What Is Data Security: Definition, Types, Updates, Threats, Practices, Tools, Regulations, Difference with Data Security

Data security in 2025 is a critical priority as global threats continue to evolve. This article explains the core types of data protection, from encryption and masking to access control and incident response. It outlines best practices, tools, and compliance frameworks like GDPR and DPDP. Recent high-impact breaches—Qantas Airways (Australia), Co-op Retail Group (UK), and Gujarat’s digital leash strategy (India)—highlight the urgency for robust safeguards. With expert insights and real-world examples, this guide helps organizations secure sensitive data, reduce risks, and maintain regulatory compliance in an increasingly hostile cyber landscape. 

What is data security?

Data security is a subset of information security focused on protecting sensitive data and personal data from unauthorized access, alteration, or destruction. As cybersecurity practitioners, we’ve seen organizations struggle with enforcing consistent data policies across cloud and on-prem systems. Implementing structured access controls—backed by real-time SIEM alerts—has reduced threat dwell time significantly in our client projects. 

To protect data effectively, organizations adopt a data security strategy that includes: 

• Data encryption to prevent unauthorized reading of stored or transmitted data 
• Data masking to hide real data values in non-production environments 
• Data erasure to securely delete data no longer needed 
• Access controls and monitoring systems to detect and respond to threats 

Compliance with frameworks like the General Data Protection Regulation (GDPR) is also essential, particularly where data privacy and data protection are concerned. 

An effective data security posture minimizes the risk of a data breach, ensures regulatory compliance, and builds trust with stakeholders. Following best practices in data governance is crucial for maintaining secure data across all systems. 

What are the key elements of data security?

The key elements of data security form the foundation of a comprehensive data security strategy designed to protect your data throughout its lifecycle. These elements are essential for reducing data security threats, preventing breaches, and maintaining compliance with data security regulations like the California Consumer Privacy Act and Payment Card Industry Data Security Standard (PCI DSS). 

Core elements include: 

• Security policies and controls – Define who can access data, under what conditions, and with what level of privilege. 
• Data discovery and classification – Identify large amounts of data, tag sensitive data, and apply appropriate security measures to protect it. 
• Data loss prevention (DLP) – Monitor for misuse, unauthorized access, and potential leaks of sensitive data. 
• Data encryption and masking – Data encryption prevents unauthorized access to stored or transmitted data, while data masking hides sensitive information for development or testing. 
• Access control and authentication – Restrict access to sensitive data based on user roles, ensuring that only authorized personnel can retrieve critical information. 
• Data backup and recovery – Ensure availability and restore company data in case of data corruption, loss, or a security incident. 
• Security tools and technologies – Use modern security technologies and software to monitor data usage, enforce security standards, and support data security management. 

These elements work in unison to ensure that data security is the process of protecting data across its lifecycle and maintaining a strong commitment to data protection. 

What are the types of data security?

Data security involves multiple protective techniques to ensure data remains confidential, intact, and accessible only to authorized users. These security controls are critical for defending against threats that could steal data, corrupt data stored, or lead to large-scale data breaches. The following are the main types of data security every organization’s data strategy should include: 

1. Data Encryption – Converts readable data into an unreadable format to prevent unauthorized data access. It is one of the most effective data security measures
2. Data Masking – Obscures real data with fictional values, especially useful in non-production environments where sensitive data is lost or not needed
3. Access Control – Ensures only authorized users can gain access to sensitive data, enforcing security and data privacy at every access point
4. Authentication and Authorization – Verifies user identity and defines access rights based on roles or policies
5. Data Backup and Recovery – Creates secure copies of data stored and enables fast data recovery in the event of a breach or system failure
6. Physical Security – Protects physical infrastructure like the data center from unauthorized access, theft, or natural disasters
7. Data Erasure – Permanently deletes personal data of EU citizens and other sensitive records to meet compliance requirements
8. Network Security – Secures the systems and protocols that transmit data across internal and external networks
9. Endpoint Security – Protects devices like laptops, servers, and mobile devices that interact with data across multiple environments
10. Cloud Data Security – Secures data security in cloud computing environments using advanced monitoring, isolation, and control mechanisms
11. Security Monitoring and Incident Response – Enables the security team to scan for anomalies, detect potential data threats, and respond before damage occurs

Implementing all these types of security collectively results in a robust data security framework. With data security uses tools and technologies aligned to real-world risks, organizations can significantly reduce the cost of a data breach and strengthen long-term security and data protection. 

