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Database Security: How Cloud DLP Can Help Protect Sensitive Data
Some of the most damaging data leaks have resulted from poor database security. In March 2020, 10.88 billion records were stolen from adult video streaming website CAM4’s cloud storage servers. In March 2018, 1.1 billion people were the victim of a breach of the world’s largest biometric database, Aadhaar. And, in April 2021, 533 million users had their information compromised when a Facebook database was leaked on the dark web for free.
Databases are great targets for hackers and cybercriminals. There’s a wealth of information that can be gained by infiltrating a database, from proprietary intellectual property to customer data to financial records. One of the reasons why database security is so difficult is known as “Anderson’s rule” — that the more useable and more accessible the database, the more vulnerable it is to security threats. Conversely, the more invulnerable the database is to threats, the more difficult it is to use.
Cloud DLP can help IT teams continually monitor and improve database security, without making it more difficult to use the data stored. Here’s how data security works, and ways to improve database security without making it impossible to use the database in question.
What is database security?
Database security is defined by Techopedia as “the collective measures used to protect and secure a database or database management software from illegitimate use and malicious cyber threats and attacks.”
Database security aims to protect both the data inside the database as well as the system storing the data (the database management system, or DBMS) and all the applications that access the data. Database security aims to limit the risk of a data breach, maintaining the confidentiality of data at rest. This can be accomplished a number of different ways; and ideally, IT teams take a multipronged approach to the threats that seek to exploit databases.
Common threats to database security
Insider threat and human error are among the biggest threats to database security. Weak passwords, password sharing, and even a malicious insider are common causes of data breaches. Training and constant monitoring, as well as IAM best practices, are key to lowering the threat of human error.
There are other attacks that are specifically designed to compromise database security. SQL/NoSQL injection attacks, for instance, involve the insertion of arbitrary SQL or non-SQL attack strings into database queries. The attack will be executed by the database when someone runs that specific query.
DDoS and buffer overflow attacks are also common. DDoS (denial of service attacks) take place when an attacker deluges a database server with so many requests that the server can no longer function to respond to legitimate requests. The server crashes as a result. A buffer overflow is a similar attack in which a process attempts to write more data to a fixed-length blog of memory than it is allowed to hold. Attackers use the excess data to launch attacks.
And finally, malware and attacks on backups or connected apps can also compromise database security. Malware can be introduced to the database using any endpoint device connecting to the database’s network. Attacks on backups can exploit misconfigurations or take advantage of the organization’s assumption that backups are lower value targets, and therefore less carefully protected than the original database.
Strategies for database security
Database security is a bit different from other web security practices. Database security involves typical training and software solutions, but also physical steps to secure database storage. Here are a few examples of strategies that should be layered to ensure proper database security.
- Identity and Access Management (IAM): Restrict access and use to the database by implementing multifactor access and data management controls.
- Perform stress testing: Load and capacity testing to make sure the database does not crash in the event of a DDoS attack or user overload.
- Secure the physical server: Make sure the database server and backup equipment are safe from theft or natural disaster; keep redundant copies for emergency recovery.
- Regularly scan for vulnerabilities: Review the system on a schedule for known/unknown vulnerabilities and create a plan to fix them.
- Encrypt data: Add extra security to protect the confidentiality of data.
Within these broad strategies, there are more targeted approaches to database security that IT teams should consider. For instance, in addition to data encryption, consider setting up packet filter firewalls, stateful packet inspection (SPI), and proxy server firewalls to protect data in motion.
Database security and cloud DLP
Cloud data loss prevention s a type of DLP that is specifically designed to protect data stored in the cloud. Cloud DLP tools protect information on IaaS, PaaS, and SaaS programs such as Slack, Google Drive, and AWS. These DLP tools can be set up to automatically scan and audit data, detecting and encrypting PII and other valuable information in the process.
More and more companies are turning to cloud storage to house their data. Public and hybrid cloud solutions are a cost-effective and easy way to store large quantities of information. However, these platforms require a unique data loss prevention program that can scan for and protect valuable information.
Nightfall is a cloud-native DLP platform that detects and classifies sensitive data and allows you to set custom actions to prevent the data from leaking. IT teams can delete messages that contain data that could lead to a data breach, such as API keys and other credentials, personally identifiable information (PII) like credit card numbers, or protected health information (PHI) like medical record numbers.
Nightfall can be fully customized to scan cloud storage environments to search for business-critical data that is at risk of loss. Protect access to code repos by scanning GitHub for private keys, or prevent PII like Social Security Numbers from being shared in Slack channels. Set up granular rules using our policy engine, and use our developer platform to set up custom scans for any cloud SaaS or IaaS platform. Any piece of data that needs protecting is covered with Nightfall.
To learn more about Nightfall, set up a demo using the calendar below.
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Nightfall is the industry’s first cloud-native DLP platform that discovers, classifies, and protects data via machine learning. Nightfall is designed to work with popular SaaS applications like Slack, Google Drive, GitHub, Confluence, Jira, and many more via our Developer Platform. You can schedule a demo with us below to see the Nightfall platform in action.
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