5 Tips to Overcome Big Data Security Issues

23Mar - by aiuniverse - 0 - In Big Data

Source – insidebigdata.com

Businesses are now collecting and using a huge amount of data. Much of this flows from an increasing range of smart devices, all interconnected as the IoT (Internet of Things). Our computer capacity continues to grow rapidly, but there is still concern over security issues that can compromise even locally generated information. In business, highly sensitive information is stored, and it’s necessary to observe government regulations to protect consumers. At the same time, incidents of data breaches continue to rise. This is why it’s essential to make data protection a priority and establish strict security measures.

Here are five tips that can help you guard data against breaches in both big data deployments and any software accessing the data.

Secure Data Storage

Managing storage is a critical part of any data strategy. Auto-tiering is necessary when you’re looking at petabytes of information. New data is automatically assigned to different storage levels to make managing huge volumes of data simpler. However, this can create other problems due to issues like unverified services or contradictory protocols. Auto-tiering also generates logs of its storage activities which also have to be protected and maintained. SUNDR (secure untrusted data repository) helps with this by monitoring for and detecting unauthorized file operations. These can come from malicious software agents. SUNDR utilizes consistency checks to ensure data is stored securely.

Secure Non-Relational Data

Many organizations handle a large volume of unstructured data such as images. They turn from standard SQL relational databases to NoSQL deployments. These solutions are becoming more common but are still vulnerable to injection attacks where malignant code is inserted. Recommended security measures include hashing or encrypting passwords. You should also use effective end-to-end encryption algorithms such as RSA, AES, and SHA-256, as well as SSL encryption.

Ensure Endpoint Security

Trusted certificates at each endpoint will help to ensure that your data remains secured. Additional measures that your organization should use include regular resource testing and allowing only trusted devices to connect to your network through the use of am MDM (mobile device management) platform.

One challenge lies in ensuring that all data is valid, given the wide scope of devices and data collection technologies. Many input devices and applications are vulnerable to hackers and malware. Intruders can mimic multiple logon IDs or corrupt the system with false data. Your big data solution should be capable of both preventing intrusion and identifying false data.

Prevent Inside Threats

Your company is also exposed to internal security risks, whether from disgruntled or simply careless employees. This is especially challenging in business environments where employees working with the data are not fully educated on proper security practices and behavior, including data scientists and software developers.

It’s important that you provide digital security training to all employees. They should know about password safety, logging off unused computers, granting permissions to other employees and risks of accessing data via public Wi-Fi.

Your company should also have user logs in place to help identify workers who might attempt to steal intellectual property, use stolen logon credentials, or otherwise try to compromise or bypass network security protocols.

Analyze and Monitor

A big data solution that includes tools for both analysis and monitoring in real time can raise alerts the instant network intrusion is detected. But this can result in large amounts of network data. Your goal is to provide an overall picture of what’s happening over sometimes large networks from moment to moment. Your organization may not have the resources to monitor and analyze all the feedback generated, including false alarms as well as real threats.

The solution is that big data analytics itself can be used to improve network protection. Your security logs can be mined for anomalous network connections. This will make it easier for you to identify actual attacks as opposed to false positives going forward.

Final Thoughts

Automated data collection is increasing the exposure of companies to data loss. When considering a big data solution, you can best mitigate the risks through strategies such as employee training and varied encryption techniques. But it’s also crucial to look for solutions where real security data can be analyzed to drive improvements.

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