What Is Data Security?

Data security refers to the measures and practices implemented to protect sensitive information from unauthorized access, use, disclosure, disruption, modification or destruction. It involves various techniques and technologies such as encryption, access controls, firewalls, antivirus software, secure backups, and employee training.

Data security encompasses every effort a company makes to keep its data free from risk and compromise. As digital data has exploded in both volume and importance in the 21st century, becoming‌ the most valuable asset a company has, protecting it becomes mandatory. Whatever tools, techniques, policies, and training methods are used to preserve that protection fall under the broad umbrella of data security.

Data security involves both an up-front effort to establish baseline data security standards followed by an ongoing effort to maintain and evolve those standards in response to new data, emerging threats, or changes in the IT environment.

Securing data begins by identifying all the data a company has stored across the enterprise. Rarely, if ever, does this data live all in one location; it’s spread widely throughout databases, applications, and endpoints, and it includes physical data (documents, notes, etc.) along with digital data. Some data sources are obvious. Others, however, are easy to overlook or ignore, leaving certain data unsecured and more vulnerable as a result. Applying minimum data security standards to everything depends on finding all of it first.

Next comes the ranking of risks. Though all data needs security, some data requires extra precautions. Sensitive data like financial records, personally identifiable information (PII), and intellectual property must be closely guarded since it’s the prime target of attacks and the most expensive when involved in a data security breach. Those in charge of data security need to identify which assets are most at risk, whether because they’re highly vulnerable or highly sensitive. Then they need to thoroughly catalog those assets and, as necessary, surround them with additional data security.

Once a clear map of the data architecture has been established, data security becomes about putting various data security solutions in place. The specific solutions will vary by organization, but in most cases will include cybersecurity tools for detecting, blocking, and remediating the full spectrum of cyberattacks. Also important will be tools for verifying and validating anyone attempting to access data while managing access privileges over time. Cybersecurity standards like antivirus and user behavior analytics can help guard against a data security breach, but in other cases, dedicated data security software will be necessary to ward off attacks.

Developing a data security policy matters just as much as installing the right data security solutions. Policies dictate how users at all levels interact with data, from how they pick their passwords to what they keep in their email inbox. Policies also prescribe how current and future technologies will handle enterprise data, from where and how it gets stored to what cybersecurity measures get applied.

Data security management is the final component. Data security starts but doesn’t stop, which is to say it takes constant review and revision. As companies store more data in more places inside elastic IT environments, the tools, policies, and methods of data security must change to be able to stay effective.

Though related — and to a certain extent, overlapping — data security and privacy are two distinct concepts that each require a concerted effort. Data privacy is about restricting access to specific types of data. It strives to give individuals control over their private information, letting them decide what can be collected and stored, who has access, and under what conditions. As such, policies that govern how companies manage data play a central role in data privacy.

If data privacy is about how companies collect, store, and utilize data, data security deals with how they defend it. And while data privacy focuses primarily on sensitive information (PII, IP, etc.), data security addresses all the information kept by a company and defends against any form of loss whether privacy is at risk or not. To secure data means to protect everything from anything. Cybersecurity tools and techniques will be used to stop attacks, but user training and data security policies help prevent those attacks in the first place.

Two brief examples illustrate the difference between data security and privacy. Encrypted data may be private, but it’s not necessarily secure unless there are additional protections in place. Likewise, there may be robust protections surrounding data that was collected in ways that violate ‌privacy policies, making it secure but not private.

If data security is about preventing anything that could have a negative effect on data, the concept of data protection is about mitigating those negative impacts. Should a lapse in security ever put data at risk, data protection keeps the consequences to a minimum.

To that end, data protection concentrates on secure data recovery: systems that back up and restore data so that companies can recover anything that was lost or corrupted in an attack. The goal of data protection is to back up everything seamlessly and systematically, and ensure that the recovery and restoration process runs efficiently. Many companies rely on secure data recovery services that are bound by service-level agreements for backup thoroughness and recovery speed. Operating without data isn’t possible, and losing it is even worse, so data protection and data security work in close coordination, the former serving as a failsafe to the latter.

