Using Data to Assess Risk

Following a data-driven approach is the best way of running a successful business. Data collection and analysis applies to many different areas- from understanding customer trends to reducing operational costs. But data also applies to risk management. By using analytics to assess business risk, you can develop more secure systems that protect against cyber-attacks.

Risk assessment is a critical part of implementing a data security plan. However, many companies assess risk by merely referring to previous experiences or industry trends. This approach is less specific and may result in higher costs, non-compliance with established regulations, and vulnerability to potential data breaches.

Luckily, following a data-driven approach will help your business protect customer data and assess the effectiveness of current controls. Risk assessment via data analytics will also result in the implementation of better risk management strategies.

But how can you begin to use data when assessing business risk? The following steps will help you get started.

1. Determine the potential risks that your business faces

Any risk assessment process begins with determining the specific risks that your company might face. Defining the scope of risks will help you follow a more targeted approach to risk management. Many businesses use regulatory requirements as a guide for setting the scope of risk analysis. You may also refer to the goals and objectives of your business to help guide this decision.

For example, if you're handling healthcare-related information, HIPAA standards will apply to your company's risk assessment plan. You may then choose to expand your scope based on the internal objectives that your company has set.

When setting a scope for your risk assessment plan, determine the type of data that will be affected, where it's stored, who has access, and how it's handled. Make sure you approach each data category individually- so that you can focus enough attention and resources to any potential risks.

2. Identify your key risk indicators

Similar to how KPIs help you determine performance levels, KRIs (Key Risk Indicators) assist in evaluating how specific risks can occur. In other words, KRIs point to the areas that you'll need to focus on when analyzing data for risk assessment. They help you follow a more targeted approach that applies to compliance requirements and your overall business goals.

Identifying KRIs begins during the data analysis phase. This phase involves outlining all associated data elements- so that specific data flows can be mapped out according to the needs of your business. For example, data analysis will involve categorizing the types of data that you handle (which may include PHI- personal health information, credit card numbers, names, and addresses, among others). After categorization, relevant KRIs can be identified from your company's assessment matrix, systems, and workflows.

3. Classify data accordingly

After categorizing your data and developing KRIs, the next step is to classify this data according to its level of sensitivity. Data classification helps you determine the level of impact that a potential breach might cause, and to align your data analysis accordingly.

Some of the most common categories that are used for data classification include public data, private data, and restricted data. Public data is information that is available to the general public -including first and last names, social media handles, press releases, etc. Private data is your more secure information- such as business purchase orders or company presentations.

Finally, restricted data is information that is only available to specific people. This includes social security numbers, addresses, bank account numbers, etc. Unauthorized access to restricted data will often result in a significant level of risk.

4. Carrying out the actual risk assessment

With the previous steps in place, you may now proceed to carry out the actual data-driven risk assessment. This process involves developing algorithms and using tools that will sift through data based on your established parameters.

As the data is being automatically scanned, all relevant discoveries will be identified for use in your final risk report. For example, your scans may uncover that PCI data isn't in its correct place, or unauthorized parties within the business are accessing personal health information.

When developing an algorithm for data scans and analysis, make sure that it has the following essential elements:

•  Automation to allow for large volume analyses

•  Artificial intelligence that uses past events to prevent future outcomes

•  IoT capabilities so that multiple devices can be assessed

5. Preparing a risk report

Once you've identified where your sensitive data lies and pinpointed all potential security issues, you can proceed to prepare a report that outlines your findings. But a risk assessment report goes further than just listing out data analysis results, security gaps, and policy violations. The report should also provide recommendations that will assist with overall risk management.

Some of these recommendations may include proposals for handling data, encrypting sensitive information, and installing protective software.