What is data security?

This concept encompasses the entire spectrum of information security. It includes the physical security of hardware and storage devices, along with administrative and access controls. It also covers the logical security of software applications and organizational policies and procedures.

When properly implemented, robust data security strategies protect an organization’s information assets against cybercriminal activities. They also guard against insider threats and human error, which remain among the leading causes of data breaches today.

Data security involves deploying tools and technologies that enhance the organization’s visibility into the location of its critical data and its usage. Ideally, these tools should be able to apply protections such as encryption, data masking and redaction of sensitive files, and should automate reporting to streamline audits and adhering to regulatory requirements.

Digital transformation is profoundly altering how businesses operate and compete today. Enterprises are creating, manipulating and storing an ever-increasing amount of data, driving a greater need for data governance. Computing environments have also become more complex, routinely spanning the public cloud, the enterprise data center and numerous edge devices such as Internet of Things (IoT) sensors, robots and remote servers. This complexity increases the risk of cyberattacks, making it harder to monitor and secure these systems.

At the same time, consumer awareness of the importance of data privacy is on the rise. Public demand for data protection initiatives has led to the enactment of multiple new privacy regulations, including Europe’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA). These rules join longstanding data security laws such as the Health Insurance Portability and Accountability Act (HIPAA), protecting electronic health records, and the Sarbanes-Oxley Act (SOX), protecting public company shareholders from accounting errors and financial fraud. Maximum fines in the millions of dollars magnify the need for data compliance; every enterprise has a strong financial incentive to ensure it maintains compliance.

The business value of data has never been greater than it is today. The loss of trade secrets or intellectual property (IP) can impact future innovations and profitability, so trustworthiness is increasingly important to consumers.

To enable the confidentiality, integrity and availability of sensitive information, organizations can implement the following data security measures:

1. Encryption
2. Data erasure
3. Data masking
4. Data resiliency

By using an algorithm to transform normal text characters into an unreadable format, encryption keys scramble data so that only authorized users can read it. File and database encryption software serve as a final line of defense for sensitive volumes by obscuring their contents through encryption or tokenization. Most encryption tools also include security key management capabilities.

Data erasure uses software to completely overwrite data on any storage device, making it more secure than standard data wiping. It verifies that the data is unrecoverable.

By masking data, organizations can allow teams to develop applications or train people that use real data. It masks personally identifiable information (PII) where necessary so that development can occur in environments that are compliant.

Resiliency depends on how well an organization endures or recovers from any type of failure—from hardware problems to power shortages and other events that affect data availability. Speed of recovery is critical to minimize impact.

Data security tools and technologies should address the growing challenges inherent in securing today’s complex, distributed, hybrid or multicloud computing environments. These include understanding the storage locations of data, tracking who has access to it, and blocking high-risk activities and potentially dangerous file movements.

Comprehensive data protection tools that enable enterprises to adopt a centralized approach to monitoring and policy enforcement can simplify the task. These tools include:

1. Data discovery and classification tools
2. Data and file activity monitoring
3. Vulnerability assessment and risk analysis tools
4. Automated compliance reporting

Data discovery and classification tools actively locate sensitive information within structured and unstructured data repositories, including databases, data warehouses, big data platforms and cloud environments. This software automates the identification of sensitive information and the assessment and remediation of vulnerabilities.

File activity monitoring tools analyze data usage patterns, enabling security teams to see who is accessing data, spot anomalies, and identify risks. Security teams can also implement dynamic blocking and alerting for abnormal activity patterns.

File activity monitoring tools analyze data usage patterns, enabling security teams to see who is accessing data, spot anomalies, and identify risks. Security teams can also implement dynamic blocking and alerting for abnormal activity patterns.

These tools ease the process of detecting and mitigating vulnerabilities such as out-of-date software, misconfigurations or weak passwords, and can also identify data sources at greatest risk of exposure.

Comprehensive data protection solutions with automated reporting capabilities can provide a centralized repository for enterprise-wide compliance audit trails.

Protecting sensitive information doesn't stop with discovery and classification. DSPM tools go steps further to discover shadow data, uncover vulnerabilties, prioritize risks and reduce exposure. Continous monitoring provides real-time dashboards that help teams focus on remediation and prevention.

A comprehensive data security strategy incorporates people, processes and technologies. Establishing appropriate controls and policies is as much a question of organizational culture as it is of deploying the right tool set. This means making information security a priority across all areas of the enterprise.

Consider the following facets in your data security strategy:

1. Physical security of servers and user devices
2. Access management and controls
3. Application security and patching
4. Backups
5. Employee education
6. Network and endpoint security monitoring and controls

You might store your data on premises, in a corporate data center or in the public cloud. Regardless, you need to secure your facilities against intruders and have adequate fire suppression measures and climate controls in place. A cloud provider assumes responsibility for these protective measures on your behalf.

Follow the principle of “least-privilege access” throughout your entire IT environment. This means granting database, network and administrative account access to as few people as possible, and only to individuals who absolutely need it to get their jobs done.

Update all software to the latest version as soon as possible after patches or the release of new versions.

Maintaining usable, thoroughly tested backup copies of all critical data is a core component of any robust data security strategy. In addition, all backups should be subject to the same physical and logical security controls that govern access to the primary databases and core systems.

Learn more about data backup and recovery

Transform your employees into “human firewalls”. Teaching them the importance of good security practices and password hygiene and training them to recognize social engineering attacks can be vital in safeguarding your data.

Implementing a comprehensive suite of threat management, detection and response tools in both your on-premises and cloud environments can lower risks and reduce the chance of a breach.

In the changing landscape of data security, new developments such as AI, multicloud security and quantum computing are influencing protection strategies, aiming to improve defense against threats.

AI amplifies the ability of a data security system because it can process large amounts of data. Cognitive computing, a subset of AI, runs the same tasks as other AI systems but it does so by simulating human thought processes. In data security, this simulation allows for rapid decision-making in times of critical need.

Learn more about AI for cybersecurity

The definition of data security has expanded as cloud capabilities grow. Now, organizations need more complex tools as they seek protection for not only data, but also applications and proprietary business processes that run across public and private clouds.

Learn more about cloud security

A revolutionary technology, quantum promises to upend many traditional technologies exponentially. Encryption algorithms will become much more faceted, increasingly complex and much more secure.

The key to applying an effective data security strategy is adopting a risk-based approach to protecting data across the entire enterprise. Early in the strategy development process, taking business goals and regulatory requirements into account, stakeholders should identify one or two data sources containing the most sensitive information, and begin there.

After establishing clear and tight policies to protect these limited sources, they can then extend these best practices across the rest of the enterprise’s digital assets in a prioritized fashion. Implemented automated data monitoring and protection capabilities can make best practices far more readily scalable.

Securing cloud-based infrastructure needs a different approach than the traditional model of defending the network's perimeter. It demands comprehensive cloud data discovery and classification tools, and ongoing activity monitoring and risk management. Cloud monitoring tools can sit between a cloud provider’s database-as-a-service (DBaaS) software and monitor data in transit or redirect traffic to your existing security platform. This enables the uniform application of policies, regardless of the data's location.

The use of personal computers, tablets and mobile devices in enterprise computing environments is on the rise despite security leaders’ well-founded concerns about the risks of this practice. One way of improving bring-your-own-device (BYOD) security is by requiring employees who use personal devices to install security software to access corporate networks, thus enhancing centralized control over and visibility into data access and movement.

Another strategy is to build an enterprise-wide, security-first mindset by teaching employees the value of data security. This strategy includes encouraging employees to use strong passwords, activate multifactor authentication, update software regularly, back up devices and use data encryption.