The constantly accelerating pace of data generation creates challenges and opportunities when it comes to harnessing Big Data. Data security is more important than ever as data collection, storage and distribution gets more and more decentralized. With mobile devices, cloud-based applications and laptops in the mix, developing stronger security intelligence and risk management is crucial.
To create an effective intrusion-prevention system that provides the best possible data security, look for products that feature multiple controls for lockdown. When evaluating your options, consider products that do these three things:
1. Aggregate all data into one place
Security controls at the device level are important, but if that’s where a security plan ends, it’s like locking a window but leaving all the doors open. Firewalls are no longer enough, and piecemeal approaches can fail. That’s why you have to see security in a new way, and that means handling Big Data with an aggregated approach. Consider products that:
- Collect information from every device into a single place; this includes security devices, applications, servers, laptops, desktops, cloud-based solutions, and network devices.
- Take into account Software-as-a-Service (SaaS) applications that might be deployed in a cloud environment. The logs from these applications often contain valuable information and can let you know who’s accessing your data.
- Learn the five stages of data theft and how hackers think, by downloading the white paper Big Security for Big Data.
2. Integrate data through automation
Once you have all the data in one place, you need to be able to examine it intelligently in order to spot usage patterns that might be a sign of intrusion. Choose products that let you:
- Use automation and rule-based processing to streamline your security efforts. Because of the volume and speed of data generation, manual review of logs would take an inordinate amount of time and expertise.
- Normalize machine logs. This allows you to create queries that can draw context-aware info from the logs. Standardization and normalization should include the development of meta fields that make the process even faster, and will allow you to index and search information quickly, leading to a better intrusion prevention system.
3. Use data analytics to identify patterns.
Once your data is aggregated, categorized and indexed, you can begin to use data analytics in order to see usage patterns that can tip you off about security breaches. You should make sure you’re employing products that allow you to:
- Put security rules and heuristics in place so you can spot patterns easily.
- Combine logs from several sources and correlate events, which will help increase the effectiveness of alerts.
- Use simple queries and filters for logs in order to create reports that are more meaningful.
- Respond quickly if any malicious activity is detected. Make sure your team is well trained for handling a security event in case a pattern is detected and malicious activity is found.
- Put software and hardware controls in place ahead of time so that you can reduce your response time. Make sure those threat-detection systems include automatic documentation that you can use in audits or future security planning.
By taking a proactive approach to data security, you can develop a security intelligence and risk management system that keeps all your data locked down safely. Detecting system intruders as they try to attack is far more effective than attempting to go after hackers once they’re inside your organization’s virtual walls.
Get more tips for boosting your security measures in the white paper, Big Security for Big Data.