Threats to data come in many forms, all of which data security has the duty to guard against. Some of the leading concerns include:

• Cyberattacks: From ransomware to phishing schemes, cyberattacks have become more sophisticated at bypassing security measures and more successful at breaking into enterprise data. The frequency of attacks, the number of hackers behind them, and the amount of resources flowing into cybercrime are all increasing. Cybersecurity and data security are closely aligned since most attacks have the malicious intent to steal or destroy data.
• Compliance: Many companies need to follow regulations mandating data security and privacy. Examples include HIPAA for health information, and GDPR, which applies to all PII collected in the European Union. Failure to collect, secure, and protect data as required can result in massive financial penalties while raising the risk of a data security breach.
• Insider threats: Whether intentionally or unintentionally, the actions of employees may result in data exposure, loss, or compromise. Many cyberattacks depend on unsuspecting users to allow them inside. And since employees have elevated access to data, there’s always a chance they’ll misuse it, in some cases with the express intent to harm a company.
• Clouds: Data security in cloud computing poses a threat because of attacks that target data in transit (to or from the cloud) or sitting at rest inside insecure cloud environments controlled by third parties. The rapid shift to cloud computing further compromises data security by transforming IT environments in ways that undermine existing defenses, sometimes without notice. Cloud data security will be a major challenge — and a continuing threat — as more data migrates outside a company’s strict control.

Everyone inside an organization or with privileges to access that organization’s data (like third-party partners) has some responsibility for data security. It only takes one person, even a person with minimal access rights, to cause a data security breach and all the hazards that follow. The role that everyone plays in data security should be a large part of training efforts and policy-making.

Data security is a big enough issue — and a big enough workload — that many companies have one or more employees working specifically on securing data. Many security teams include a data security analyst to hunt for threats, search for vulnerabilities, and lead data security improvement efforts. When necessary, companies will employ specialists to handle the unique requirements of things like data center security or big data security. When there’s a Chief Information Security Officer (CISO) in the C-suite, companies will look to that person to lead data security efforts and account for any failures. In other cases, the IT director or security head will be in charge of data security.

Application programming interfaces (APIs) are a significant factor in data security. They’re the doorway into applications and all the data, privileges, and functionality they contain. Modern enterprises can’t operate without APIs and the efficient exchange of information they make possible. But they also can’t underestimate the threat to data this represents.

Incorrectly configured APIs could be a hidden weakness in an otherwise sound data security strategy. Just as problematic, sophisticated attacks can seize on any weakness in an API to launch a full-scale data security breach. A Facebook breach that exposed the data of 50 million users was just one of many major attacks blamed on insecure APIs.

Upholding data and network security, preventing (rather than mitigating) attacks, and ensuring business continuity all depend on API security. Data security is at risk without it, and, more broadly, no cybersecurity strategy will succeed until APIs become ironclad against attacks.

Data masking is a technique used to protect sensitive information by obfuscating it while retaining its usability for non-production environments, such as testing or development. Through data masking, an organization can create a “masked” version of its data that behaves like real data but is anonymized. This ensures that testers or developers can interact with realistic data without risking exposure to personal, financial, or other sensitive information.

Data masking supports data security by reducing the risk of exposure during internal operations. It is particularly important in industries handling vast amounts of personal data, such as healthcare and finance. By replacing original data with fictitious but realistic alternatives, data masking helps ensure that unauthorized access doesn’t lead to breaches, protecting against data loss while preserving the integrity of the system’s functions.

Regular security audits are essential for maintaining strong data security practices. By conducting audits, organizations can identify vulnerabilities, ensure compliance with security regulations like GDPR and CCPA, and assess whether data protection measures are effectively implemented. Security audits involve reviewing data access controls, encryption protocols, multi-factor authentication, and other protective measures to ensure the organization’s policies are up to date with evolving cybersecurity threats.

In the event of a data breach, failing to have proper audit trails can complicate investigations and regulatory responses. By proactively performing audits, companies not only identify weaknesses before they are exploited but also ensure ongoing compliance with legal standards. Maintaining an audit log also provides valuable insights into how data is handled and who has access to it, further enhancing overall data security.