Data Security

What is Data Security?

Data security is a practice that involves the protection of digital information from unauthorized access, corruption, destruction, modification, theft, or disclosure.

The data security process encompasses techniques and technologies such as security of physical hardware (e.g., storage devices), logical security of software applications, administrative and access controls, organizational policy standards, and other data security practices.

Why is Data Security Important?

The core elements of data security include availability, confidentiality, and integrity. Organizations that do not keep the said elements in check may end up regretting it or worse. Below are some of the major reasons for implementing data security measures, especially for organizations that handle not only their own data but customer data as well.

• The main purpose of data security is to protect organizational data, which contains trade information and customer data. The data can be accessed by cybercriminals for malicious reasons, compromising customer privacy.
• Compliance with industry and government regulations; it is critical to adhere to regulations for the business to carry on operating legally. The regulations exist to protect consumer privacy.
• Data security is also important because if a data breach occurs, an organization can be exposed to litigation, fines, and reputational damage.
• Due to a lack of adequate data security practices, data breaches can occur and expose organizations to financial loss, a decrease in consumer confidence, and brand erosion. If consumers lose trust in an organization, they will likely move their business elsewhere and devalue the brand.
• Breaches that result in the loss of trade secrets and intellectual property can affect an organization’s ability to innovate and remain profitable in the future.

Types of Data Security Technologies

There are various types of data security technologies in use today that protect against various external and internal threats. Organizations should be using many of them to secure all potential threat access points and safeguard their data. Below are some of the techniques:

Data encryption

Data encryption uses an algorithm to scramble every data character converting information to an unreadable format. Encryption keys from authorized users only are needed to decrypt the data before reading the files.

Encryption technology acts as the last line of defense in the event of a breach when confidential and sensitive data is concerned. It is crucial to ensure that the encryption keys are stored in a secure place where access is restricted. Data encryption can also include capabilities for security key management.

Authentication

Authentication is a process of confirming or validating user login credentials to make sure they match the information stored in the database. User credentials include usernames, passwords, PINS, security tokens, swipe cards, biometrics, etc.

Authentication is a frontline defense against unauthorized access to confidential and sensitive information, making it an important process. Authentication technologies, such as single sign-on, multi-factor authentication, and breached password detection make it simpler to secure the authentication process while maintaining user convenience.

Data masking

Masking whole data or specific data areas can help protect it from exposure to unauthorized or malicious sources externally or internally. Masking can be applied to personally identifiable information (PII), such as a phone number or email address, by obscuring parts of the PPI, e.g., the first eight digits or letters within a database.

Proxy characters are used to mask up the data characters. The data masking software changes the data back to its original form only when the data is received by an authorized user. Data masking allows the development of applications using actual data.

Tokenization

Tokenization is similar to data encryption but differs in that it replaces data with random characters, where encryption scrambles data with an algorithm. The “token,” which relates to the original data, is stored away separately in a database lookup table, where it is protected from unauthorized access.

Data erasure

Data erasure occurs when data is no longer needed or active in the system. The erasure process uses software to delete data on a hardware storage device. The data is permanently deleted from the system and is irretrievable.

Data resilience

Data resilience is determined by the ability of an organization to recover from incidences of a data breach, corruption, power failure, failure of hardware systems, or loss of data. Data centers with backup copies of data can easily get back on their feet after a disruptive event.

Physical access controls

Unlike digital access control, which can be managed through authentication, physical access control is managed through control of access to physical areas or premises where data is physically stored, i.e., server rooms and data center locations. Physical access control uses security personnel, key cards, retina scans, thumbprint recognition, and other biometric authentication measures.

Data Security Best Practices

An organization can take several steps in addition to the data security technologies above to ensure robust data security management.

1. External and internal firewalls: Using external and internal firewalls ensures effective data protection against malware and other cyberattacks.
2. Data security policy: An organization should adopt a clear and comprehensive data security policy, which should be known by all staff.
3. Data backup: Practicing backup of all data ensures the business will continue uninterrupted in the event of a data breach, software or hardware failure, or any type of data loss. Backup copies of critical data should be robustly tested to ensure adequate insurance against data loss. Furthermore, backup files should be subjected to equal security control protocols that manage access to core primary systems.
4. Data security risk assessment: it is prudent to carry out regular assessments of data security systems to detect vulnerabilities and potential losses in the event of a breach. The assessment can also detect out-of-date software and any misconfigurations needing redress.
5. Quarantine sensitive files: Data security software should be able to frequently categorize sensitive files and transfer them to a secure location.
6. Data file activity monitoring: Data security software should be able to analyze data usage patterns for all users. It will enable the early identification of any anomalies and possible risks. Users may be given access to more data than they need for their role in the organization. The practice is called over-permission, and data security software should be able to profile user behavior to match permissions with their behavior.
7. Application security and patching: Relates to the practice of updating software to the latest version promptly as patches or new updates are released.
8. Training: Employees should continually be trained on the best practices in data security. They can include training on password use, threat detection, and social engineering attacks. Employees who are knowledgeable about data security can enhance the organization’s role in safeguarding data.

Data Security Laws and Regulations

A growing number of countries and regions are adopting data security laws and regulations that are mainly focused on protecting personal data and guidance on usage and accessibility to all concerned. The regulations also aim to ensure that providers of personal data are treated fairly and sharing of data is done legitimately.

General Data Protection Regulation (GDPR)

In April 2016, the European Union (EU) adopted the General Data Protection Regulation (GDPR), which requires businesses and organizations that deal with personal data to protect the personal data and privacy of EU citizens on transactions between member states.

GDPR also regulates the export of personal data outside the EU borders. It is a regulation governing the 28 EU member states that compels organizations to seriously deal with data security and privacy or face stiff penalties.

Compliance with the GDPR focuses on data classification, especially on sensitive data, continuous monitoring, which requires reporting of data breach incidents within 72 hours, metadata management in terms of storage, the purpose of collection and regular data review, and lastly, data governance and access which controls authorizations to corporate data.

Sarbanes-Oxley Act of 2002

The Sarbanes-Oxley Act of 2002 is a U.S. federal law requiring public companies to submit annual assessments detailing the effectiveness of their internal financial auditing controls. The legislation emphasizes compliance on auditing and continuous monitoring, access control, and reporting on data activity usage as evidence of compliance.

Data Security Trends

Quantum computing

Quantum computers use quantum phenomena such as superposition and entanglement to perform computations. It is likely to affect data security profoundly and pose a significant threat. Quantum technology will need to take the lead in transforming how we encrypt data now and bring quantum-proof solutions before quantum computers start breaking current data encryption.

Artificial intelligence (AI)

Artificial Intelligence (AI) augments the capabilities of a data security system making it more efficient in handling increased amounts of data. AI works by simulating human thought processes or intelligence in machines that are programmed to think like humans. Adopting AI in data security enables swift decision-making during critical times.

Multi-cloud security

The multi-cloud security trend came about due to the adoption of cloud computing and storage. Therefore, multi-cloud security refers to a type of protection that extends beyond data to applications and processes that interact with cloud storage services.

Additional Resources

• Artificial Intelligence (AI)
• Financial Engineering
• Data Warehousing
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