7 Big Data Security Changes You Need to Know
Data is a crucial asset for every organization right now. They have put in different infrastructures and measures to ensure they get the best data for company analysis and insights for easier decision-making. You must safeguard your big data from security risks like cyber threats, theft, unauthorized access, etc. Proper big data security will save your company from losing finances, getting a bad reputation, losing customer trust and paying fines to regulatory bodies.
Big data normally consists of unfiltered, structured, unstructured, and real-time data. The main sources include databases, mobile applications, e-commerce, SAAS businesses and platforms, CRM software, machine learning, Internet of Things devices, etc. Proper big data security ensures data integrity, availability and confidentiality. Companies must adapt to some of the big data security changes and trends to ensure that big data is safe. The guide will discuss some of the latest big data security changes organizations must implement.
7 Big Data Security Changes You Need to Know
The common data security changes happening right now result from different technological changes in the data industry. The changes involve how teams handle data, software they can use and how to implement it. Some of the expected changes include:
1. Data compliance
Several data regulation bodies ensure businesses have better data governance every time they deal with big data. Failing to comply with the regulations, you pay hefty fines. Most organizations deal with the movement of data from one location to another, which can be a big huddle sometimes.
Some common data compliance regulation and governance organizations include The European Data Protection Board (EDPB), General Data Protection Regulation (GDPR), etc., each having different rules, sets of data they deal with and fines.
Organizations should implement different data integration methods, especially during data transfer. The common integration tools that eliminate risks and improve data security include Talend, Boomi, Oracle, Jitterbit, Tibco, Mulesoft, etc.
2. Data Access
Data access provides the ability to copy, move, insert, retrieve, delete, and transfer any data with the systems. It is one of the easiest ways of achieving data governance. Data access enables users to get data despite the location. Having proper data access provides a high level of security protecting data.
Common types of data access include:
a. Random access – It enables users to get any data from any location in the storage drives. It is the easiest way of retrieving data.
b. Sequential access – It uses different methods like seeking methods to a drive until you get the right piece of data.
Implement the principle of least privilege. It uses different codes to provide permission to who can access specific data. It limits the number of people who can access the company data promoting big data security. Implement data access rules on all data warehouses, data centers and any related places where companies store data.
3. Data categorization
Data categorization is a type of data classification that helps you categorize data using different standards. The commonly used measures include using public, confidential and internal. It will limit access and reduce the chances of data breaches, making it easier for regulation and compliance.
This will allow companies to categorize data according to value, timeframe, rights, location and pertaining employees. It makes having proper security for all the big data you deal with easier.
4. Data encryption
Data encryption involves using different encryption methods. Your data must be well encrypted, especially when you want to meet the data governance regulations set up by bodies, i.e., GDPR rules and regulations.
Data encryption guarantees better data security, as cybercriminals will have difficulty accessing your data. To protect sensitive data from cyber threats, i.e. malware, viruses, Trojans, etc., encrypt all the data, emails, files and devices and ensure there is no data loss.
Commonly used types of encryption include:
a. Using Symmetric-Encryption –This type of encryption technique requires one key to use when encrypting and decrypting any file. It is very fast compared to asymmetric encryption.
b. Using Asymmetric Encryption – It requires two different keys like private and public. Everyone can access public keys, while the private key is kept confidential. At present, SSL certificate uses asymmetric encryption method to secure website.
For data encryption, it is necessary to buy and install an SSL certificate from a reliable SSL provider like Comodo SSL certificate or any other. It uses robust encryption algorithms to protect any data in moving between the users and the server. It makes it hard to read any data that is in transit.
Lacking an SSL certificate provides hackers a chance to access your data in transit. Encryption also reduces the risks of data breaches or any other security challenges the company may face. It promotes data confidentiality by ensuring it is safe from any threats.
5. Authentication
It involves companies understanding the data source and its integrity. It is also good for all employees to use two-factor authentication for additional security. The best way to confirm this is by inputting measures to get better data entities and having validation measures for data integrity.
Organizations have two mechanisms for implementing data authentication:
a. Data authentication algorithms – It involves using message authentication codes and key hash message authentication algorithms to detect errors during data transmission.
b. Public keys cryptography – This method is very effective since it works well by producing digital signatures. Most digital signatures use the hash to ensure better data movement security.
6. Data Monitoring
It involves using different tools and software to get more insights into your data usage and access. Most of the tools work in real-time, making it easier to comply with all regulations and protect sensitive data. The software captures all the miner details from queries and user-level activities. It makes it easier to identify issues, get alerts and block any queries that may hurt your big data.
It also promotes big data security by providing more details about your database, from access privileges, roles, and other related activities. For companies deploying using the cloud might face challenges like choosing the right monitoring software. You must ensure that it promotes data governance and compliance by following the rules and laws.
