How to handle the security problem with big data

Big data can be a massive opportunity for organisations, but storing that data securely can be a obstacle for enterprises who may know that they have an obligation to customers to keep their data safe and protected from a data breach, but may not know exactly what steps they should be taking.

When producing information for big data, organisations have to make sure that they have the right balance between privacy and usefulness of the data. This follows a process of anonymising the data, encrypting it, putting proper access control in place with security monitoring, risk assessment and making sure storage complies with local regulations.

Anonymise

Before the data is stored, it should be sufficiently anonymised, which involves removing any unique identifiers for a user attached to that piece of data. This in itself can be a security challenge, as removing unique identifiers might still not be enough to guarantee that the data will remain anonymous. The anonymised data could be cross-referenced with other available data following de-anonymisation techniques. Therefore, it should also be encrypted.

 

Encrypt

Both the raw data and the outcome from analytics should be sufficiently protected with encryption, a universally accepted standard of securing data. However, in the case of cloud services, data cannot be sent encrypted by the users if the cloud needs to perform operations over the data. A solution for this is to use “Fully Homomorphic Encryption” (FHE), which allows data stored in the cloud to perform operations over the encrypted data so that new encrypted data will be created.

 

In addition, protect communications: data in transit should be adequately protected to ensure its confidentiality and integrity.

 

Access Control and Security Monitoring

Satisfactory access control mechanisms will also be key in protecting the data. Access control has traditionally been provided by operating systems or applications restricting access to the information, which typically exposes all the information if the system or application is hacked. A better approach is to protect the information using encryption that only allows decryption if the person trying to access the information is authorised by an access control policy.

 

One problem that may need to be overcome is that software commonly used to store big data, such as Hadoop, doesn’t always come with user authentication by default. This makes the issue of access control trickier, as a default installation would leave the information open to unauthenticated users. By using real-time security monitoring, access to the data is monitored and threat intelligence applied in order to prevent unauthorised access to the data.

 

Risk Assessment and Compliance

Organisations should run a risk assessment over collected data and consider if they collect customer information that should be kept private to establish adequate policies that protect the data and clients' right to privacy. They should also carefully account for regional laws around handling customer data, such as the EU Data Directive.

 

If the data is shared with other organisations, then it should be considered how this is done. Deliberately released data that turns out to infringe on privacy can have a huge impact on an organisation from a reputational and economic point of view. Anyone using third party cloud providers to store or process data needs to ensure that providers comply with regulations.

 

The main challenge introduced by big data is how to identify sensitive pieces of information that are stored within the unstructured data set, so it is crucial to bear in mind that security is a process, not a product. Therefore, organisations using big data will need to introduce adequate processes and apply traditional information lifecycle management that helps them balance effectively managing and protecting the data, as well as their customers' privacy.