Businesses collect and store important data on a daily basis — anywhere from their customers’ personal information to private financial information. With the high volume of data processed and ever-evolving data protection regulations, like the GDPR and CCPA, it has become critical for businesses to protect their data, and this is where data security comes into action. Data security is the practice of protecting data in accordance with laws and industry standards. Businesses that implement strong data security measures can mitigate potential risks and prevent cyberattacks or data breaches.
What are the core elements of data security?
The CIA triad, not to be confused with the U.S. Central Intelligence Agency, is often referred to as the core model that guides an organization’s data security efforts. The acronym CIA stands for confidentiality, integrity and availability, which are the core tenants of a business’s data security plan.
This component enforces the privacy aspect of a data security plan. Particular pieces of data need to be protected and disclosed only to approved parties, and this is especially prudent when it comes to sensitive data.
In practice, this could look like a secure website requiring two-factor authentication to access an online account.
This ensures that the accuracy of data is maintained without unauthorized deletions or modifications. Whether data is altered with malicious intent or accidentally, if the integrity of the information has been compromised, that could result in negative consequences for the organization.
An example of maintaining data integrity may be controlling user permissions on certain data, such as read-only access for a particular group of users and editing permissions for users who are qualified to edit data.
This area dictates that authentication processes, access channels and systems need to be properly maintained so that authorized persons are able to access data when they need to do so.
To put this principle into practice, organizations need to ensure that systems are running efficiently (like keeping hardware up-to-date), can handle unexpected network loads, and have failover recovery plans in place. External forces like disruption of service, natural disasters or outages can compromise data availability.
The importance of data security is increasing
Data security has always been important, but that importance is rising every day. Not only is data more prevalent than ever in business operations, but the COVID-19 pandemic fundamentally altered certain industries’ relationships with data. Increased online shopping activity has made retailers and the personal information they collect and store from those transactions a huge target for cyberattacks. Meanwhile, many other businesses are trying to remain productive amid the mass shift to remote work but risking security in the process by sending data across external networks. These companies need to consider things like endpoint laptop devices when planning for evolving threats. Data security measures need to be holistic and consider all stages of the data lifecycle.
What are the different types of data security measures?
- Authentication – Authentication technology is designed to help verify if a user’s credentials match with the approved credentials stored within an organization’s database. Some common examples of authentication implements are two-factor authorization or biometrics.
- Data backup – Backing up data entails making a copy of data and storing that on a separate system or environment. That way, in case of a system failure, natural disaster, data corruption or data breach, an organization can recover any potentially lost data.
- Data encryption – Encryption is used to protect the data stored, transferred and exchanged. By using an algorithm called a cipher, organizations can use encryption keys to turn normal text into encrypted ciphertext, which is unreadable to an unauthorized user. The data can only be decrypted by a user with an authorized key.
- Data masking – Data masking software hides data by obscuring letters and numbers with proxy characters, and then reverting data to its original form when an authorized person receives the data.
- Data loss prevention (DLP) – To aid in ensuring the integrity of data, DLP software detects and prevents data breaches and unwanted destruction of sensitive data.
- Employee education – A strong framework for data security involves more than just cybersecurity tools — it also involves following a set of rules and processes. Organizations should make time to train employees on what data security is and the importance of data security, along with general tips, like how to create a strong password and how to handle suspicious emails, to create a security culture in your organization.
What types of data require prioritized data security efforts?
Data is commonly classified into four different groups: public, internal, confidential and restricted. Public data, as the name indicates, is data that is freely accessible to the public with minimal to no security measures. Prioritized from least to greatest, in terms of the level of sensitivity, are internal, confidential and restricted data. Information with a high level of sensitivity should be prioritized when it comes to an organization’s data security efforts, since unauthorized disclosure or a data breach of such information could result in severe financial, legal, regulatory or reputational risk.
Some examples of restricted sensitive data include:
- Personally identifiable information (PII) – Data that can be used to conduct identity theft, blackmail, stalking, or other crimes against a person.
- Protected health information (PHI) – Information regarding a person’s health-related information, like medical conditions, medical history or medical emergency contact information.
- Biometric data – Biometrics are a set of physical and behavioral characteristics that can be used to digitally identify a person and grant access to devices or systems.
Additionally, organizations need to be aware of laws or regulations that may be location-specific, like the CCPA, or that may be industry-specific like the GLBA. If there are certain laws that apply directly to an organization, data that falls underneath those governances should be a focused effort.
What data security factors should a business consider?
As technologies and regulatory laws change, it’s important for organizations to regularly revisit their data security approach to ensure that they’re doing their best in protecting their employees and customers, and remaining compliant with federal and state laws. Some of the factors that organizations may want to consider when revisiting their data security approach are:
- Where sensitive data is stored (on-premise network, the cloud, etc.)
- Who has access to stored sensitive data
- What types of data your organization is collecting from customers
- The size of your organization and customer base
- Your organization’s level of data security knowledge
- The current roles and responsibilities of your security team
- What cybersecurity softwares your organization is currency implementing
Which business verticals are the most reliant on data security?
Although data security is a critical endeavor for all organizations, particular industries have greater stakes when it comes to ensuring data security.
- Finance – Organizations that offer financial services likely collect sensitive financial data from consumers and need to take the right precautions to protect said data. These organizations also need to be aware of the GLBA, which directly impacts financial institutions.
- Education – Higher education institutions collect large amounts of sensitive data, like healthcare-related information and payment information, from both their staff and student body.
- Healthcare – Healthcare organizations are required to comply with multiple data security regulations, like HIPAA and HITECH, and need to accurately protect sensitive patient data.
- eCommerce – There are multiple security regulations that eCommerce organizations must adhere to, like the GDPR, CCPA and PCI-DSS which all protect consumer data privacy rights.
- Manufacturing – With the development of the Internet of Things (IoT), many manufacturing companies store large amounts of customer data.
- Telecommunications – A great deal of sensitive data, like user behaviors, location and transactional information, is collected in real-time by telecommunication companies.
What are the risks of getting data security wrong?
If the confidentiality, integrity or availability of sensitive data is compromised, organizations could face serious financial, legal and reputational backlash. According to a report by IBM, the global average total cost of a data breach in 2020 is $3.86 million, with the healthcare industry having the highest industry average cost. This financial loss can be attributed to the amount of money allocated towards containing or remediating a data breach and towards compensating affected individuals, at a minimum. There are also legal risks, like lawsuits or settlements.
Apart from the financial and legal risks, companies may also face long-lasting reputational damage. A breach or cybersecurity attack can cause panic among an organization’s customer base, and it could take years for an organization to rebuild trust with the public.
Where should you start when identifying data security risks?
Data security for businesses does not have to be an overwhelming endeavor. By taking the proper precautions, you can enable your organization’s security team to proactively manage potential data security risks and quickly remediate any existing risks.
The first step to assuring a strong data security framework is sensitive data discovery. Most companies don’t even realize the amounts of data scattered across their organization that go unseen. Data discovery helps organizations get a full view of their data landscape by finding structured and unstructured data, which can typically be hard to trace.
Data privacy management software can make this step easier for security teams. The Spirion Sensitive Data Platform, includes tools that streamline the entire process with automated data discovery, data classification and remediation.