To make data monitoring easier, companies must set up teams to ensure everything runs perfectly. They will get alerts, create reports for all activities, provide security audits, block any malicious activities, update privileges and provide better oversight.
To have big data security, companies must first ensure there is a better data ingestion, check the issues and fix them, and have better or clean data by removing any duplicates, replacing old data, etc.
Common data monitoring tools include datadog, checkmk, Zabbix, ManageEngine, dyne-trace, etc.
7. Data Masking
It involves the use of fake data to protect your real sensitive data. It involves using different processes to change different data values according to the same format of your real data. This protects companies against threats like data exfiltration and loss, it also reduces risks related to cloud security, in case of an attack, the data becomes useless for the hackers, it makes it easier to share your data, etc.
There are scenarios where you will have to use masking algorithms. You must ensure that fewer people can access the algorithms and real data. Most algorithms are susceptible, and you must learn how to handle that.
To achieve big data security using data masking, conduct proper research to understand which data you need to be protected, categorize your sensitive data, ensure integrity, and use the masking type you feel will be easier for your organization.
There are different types of data masking that you can apply to your organization to promote big data security. The common types are as follows:
a. Static data masking – It involves protecting your database keeping another copy of the database in a different environment, production and deleting any unnecessary data.
b. Deterministic Data Masking – It involves changing two sets of data using the same way such that one value of data is usually replaced by the other. For example, John Evans is replaced by Johnis Evanson every time.
c. On-the-Fly Data Masking – It involves transferring data from production to the test environments without saving them to the disk. It makes it easier for organizations that deal with many data deployments not to save the data on disk.
d. Dynamic Data Masking – It happens when one consumes data from the test environment directly from the production environment.
Organizations can implement data masking using the following techniques:
a. Implementing data scrambling – it involves changing the characters and putting them in random order, for example, if the user id in the database is 120003, you can replace it using 34576 in the testing database.
b. Implementing null values when an unauthorized user tries accessing some real data of your big data infrastructure.
c. Data ageing – It allows changing data that is related to date fields. You can change the dates by adding 1,000 and getting another date.
d. Data shuffling – It involves shifting different data values using different random sequences. For example, if you deal with varying customer names, you can interchange them across different database records.
e. Using functions to replace any values that exist on your platform. For example, if you have an original value you get by subtracting the total salary from the basic salary, you can replace it using a function for the same task.
f. Data substitution – Replacing all the real values with fake values randomly.
g. Using Pseudonymisation – It ensures you cannot use real-time data for identification, meaning you remove any identifications but instead use other alternative methods.
Big Data Security Issues
Companies must protect themselves from different big data security issues to prevent further damage. Some of the common ones include:
1. Social engineering. It involves using different hacking techniques, i.e., phishing, identity theft, malware, viruses, and Trojans, to access sensitive information, which they can use later to conduct attacks on different organizations resulting in huge losses.
2. Insecure networks – Some businesses use insecure networks, which hackers use to find any vulnerabilities and have access to your system. It gives them access to all the big data, and hackers can use it how they like causing the company big issues.
3. Use of fake data – Cybercriminals can use fake data to change how your system works. For example, if you own an e-commerce store, they will do it with fake reviews and products that will affect your results and future projections during the analysis.
4. Physical theft – Several companies have their own data centers to store their big data. Ensure there is enough physical security, proper diligence and less access to the center. Some hackers can send someone to access your data center and steal large volumes of data, hurting organizations.
5. Human errors – Some employees may lack enough knowledge about cybersecurity and become cyber threat victims, i.e., by clicking phishing links. It provides hackers access to company big data, and you will become a data breach victim due to someone’s mistake.
Benefits of having proper data security
There are several benefits organizations, or businesses get from having better data security in place. The common ones include:
a. Getting more customers. A company that analyses its big data well and understands it will use the patterns to ensure the customer experience is good, making them return and refer other customers to your business.
b. It provides more space for innovation. Bid data security allows organizations to ensure everything is well set up and there are secure systems. It gives the company more time to focus on other aspects, i.e., processes, marketing, development and customer service, which increases productivity and innovation.
c. Risk mitigation. It helps you identify any risks in your data patterns, i.e., fake data making you take action faster to prevent further damages.
d. Reduction of costs. Using big data tools reduces costs by doing large volumes of work using less time, i.e., analyzing data, creating dashboards, faster processing and better storage.
Conclusion
There are many cyber threats, and it is the role of every organization, especially those dealing with sensitive information, to have proper big data security measures. The measures will force organizations to increase their budget toward data security. It must upgrade its infrastructure and employ cyber security researchers to help them be safe from data breaches.
Organizations will also have to get different approaches when setting up infrastructure, setting up risk analysis schedules, ensuring compliance with data governance bodies and changing their hiring methods. Right now, several tools online can help you automate big data security processes. Examples include Teradata, Snowflake, Cloudera and IBM.