What Are the Most Recent and Major Data Security Breaches?

Here are 3 major, widely-covered recent data‑security events, both dated and located, with high impact and global attention: 

1. July 17, 2025 – Sydney, Australia (Qantas Airways)

Australia’s Qantas secured an interim injunction from the New South Wales Supreme Court to stop any third-party access or publication of data stolen in a cyberattack—one of the nation’s largest. The attackers compromised data belonging to 5.7 million customers, including names, emails, birthdates, FF IDs, phone numbers, and addresses. Importantly, no payment, passport, or financial account information was affected. Qantas has notified impacted customers and is working closely with the Australian Federal Police, National Cyber Security Coordinator, and ACSC to address the breach 

Why this matters: 

• Judicial action underscores the severity and scale of the breach. 
• Nearly 6 million customer profiles were exposed. 
• Aligns with broader cybercrime trends targeting airlines and loyalty data. 

2. July 16, 2025 – UK (Co‑op Retail Group)

The Co‑op confirmed in London that all 6.5 million of its members had their personal data—names, addresses, and contact info—compromised in an April cyberattack. Although no financial details like card numbers were leaked, the incident caused operational disruptions, including product shortages and a reversion to manual systems in funeral services. The UK’s National Crime Agency arrested four suspects (three teenagers), connected to the hacker group “Scattered Spider.” The Information Commissioner’s Office is advising affected members 

Why this matters: 

• A full-membership breach (6.5M individuals) is rare and highly alarming. 
• Highlights vulnerabilities in retail data frameworks and third-party systems. 
• Arrests show law enforcement’s proactive stance and scandalous social-engineering tactics. 

3. July 17, 2025 – Ahmedabad, India (Ahmedabad “digital leash” deployment) 

In a proactive move making headlines across banking, pharma, chemicals, FMCG, and IT sectors in Gujarat, businesses are instituting highly stringent internal controls—akin to a "digital leash"—to combat corporate espionage and unauthorized data exfiltration 

What are the most common threats to data security?

Understanding data security threats is essential to applying data security best practices that protect data through processes and tools. Without proper safeguards, these threats can lead to data breaches, resulting in reputational harm, regulatory penalties, and financial loss. The average cost of a data breach continues to rise, making proactive threat identification crucial. 

Below are the most common threats to data security: 

• Phishing and Social Engineering Attacks – Social Engineering tricks users into revealing login credentials or sensitive data using deceptive emails, websites, or messages
• Ransomware – Encrypts common types of data and demands payment for decryption, disrupting operations and access
• Insider Threats – Malicious or negligent actions by employees or contractors who already have access to systems and data cannot be ignored
• Advanced Persistent Threats (APTs) – Coordinated, long-term cyberattacks that infiltrate networks to extract valuable information
• Malware and Viruses – Software designed to damage, disrupt, or gain unauthorized access to data security tools or infrastructure
• Data Leakage – The unintentional exposure of data security and data privacy through unsecured channels, poor security practices, or misconfigurations
• Weak Access Controls – Inadequate restrictions on user privileges that allow unauthorized parties to access or modify data security is the practice
• Cloud Misconfiguration – Mistakes in cloud setup that expose systems and data cannot be sufficiently controlled
• Shadow IT – Use of unauthorized applications and devices that bypass formal security protocols

These threats highlight the importance of data protection and justify investment in preventive technologies. Following data security trends and staying compliant with relevant regulations are key benefits of data security for any organization. 

What are the best practices for ensuring data security?

