All new technologies strive to ease the burden of complex business problems, which is why you need to start looking at how big data analysis helps your compliance efforts. Increasingly, cyber security compliance requires companies to analyze the impact of diverse information inputs and streams. Thus, big data analytics provide important compliance insights that enable your organization to streamline its efforts and increase profitability.

Using Big Data to Drive Your Business & Compliance Program

What is Big Data?

Big data means large sets of information that computers can analyze mathematically to show patterns, trends, and associations. Traditionally, big data incorporates the three V’s of volume, velocity, and variety:

• ​Volume: Not just the amount of data but the number of places from which you collect data (e.g., business transactions, social media, sensors).
• ​Velocity: The speed of collection to ensure timely review (e.g., real-time data from sensors and RFID tags).
• ​Variety: The data’s format, which can be numeric, text-based, visual, audio, structured, or unstructured.

What is the “variability” problem?

Variability means that data can change over the course of processing. While related to variety, variability refers to inconsistencies in the data arising from disparate types and sources. For example, the data point “ocean” can be represented as text (e.g., "Atlantic," "waves") or as images. The text “wave” might mean a hand gesture or a water wave, leading to outliers and complexity in aggregation. The greater the variability, the greater the complexity, making analysis of different data types difficult.

What are structured and unstructured data?

The primary computational problems with big data lie in the difference between structured and unstructured data.

Structured Data

Structured data is information easily displayed in tables for ordering and processing, such as spreadsheets. You can manipulate columns to create various views into the data.

Unstructured Data

Most information collected is not easily organized into tables. Unstructured data includes text, images, or binary programming that makes numerical organizing difficult. Sometimes, data is a combination of both structured and unstructured. For example, emails have structured information (to, from, subject lines) and unstructured text in the message body.

How do you analyze big data?

Protecting your data environment requires the volume, velocity, and variety that big data brings. More information stored in more locations using more vendors increases the number of attack vectors, making protection more difficult. Malicious attacker methodologies continuously evolve, requiring insights that match their speed. Big data collects the information, but analytics provide the insights necessary for better business decisions.

Predictive Analytics

Predictive analytics uses modeling, machine learning, and data mining to use historical data to predict future events. These statistical modeling methodologies allow you to use overwhelming real-time data collection effectively. For example, cutting-edge anti-virus protection uses machine learning and big data to predict the next ransomware attacks rather than only protecting against already known ransomware.

Prescriptive Analytics

Prescriptive analytics takes data and helps model best decisions. Rather than simply guessing what might happen to your organization, prescriptive analytics enables you to take actions that can protect your organization. For example, big data can collect information about attempted intrusions, and prescriptive analytics models can help you decide which ones to prioritize.

How you can enable information security with big data and machine learning analytics

Once you collect all the data, you need to use statistical methodologies to make that information useful. By bringing together predictive and prescriptive analytics, you can use collected big data to protect your environment and monitor control effectiveness. New technologies, like security ratings, continuously collect, aggregate, and analyze publicly available data to provide insight into your control effectiveness and that of your vendors.

Machine learning algorithms for threat detection take information from across the internet and combine it. While you may monitor your environment yourself, big data analytics solutions compare millions of monitored environments to determine normal versus abnormal network and system activities. By aggregating this information, these solutions show you attacks against other environments to help you prepare and protect against threats that have not yet happened to you.

How big data and machine learning enable compliance and better business decisions

Starting with a strong security stance allows you to build a strong compliance program. With a “Security First” approach to compliance, you can focus your program to align across multiple frameworks. Determining your control effectiveness as part of your governance program allows you to ensure ongoing compliance. For example, a primary compliance directive across standards and regulations includes ensuring that you patch systems and networks with the most recent software updates. If your continuous monitoring analytics show a weakness in your software updates, then you can better prove compliance.

How ZenGRC Works Similar to Big Data Analytics

With ZenGRC’s System of Record, you can aggregate your compliance documentation and align it across multiple standards to enable better compliance insights.

Dashboards help you track the completion status of your InfoSec compliance programs and prioritize your efforts when new requirements or frameworks are added. Just as big data collects information across the internet, ZenGRC collects information across your enterprise. In the way that big data predictive analytics can enable better macro insights, ZenGRC enables better organizational insights.

With ZenGRC you can leverage work across compliance initiatives, test controls once, and use evidence multiple times. You can also run audits and automate routine compliance tasks using ZenGRC. The same way that big data collection finds patterns across information on the internet, ZenGRC enables pattern finding within your compliance program to make the process more efficient.