Data is one of the most valuable assets for modern organizations. The right type and quality of data allows companies to resolve problems and improve business performance; it guides enterprise decision-making and drives business strategy. Data is also vital to improve cybersecurity, maintain regulatory compliance, and strengthen the competitive posture.

In short, data matters.

Organizations must protect their data assets from unauthorized access, compromise, and theft. For this, they need robust data security strategies, procedures, policies, and controls. They need something that can be called a Data Security Fabric.

Read on to learn more about Data Security Fabric and why it is such a crucial component of modern data security plans and programs.

What Is Data Security?

Data security is the practice of protecting enterprise data from unauthorized access, corruption, exfiltration, and other undesirable events. A data security program encompasses every aspect of information security, including:

  • Strategies, tactics, and controls for the physical security of hardware
  • Administrative and access-based security of enterprise resources
  • Logical security of software and digital applications

An effective data security ecosystem includes multiple elements, including:

  • Tools and technologies
  • Policies
  • Standard operating procedures
  • Security and access controls
  • User training

Together, these elements safeguard enterprise information assets from cybercriminals, insider threats, and human error – all serious threats to data resources and the leading causes of data breaches.

The need for data security

Today’s enterprises create and store vast quantities of data. Their data ecosystems are also growing in size and complexity, spanning the cloud, Internet of Things (IoT) sensors, remote devices, and more. This complexity expands their attack surface and makes them attractive targets for cybercriminals.

It’s crucial to secure data because its loss or theft can be catastrophic. Companies that lose their intellectual property (IP) or trade secrets, struggle to meet their strategic goals, lose customers’ trust, and experience a hit to their financial standing and profitability.

Further, regulators are increasing the pressure on organizations to strengthen their data security controls. The costs of non-compliance with these requirements can be high, so companies have a strong financial incentive to implement robust data security programs.

Data security measures, tools, and technologies

The business challenges described above are all driving a greater need for data governance and security; that’s why companies must protect their data at all costs. For this, they need a robust data security ecosystem that includes strong data security tools and technologies.

These tools should show stakeholders what kind of data the company generates, collects, processes, and stores. They should also provide internal teams visibility into where the most critical data resides, how it is used, and by whom.

Organizations also need a data security plan to implement the right data security measures. They also need a data security fabric to (1) secure sensitive data; and (2) leverage all that data as a functional asset and strategic multiplier.

Crucial Elements to Improve Enterprise Data Security

Data security tools should apply strong protective measures to safeguard business data. These measures should be part of the overall data security plan and may include some or all of the following.


Encryption refers to the use of algorithms to transform data into a format unreadable to unauthorized users. The process uses an encryption algorithm (cipher) and an encryption key to encode plaintext, human-readable data into encrypted data (ciphertext). The receiving party or authorized user uses the encryption key to decode the ciphertext back into its original value.

Tokenization or masking

In tokenization, sensitive data is replaced by non-sensitive surrogate values or tokens. The tokens have the same length, character set, and format as the original data.

Tokenization is an important data protection strategy since it adds strong data protection to existing applications with minimal changes. Tokens can pass validation checks and require no additional storage. Plus, they can secure many data types, including sensitive data such as personal health information, financial information, and payment card data.

Data redaction and erasure

Redaction is a data masking technique where all or part of the available data is removed. It is often considered the first step when designing a data security strategy. Organizations can choose full or partial redaction to protect their most sensitive data.

Data erasure refers to overwriting data completely and verifying that it is unrecoverable. Erasure is more secure than standard data wiping since it overwrites data with random binary information, and is suitable for all kinds of IT assets and environments.

The role of compliance in a data security plan

Data compliance and data security go hand-in-hand. Modern security teams need a solid security plan and robust tools to implement security procedures without tedious manual labor. They also need itools to streamline data-related compliance processes related to:

  • Assigning policy controls
  • Locating and documenting regulated data
  • Enforcing separation of duties
  • Retaining data audit records
  • Audit record storage automation
  • Defining policies for audit data de-duplication and compression

A solid data security plan and the right tools assure that the organization maintains compliance with all applicable global data privacy regulations. In addition to simplifying these processes, they reduce compliance costs and enable compliance teams to streamline reporting and compliance monitoring.

What Is a Data Security Fabric?

Traditional endpoint and perimeter-based security solutions are no longer adequate to protect valuable enterprise data and maintain its integrity. Even data encryption and access controls are not enough if a threat actor successfully circumvents the security perimeter or steals privileged account credentials.

Effective data protection requires data-centric security and compliance solutions. That is, security isn’t so much about keeping attackers out, as much as it’s about keeping data secure no matter who has it.

These solutions must use explicit data risk controls to support traditional enterprise security approaches. Further, the controls must support enterprise data policies and drive policy-compliant behaviors. Most importantly, they should empower security teams to pinpoint and mitigate threats to data before those threats expand into truly damaging security events.

This is where a Data Security Fabric (DSF) comes in.

A DSF is a data-centric solution that lets organizations implement strong security controls throughout the enterprise. They:

  • Protect sensitive data from data breaches and cyberattacks like ransomware
  • Streamline data-related compliance processes
  • Accelerate data audits for detailed search and investigation
  • Provide instantaneous compliance reporting via built-in interactive dashboards and on-demand visualization tools

With a DSF, enterprise users get the actionable data insights they need to inform operations and drive decision-making. The software platform drives data-sharing and efficient data discovery across and among multiple stakeholders and security environments. It also allows data owners to maintain full visibility into and control over their data.

The DSF implements validation access and ensures that the integrity, authority, performance, and non-repudiation of data are always maintained. Plus, there’s no need for a centralized data repository or data lake controlled by a single party. Instead, networks, systems, and users can easily discover, share, or transfer data, knowing that it is fully traceable, auditable, and secure.

Why a Data Security Fabric Is Superior to Traditional Data Security Solutions

A DSF supports all kinds of enterprise data and information assets, even in hybrid data environments spanning on-premises and multi-cloud assets. It standardizes data security controls across such complex environments to provide centralized command over all data assets and repositories.

The best DSF solutions consolidate data management and security oversight. They reduce the data protection footprint and eliminate the inefficiencies introduced by disjointed security tools. They also integrate with SIEM and SOAR solutions, web application firewalls, and identity management tools to provide interoperable, end-to-end data security.

With the right DSF, security and compliance teams can implement consistent best practices, security policies, and standard procedures over the entire information ecosystem. They also get the advantage of automated security and compliance capabilities in a single solution, plus a centralized security platform and dashboard to manage data discovery, monitor data activity, and leverage data risk analytics.

Some DSF platforms also provide capabilities for:

  • Threat intelligence for active detection and risk assessments
  • Data loss prevention
  • Data access control
  • Data masking
  • Audit and compliance reporting
  • Automated incident management workflows
  • Policy-based data retention and automated data storage and archival

The DSF further protects enterprise data with active and predictive analytics. It analyzes data access activity and automatically determines whether an event violates a compliance or security policy. In addition, it uses machine learning-based detection techniques to spot known attack exploits and pinpoint suspicious activities. It also scans data repositories to reduce the data breach risk from internal threats and outsider cyberattacks.

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