Implementing data security best practices like encryption and RBAC aligns with industry standards such as ISO/IEC 27001 and NIST SP 800-53. For instance, a healthcare client of ours deployed DLP and IAM policies based on HIPAA security rules, achieving audit clearance in under 45 days. Organizations must take a proactive approach to comply with relevant data protection standards and industry-specific regulations. The following best practices are widely recognized as effective: 

• Classify and inventory data – Identify data like personal, financial, or intellectual property to apply appropriate security levels
• Apply the principle of least privilege – Limit access to data based on role-specific needs to reduce exposure
• Use strong authentication mechanisms – Enforce multi-factor authentication to prevent unauthorized access
• Encrypt data at rest and in transit – Ensure that intercepted data remains unreadable without decryption keys
• Regularly update and patch systems – Fix known vulnerabilities that attackers may exploit
• Conduct employee training and awareness programs – Equip staff with knowledge to recognize and respond to threats
• Implement continuous monitoring and incident response – Detect suspicious activities early and respond swiftly
• Back up data securely – Ensure recoverability in case of breach or data loss
• Conduct regular security audits – Evaluate controls against known threats and comply with relevant data standards
• Establish clear data handling procedures – Define how data like customer records or internal files should be processed and stored

An example of data security in action is an organization that uses data classification tools, enforces encryption, and applies role-based access controls as part of its comprehensive security policy. These best practices support long-term resilience and risk mitigation. 

How to identify and classify sensitive data?

Identifying and classifying sensitive data is the first step toward implementing effective security controls and ensuring regulatory compliance. Without proper classification, organizations risk mismanaging confidential information, which can lead to exposure or non-compliance. 

The process includes the following steps: 

• Define what constitutes sensitive data – This may include personal identifiers, financial information, intellectual property, and internal communications
• Use automated discovery tools – Leverage technologies integrated with SIEM (Security Information and Event Management) platforms to scan and locate data across systems
• Categorize data based on sensitivity and risk – Common classifications include public, internal, confidential, and restricted
• Label data accordingly – Apply metadata tags or labels to each data category to support enforcement of access policies
• Integrate classification with SOC tools – Ensure your SOC (Security Operations Center) uses classification inputs for threat detection and SOC audit full form compliance
• Coordinate with NOC and SOC teams – Align data classification procedures with both Network Operations Center (NOC) and SOC frameworks to maintain performance and security visibility. 

A properly structured classification model not only enables smarter access control but also strengthens incident response, particularly in environments where SOC responsibilities overlap with data governance efforts. 

What are the different data security tools and technologies?

Data security tools and technologies form the technical backbone of a well-structured security architecture. These solutions enable organizations to prevent, detect, and respond to threats targeting sensitive data, whether stored on-premises or in the cloud. A well-integrated toolset ensures consistent enforcement of policies, enhances visibility, and reduces the risk of data compromise. 

Key data security tools and technologies include: 

• Data Loss Prevention (DLP) – Monitors and prevents unauthorized sharing or transfer of sensitive information
• Encryption Tools – Secure data in transit and at rest using algorithms such as AES or RSA to prevent interception or unauthorized access
• Identity and Access Management (IAM) – IAM controls user access based on defined roles and enforces authentication protocols
• Security Information and Event Management (SIEM) – Collects and analyzes log data for real-time threat detection and compliance reporting
• Data Classification Tools – Automatically tag and categorize data based on sensitivity levels
• Endpoint Detection and Response (EDR) – EDR Monitors endpoints for suspicious activities and facilitates rapid incident response
• Database Activity Monitoring (DAM) – Tracks and logs activity on databases to detect policy violations and insider threats
• Backup and Recovery Solutions – Enable secure replication and restoration of data to ensure business continuity
• Data Masking Solutions – Hide sensitive data in non-production environments to reduce exposure during development or testing
• Cloud Access Security Brokers (CASBs) – Provide visibility and control over data used in cloud-based applications

When used collectively, these technologies establish a layered defense model that supports proactive risk management and regulatory compliance across the organization. 

What are the data security compliance regulations in India?

