6 Tips to Create a Comprehensive Big Data Security Policy

Nimrod Iny
|
Dec 5, 2022

We create 2.5 quintillion bytes every day. 90% of data in the world today was created in the last two years. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few.

This deluge of data is called Big Data.

What is big data security?

As our world becomes increasingly digitized, so too does the need to protect the data we create.

This is where big data security policy comes in.

Big data security policy is a set of guidelines and best practices for securing the vast amounts of data generated by organizations and individuals. It also covers various topics, from data governance and risk management to data security and privacy.

Why do you need a big data security policy?

Big data security is a multi-faceted challenge for companies that operate in the cloud. This challenging threat includes the theft of information stored online, ransomware, or DDoS attack.

Theft of information is a big problem for companies because it can lead to the loss of proprietary information, customer data, or other essential data. Ransomware is another big challenge for companies because it can encrypt data and make it unavailable to the company or customers. Global ransomware attacks worldwide have increased from 55.1% in 2018 to 71% in 2022.

No one is truly safe from big data leaks, which happen more often than we realize.

In 2021, 533 million Facebook users' phone numbers and personal data was leaked online. In 2020, 250 million Microsoft customer records were exposed online. In 2019, over 275 million records containing sensitive, personally identifiable information of Indian citizens were left exposed on the web for over two weeks. In 2018, Cathay Pacific Airways stocks plummeted after a massive data breach revealed that 9.5 million passenger details were left vulnerable after a hack.

Organizations must balance security and privacy when creating a big data security policy. Too much security can impede business operations, while too little security can leave an organization vulnerable to attack. The goal is to find the sweet spot that provides adequate security without sacrificing privacy or business productivity.

6 Tips to Create a Comprehensive Big Data Security Policy

Big data security policy is critical to any organization’s overall security posture. With the right policies in place, organizations can protect their data from unauthorized access and use while still allowing legitimate users to work with the data they need.

Here are six tips for creating a comprehensive big data security policy.

1. Create an introduction, scope, and definitions

With the rise of big data comes new security challenges. Big data security is the process of securing large data sets and protecting them from unauthorized access or theft. Organizations need to have a comprehensive security policy to properly secure big data.

While it's not hard to create a policy, creating an effective policy requires a clear introduction, scope, and definitions. Why? Because it provides context and ensures that everyone is on the same page in terms of terminology. The introduction sets the stage for the rest of the document; the scope outlines what the policy covers and does not, and the definitions section clarifies key concepts and terms used throughout the policy.

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2. Take inventory of sensitive data

We know that there is sensitive data collected - but does your organization know what kind, the scope, and its current security levels?

Shadow data is an issue that many organizations face because it increases the scope and spread of sensitive data. This makes taking inventory of sensitive big data a much harder task. Tools offered by Polar Security can help the discovery process and provide actionable insights for continuous security and compliance.

Manual review of data sources can be tedious and time-consuming and has the potential to miss data stores containing sensitive data. Discovery and classification tools can automatically scan your data and provide a bird's eye view map based on your organization's cloud infrastructure connections. Data loss prevention tools can also monitor your data and identify sensitive information at risk of being leaked.

3. Use encryption

In big data security, encryption protects data in transit and at rest. Data in transit is protected by using SSL/TLS encryption, which encrypts data as it's sent between two systems. Data at rest is protected by using file-level or disk-level encryption, which encrypts data as it is being stored on a disk or other storage device. Encryption is a key part of a big data security policy because it helps to ensure that data is not accessible to unauthorized users.

Encryption is a process of transforming readable data into an unreadable format. This is done using a key, which is a piece of information that controls the transformation. This can be used to protect data in big data security scenarios by ensuring that only authorized users can access the data. Encryption can also be used to ensure that data is not tampered with while in transit.

4. Intrusion detection and prevention

Intrusion detection and prevention work by monitoring and analyzing the data passing through a network for suspicious activity. If suspicious activity is detected, the system can take action to prevent the intrusion, such as blocking the offending IP address or disconnecting the offending user.

This process can be incorporated into a big data security policy by setting up alerts to notify the security team of suspicious activity and by configuring the system to take action to prevent intrusions.

Polar detects shadow data and sensitive data flows for Ocrolus

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See how Ocrolus discovered 1,389 shadow data stores within its cloud environment in less than 5 minutes

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5. Centralize key management

Different layers of your application and organization will require different levels of permission. Secrets sprawl happens when there are too many keys in rotation and not enough of the right people or applications to access them. This can lead to long-term security vulnerabilities and compliance and operational issues.

More than 6 million passwords, API keys, and other sensitive data was leaked in 2021 due to secrets sprawl or improper management of keys - double the amount from 2020. Leaked keys are rampant, and having a centralized key management system can ensure that your organization is always in control of accessibility.

But before you can incorporate a centralized key management system into your big data security policy, you need to know where your current keys are stored, where they are used, who has access to them, and what kind of data they are protect.

6. Secure the network with a virtual private network

7 out of 10 cloud infrastructures were breached in 2020. A VPN or Virtual Private Network can help keep sensitive data from the public domain and ensure that only authorized applications and people have access to it.

Most organizations now recognize the need for a comprehensive security strategy that includes data protection. But as data has become more distributed and mobile, it has become more difficult to protect. A VPN can help provide the security needed for data in transit, whether across the Internet or within a private network.

A virtual private network can provide several layers of security for data, including encryption and a secure tunnel for data, making it more difficult for data to be intercepted in transit.

Secure your big data platform from threats with Polar Security

Big data is here to stay, and your organization needs to be able to automate processes to implement your big data security policies.

Many organizations overlook one important fact - you cannot protect what you can't see. This is where Polar Security comes in.

Polar has developed an agentless, non-intrusive platform to help you automate your sensitive data discovery, mapping and classification. The platform works autonomously to map all your cloud data, classify it and track anomalous data movements. Figure out where your sensitive data is hiding within your infrastructure so you can effectively implement your newly minted big data security policy with minimal hassle for your teams.

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