Big Data is big; it is a conglomeration of massive volumes of data from multiple disparate sources, devices, and applications. But this very nature makes it prone to security threats. The constant cycle of sending and receiving data is a window for attacks and breaches to occur. With malicious attacks becoming more sophisticated and hard-to-detect, it is necessary for security measures, tools, and compliances to evolve to keep pace. Companies which use big data must be able to handle and resolve such challenges every day. In such a scenario, it is imperative to analyze the areas which could pose a risk.
Five Data Security Risks
The top six security issues to watch out for when working with big data:
- Network security – When data passes through the network, it could be at risk. It is a good idea to track and monitor not only the end but also the origins of the data and the networks it passes through. Encryption and multi-stage verification is a good way to protect the data and keep it confidential.
- Data storage access – Data is power, as such, it must be protected by restricting access only to a chosen few who can be trusted. It is a good idea to secure it with stringent measures and tools. Apart from this, data should be regularly monitored during transfers and storage to check for breaches and prevent people from sneaking into the network. Large amounts of data received should also be validated.
- Metadata access – A company can lose its business if its metadata is compromised. The metadata holds the key to all the company’s insider data; as such it is necessary to introduce access control based on confidentiality to only a few trusted resources to protect critical information.
- Distributed data frameworks – If there is only a single level protection for data spread across multiple frameworks, it is a recipe for a security disaster. While distributed data moves more efficiently, multiple entry points make the data vulnerable. The networks themselves may fall prey to an attack or the server may get locked in case of an issue.
- Non-relational Data storage – Non-relational databases and storages like NoSQL come with their own set of problems. Their constant evolution makes it difficult for security measures to keep pace. Moreover, they rely on a simple middleware to keep the data secure which is not strong enough to protect the network from server breaches.
- Data Mining – While data mining is a useful way to gain relevant insights and information on trends and customers, there could be unethical practices whereby people can gather and use personal data without taking permission or notifying the person.
Four Tips to Protect Data
- Invest in a good antivirus to defend against external and internal threats
- The focus should be on securing applications rather than devices
- Access to devices and applications storing critical data should be restricted and monitored
- Implement an effective security information management system
- Maximize protection by early risk detection, mitigation and take proactive as well as reactive action
In conclusion, it is a good idea to understand and analyze your risks so that you can face them better and protect your data. Constant vigil and monitoring can help prevent or resolve the worst attacks in case they occur and help protect against future ones.
We would love to hear what you are doing to protect the data in your organization. Leave us a comment.