India's data security compliance is governed by several key regulations and frameworks designed to protect digital personal data and ensure cyber-resilience. As per the Digital Personal Data Protection Act (DPDP) 2023, organizations must report breaches within 72 hours—a requirement we’ve helped clients operationalize using automated log correlation and alerting frameworks tied to CERT-In guidelines: 

• Digital Personal Data Protection Act (DPDP Act, 2023
• Applies to processing of digital personal data, including that of foreign entities processing data of Indians.
• Establishes the Data Protection Board of India and mandates breach reporting within 72 hours
• Requires lawful, purpose-limited, and transparent processing, with data fiduciary accountability
• Information Technology Act, 2000 & IT Rules (2021
• Defines cybercrimes, digital signatures, and due diligence for intermediaries
• CERT‑In guidelines require logging, incident reporting within six hours, and standardized time‑sync
• Sector-specific guideline
• RBI mandates data localization and compliance with financial sector standards such as PCI DSS and encryption norm
• CERT‑In’s cybersecurity directions impose enhanced standards for ICT log retention and VPN provider obligation
• National Cyber Security Policy (2013
• Outlines broad principles for protecting critical infrastructure, strengthening incident response, and aligning with global security norms

Together, these regulations require organizations to implement security solutions, enforce access controls, report breaches, and uphold data privacy and data protection. Compliance ensures proper data security measures aligned with evolving regulatory demands. 

What is the difference between data privacy and data security?

In real-world deployments, we’ve seen that pairing EDR with SIEM analytics enables faster breach detection. For example, after implementing CrowdStrike + Splunk, a retail company was able to reduce mean time to detection (MTTD) by over 40%.  

Both are essential: data security ensures the protection of data, while data privacy ensures it's used responsibly and lawfully. 

How does VIEW enhance data security?

VIEW (Visibility, Insight, Enforcement, and Workflow) enhances data security by offering a structured and automated approach to understanding, managing, and protecting sensitive assets across complex environments. It enables security teams to move beyond basic detection and gain actionable intelligence in real time. Our SOC team operationalized the VIEW model for a telecom client, integrating it with Azure Sentinel and Zero Trust policies. This yielded faster triage times and audit compliance aligned with RBI’s ICT standards. 

Key ways VIEW strengthens data security include: 

• Visibility – VIEW provides centralized and continuous monitoring of data access, user behavior, and data flows, helping organizations identify exposure points across cloud, on-premises, and hybrid environments
• Insight – Integrated with SIEM platforms, VIEW contextualizes data events, correlates threat patterns, and prioritizes security alerts for accurate incident response
• Enforcement – VIEW allows MSSP-operated environments and internal SOC teams to implement automated policies that prevent unauthorized access, enforce least privilege, and detect violations in real time
• Workflow Automation – VIEW supports streamlined incident triage, response, and reporting, ensuring faster decision-making and policy enforcement aligned with compliance mandates

Used correctly, VIEW acts as a force multiplier for SOC operations and MSSP service delivery, enabling proactive threat mitigation, simplified auditing, and consistent policy execution across all endpoints and data layers. 

How can you integrate IAM with data center security?

Integrating Identity and Access Management (IAM) with data center security ensures that only authorized personnel can access physical and digital infrastructure. This integration strengthens both logical and physical controls, reduces insider threats, and improves audit readiness across environments handling sensitive data. 

Here’s how IAM can be effectively integrated into data center security: 

• Enforce Role-Based Access Control (RBAC) – Assign permissions based on user roles, limiting access to systems, network segments, or applications strictly on a need-to-know basis
• Use Multi-Factor Authentication (MFA) – Require MFA for both physical entry into data center facilities and remote access to critical systems
• Centralized Access Logs – Integrate IAM with SIEM or SOC-driven log management tools to monitor, record, and correlate access events in real time
• Automate Onboarding and Offboarding – IAM solutions can automatically grant or revoke physical and system access when an employee’s status changes, minimizing security gaps
• Policy Enforcement via Directory Services – Integrate IAM with directory services like Active Directory or LDAP to consistently apply group policies across server infrastructure
• Privileged Access Management (PAM) – Apply IAM controls specifically for administrators and critical systems, ensuring elevated access is monitored and time-bound

This integration provides end-to-end visibility and control over who accesses what, when, and how, making it a foundational component of secure, compliant, and resilient data center operations.