In many areas of data management, an assumption exists that the data being leveraged for analysis and planning has been well vetted and secure.
Many organizations assume that the data they use has been through a data-cleansing and profiling process so that the data can be trusted. The professionals working with big data assume that they do not have to worry about security or governance.
Here is an eye-opener!!
The current world of big data offers a new set of concerns that make security and governance a MUST.
In this blog, we present the topics that you need to think about and plan for when you begin to leverage big data sources as part of your analysis and planning process.
Often big data analysis is conducted with a vast array of data sources that might come from many unvetted sources. Once you acquire the data, you will subject your company to compliance issues if it is not managed securely. The data that remains after disposing unwanted data, will need to be secured and governed. Therefore, whatever your information management strategy is, you will have to have a well-defined security strategy.
Some experts believe that different kinds of data require different forms of protection and that, in some cases in a cloud environment, data encryption might, in fact, be overkill. You could encrypt everything. You could encrypt data, for example, when you write it to your own hard drive, when you send it to a cloud provider, and when you store it in a cloud provider’s database. Encrypting everything in a comprehensive way reduces your exposure; however, on the flipside, it poses a performance penalty.
When data is anonymized, you remove all data that can be uniquely tied to an individual (such as a person’s name, Aadhaar number, or credit card number)
This technique protects sensitive data by replacing it with random tokens or alias values that mean nothing to someone who gains unauthorized access to this data
In this technique, access controls are built into the database to protect the whole database so that each piece of data doesn’t need to be encrypted
What issues should you consider when you incorporate these unvetted sources into your environment? Consider the following:
1) Determine beforehand who is allowed to access new data sources initially as well as after the data has been analysed and understood.
2) Understand how this data will be segregated from other companies’ data.
3) Understand what your responsibility when leveraging the data. If the data is privately owned, you must make sure that you are adhering to contracts or rules of use. Some data may be linked to a usage contract with a vendor.
4) Understand where your data will be physically located. You may include data that is linked to customers or prospects in specific countries that have strict privacy requirements. You need to be aware of the details of these sources to avoid violating regulations.
5) Understand how your data needs to be treated if it is physically moved from one location to another. Are you going to store some of this data with a cloud provider? What type of promises will that provider offer in terms of where the data will be stored, and how well it will be secured?
The way that an organization deals with big data is an ongoing cycle and not a one-time project. The potential for causing risk to the business can be serious if consistent rules and processes are not applied consistently. Data quality should also be approached from a governance standpoint. When you think about policy, here are some of the key elements that need to be codified to protect your organization:
1) Determine best practices that your peers have implemented to have consistent polices documented so that everyone has the same understanding of what is required.
2) Compare your policies with the governance requirements for your own business and your industry. Update your policies if you find oversights.
3) Do you have a policy about the length of time that you must hold on to information? Do these policies apply to the data you are collecting from external sources, such as customer discussion groups and social media sites?
4) What is the importance of the data sources that you are bringing into the business? Do you have quality standards in place so that a set of data is only used for decision making if it is proven to be clean and well documented? It is easy to get caught up in the excitement of leveraging big data to conduct the type of analysis that was never achievable before. But if that analysis leads to incorrect conclusions, your business will be at risk. Even data coming from sensors could be impacted by extraneous data that will cause an organization to come to the wrong conclusion.
5) In a big data environment, security starts with assessing your current state. A great place to begin is by answering a set of questions that can help you form your approach to your data security strategy. Here are a few important questions to consider:
6) Have you evaluated your own traditional data security approach?
7) How do you control access rights to the data in your applications, your databases, and your warehouse both those within your company and those from third-party sources? Who has the right to access existing data resources as well as the new big data sources you are introducing? How do you ensure that only the right identities gain access to your applications and information?
8) Can you identify data vulnerabilities and risks and then correct any weaknesses?
9) Do you have a way of tracking your security risk over time so that you can easily share updated information with those who need it?
10) Is your overall infrastructure protected at all times from external security threats? If not, this could be the weak link that could seriously impact the security of your data.
11) Do you maintain your own keys if you are using encryption, or do you get them from a trusted, reliable provider? Do you use standard algorithms? Have you applied this standard to new data sources that you have determined are critical to your business?
12) Are you able to monitor and quantify security risks in real time?
13) Can you implement security and governance policies consistently across all types of data sources, including ones that reside in a cloud environment?
14) Can you protect all your data no matter where it’s stored?
15) Can you satisfy auditing and reporting requirements for data wherever it resides?
16) Can you meet the compliance requirements of your industry?
17) What are your disaster and recovery plans? How do you ensure service continuity for all your critical data sources?
Most companies dealing with Big Data also has special protocols to implement security measure to mitigate the security risk. Much like other types of cyber-security concerns, big data may also be infected or attacked from different sources. The diversity and heterogeneity of data increase the risk of malicious entry in big data. Big data security includes the precautionary tools that ensure the safety of big data from theft, attack, or harms which negatively affect it. Big data security is an umbrella term that includes all security measures and tools applied to analytics and data processes. Data breaches, data brokerage, and data discrimination can occur if big data privacy isn’t taken seriously. For this reason, data governance and integration are vital for proper compliance and privacy management. If your organization is searching for a big data privacy solution, Go4hosting collects, governs, transforms, and shares data with internal stakeholders while ensuring data privacy. Explore us to reduce the risk of privacy issues often associated with big data and ensure your company has data it can trust. For further queries and consultation, please get in touch with us on https://go4hosting.com or email us on email@example.com. Our team of subject matter experts will assist you achieve your targets in the most customized and optimal manner. We are looking forward to working with you.