still access what they need to do their jobs.

Data risk management policies are the specific guidelines you follow to protect your data.

These policies ensure the confidentiality, integrity, and availability of your data.

At the same time, data risk management helps you minimize the impact of data-related risks on your operations, reputation, and compliance obligations.

Why is data security risk management important?

Data security risk management is critical for several reasons:

• Protect Your Data: You’re more dependent on digital data than ever. You need to protect sensitive information like personal data, financial records, and intellectual property from unauthorized access, theft, or misuse.
• Stay Compliant: You’re likely subject to data protection regulations like GDPR and HIPAA that require specific security measures. Data risk management helps you stay compliant and avoid legal penalties and reputation damage.
• Keep Your Business Running: Good data risk management helps you maintain data availability and integrity. This keeps your business processes and services running smoothly.
• Protect Your Reputation: Data breaches can seriously damage your reputation. You’ll lose customer trust and business opportunities. Proactive risk management helps you prevent these incidents.
• Avoid Costly Breaches: The costs of data breaches can be huge - legal fees, fines, and cleanup efforts add up fast. Data risk management helps you prevent or reduce these costs.
• Make Better Decisions: Data risk management shows you potential risks and vulnerabilities in your systems. This helps you make smart decisions about where to invest your security budget.

Eight steps to perform a data risk assessment

Before you can manage risk, you first need to identify and prioritize risks to your data. You’ll do this through a data risk assessment.

Here’s how to do it:

1. Identify Your Data Assets: Start by finding and cataloging all your data assets. This includes databases, files, applications, and any other places where you store or process data.
2. Classify Your Data: Sort your data based on how sensitive and important it is. You might use categories like public, internal, confidential, or highly confidential. This helps you figure out how much protection each type needs.
3. Find Threats and Vulnerabilities: For each data asset, identify potential threats (like compromised employee credentials, natural disasters, human error) and vulnerabilities (like SQL injection, outdated software). Use dark web monitoring and vulnerability assessments to guide this process.
4. Assess Impact and Likelihood: For each risk you’ve identified, figure out how much it would hurt your business if it happened. Also evaluate how likely it is to occur. You can rate these as low, medium, or high, or use actual numbers like financial impact and probability percentages.
5. Prioritize Your Risks: Based on your impact and likelihood assessments, rank your risks. This helps you focus on tackling the biggest threats first.
6. Put Controls in Place: Figure out and implement the right controls to reduce each high-priority risk. You might use technical measures (password resets, access controls), administrative measures (policies, training), and physical measures (secure facilities).
7. Monitor and Review: Regularly check how well your controls are working and update your risk assessment to keep it current. Adjust your controls and priorities based on changes in threats, your business operations, or regulations.
8. Document Everything: Write down your risk assessment process, findings, and the controls you’ve put in place. Keep your team and management updated on your data risk situation so everyone stays informed.

Remember that data risk assessment isn’t a one-time thing. You need to revisit it regularly or whenever there are big changes in your data environment.

Common data risks

Now that we’ve covered why data risk management matters and how to identify your risks, here’s a list of common data risks you need to worry about:

1. Data Breaches: Unauthorized access to your sensitive data can lead to financial loss, reputational damage, and legal consequences.
2. Data Loss: Accidental or intentional deletion, corruption, or destruction of your data can disrupt your business operations and lead to loss of valuable information.
3. Data Privacy Violations: Non-compliance with data protection regulations (such as GDPR or CCPA) can result in hefty fines and loss of customer trust.
4. Data Quality Issues: Inaccurate, incomplete, or outdated data can lead to poor decision-making, inefficiencies, and customer dissatisfaction.
5. Insider Threats: Your employees or contractors with access to sensitive data can intentionally or unintentionally cause data breaches or leaks.
6. External Attacks: Cyberattacks such as phishing, ransomware, and malware can compromise your data integrity and availability.
7. Third-Party Risks: Your dependence on external vendors or partners for data processing or storage can introduce vulnerabilities if they’ve got inadequate security measures.
8. Legal and Compliance Issues: Failure to comply with industry-specific regulations and standards can result in legal penalties and business disruptions.
9. Data Accessibility: Ensuring that authorized users have timely and secure access to your data while preventing unauthorized access is a constant challenge.
10. Data Storage and Management: The increasing volume and complexity of your data requires special attention to ensure it’s stored securely and efficiently.

10 Data risk management strategies

Here are ten data-driven risk management strategies that you can use to protect your sensitive information and reduce the likelihood of a data breach:

1. Access Control: Set up strict access control policies so only authorized personnel can access your sensitive data. Use role-based access controls (RBAC) to limit access based on each user’s role and responsibilities. Segment your network to prevent a breach from spreading.
2. Encryption: Encrypt your sensitive data both at rest and in transit to protect it from unauthorized access. This is especially important for data that’s stored in the cloud or transmitted over the internet.
3. Regular Audits and Monitoring: Conduct regular audits of your data security policies and practices. Use data analytics in risk management to monitor and detect any unusual activities or potential breaches in real-time.
4. Data Backup and Recovery: Regularly backup your critical data and make sure you’ve got a comprehensive disaster recovery plan in place. This’ll help you quickly restore data in case of a breach.
5. Incident Response Plan: Develop and regularly update an incident response plan. This should include procedures for identifying, containing, and stopping data breaches, as well as notifying affected parties.
6. Employee Training: Regularly train your employees on data security best practices and the importance of protecting sensitive information. This can help prevent accidental leaks or breaches due to human error.
7. Vendor Risk Management: Assess the security practices of third-party vendors who’ve got access to your data. Make sure they adhere to your security standards to prevent data breaches through third-party systems.
8. Regular Software Updates: Keep all your software and systems up to date with the latest security patches. This helps protect against known vulnerabilities that could be exploited by attackers.
9. Data Retention Policies: Set up data retention policies to make sure data isn’t kept longer than necessary. This reduces the amount of data at risk and helps comply with data protection regulations.
10. Dark Web Monitoring: Use continuous dark web monitoring to scan for and identify any company or customer data that may have been leaked or is being sold on the dark web. This can provide early warning signs of a data breach and lets you proactively mitigate the risk before the data’s exploited.

Data Risk Management vs. Data Security

While often used interchangeably, data risk management and data security serve different but complementary purposes in protecting your information.

Data security focuses on setting up technical controls to protect data from unauthorized access. Think firewalls, encryption, access controls, and antivirus software. These’re the tools you deploy to create barriers around your data.

Data security risk management takes a broader approach. You’re looking at your entire risk landscape. You’re making strategic decisions about where to invest your security resources. You’re not just asking “How do we protect this data?” You’re asking “What happens if this protection fails?”

This strategic approach helps you prioritize which data needs the strongest protection. It shows you which risks pose the greatest threat to your business operations. Your customer payment data might need different protection levels than your internal training documents.

The key difference is perspective. Data security is tactical. You’re implementing specific controls. Data risk management is strategic. You’re understanding business impact and making informed decisions about risk tolerance and investment priorities.

Building a Data-Driven Risk Management Program

Modern data risk management relies heavily on data analytics and automation. You’re identifying, assessing, and responding to threats more effectively.

A data-driven approach means using actual metrics to guide your decisions. You’re not relying on assumptions or gut feelings. You’re collecting data about your security environment. You’re analyzing patterns and using those insights to improve your defenses.

Start by setting up baseline measurements for your current security posture. How long does it take to detect a breach? How many false positive alerts does your team handle daily? What’s your mean time to response for different incidents?

Next, set up continuous monitoring tools. These tools process large volumes of security data in real-time. They identify anomalies and correlate events across multiple sources. They provide early warning signs of potential threats.

Machine learning and AI play increasingly important roles here. These technologies analyze patterns in your data that humans might miss. They predict potential vulnerabilities. They automatically respond to certain types of threats.

The goal isn’t to replace human judgment. You’re augmenting it with better data and faster analysis. Your security team can focus on strategic decisions. Automated systems handle routine monitoring and initial threat triage.

Regular reporting and metrics help you demonstrate value to leadership. Show how your investments in monitoring and prevention translate to reduced incident costs. Show faster response times and improved security posture.

Advanced Data Risk Monitoring Techniques

Traditional security monitoring relies on signature-based detection and rule-driven alerts. Advanced data risk monitoring goes beyond these reactive approaches. You’re getting proactive threat identification.

Behavioral analytics monitor how users and systems normally behave. They flag unusual activities that could indicate compromised accounts or insider threats. If an employee normally accesses files during business hours but suddenly downloads sensitive data at 3 AM, that’s a red flag you need to investigate.

Threat hunting involves actively searching for signs of compromise in your environment. You’re not waiting for automated alerts. Your security team uses threat intelligence, known attack patterns, and anomaly detection. They’re looking for evidence of advanced persistent threats that might evade traditional security controls.

Integration with external threat intelligence feeds provides context about emerging threats. You’re learning about active threat actor campaigns and indicators of compromise specific to your industry. This helps you understand what’s happening in your environment. It shows how it connects to the broader threat landscape.

Dark web monitoring extends your visibility beyond your network perimeter. You’re identifying compromised credentials, leaked data, and discussion of planned attacks against your organization. This external perspective often provides the earliest warning signs of potential breaches.

Data analytics in risk management also includes predictive modeling. This identifies which systems or data sets are most likely to be targeted. It’s based on historical patterns, asset value, and current threat trends. This helps you prioritize security investments and focus monitoring efforts where they’ll have the greatest impact.

How Breachsense Helps Protect Data

Data breaches are arguably one of the biggest risks you face.

According to the IBM Cost of a Data Breach Report, the average cost of a data breach is USD 4.45 million.

The Verizon Data Breach Investigations Report found that 86% of all breaches involve stolen or weak passwords.

Exploiting leaked credentials and session tokens has become criminals’ preferred method for gaining initial access because it bypasses traditional security defenses and goes undetected.

Schedule a demo to learn how Breachsense gives you visibility into your leaked data so that you can act before criminals do, or assess your dark web exposure with our exposure scanner.