Data Security: Meaning, Importance, Best Practices, and Solutions

Statistics project data to exceed 180 zettabytes by 2025, emphasizing its critical role in innovations and developments globally. The exponential growth of data can also be linked to the growing adoption of multi-cloud environments, which allows businesses to reduce storage costs, improve scalability, and streamline operations.

With the emergence of AI models like GPT or Bard, data has found a whole new set of use cases. AI models require feeding on massive volumes of unstructured data to train themselves for purpose-built AI applications or fine-tune the models for speed, efficiency, and accuracy.

However, as data grows, so does the threat landscape. As a result, data is exposed to many existing and emerging risks.

Consider a medium-sized technology company that relies heavily on its internal databases to manage customer information, proprietary software code, and strategic business plans. The company employs a number of IT administrators and developers who have elevated access privileges to these sensitive systems. Recently, it was discovered that one of the senior developers abused their privileged access to extract proprietary code and customer data without authorization. This unauthorized access went undetected for several months, leading to significant intellectual property theft and potential exposure of sensitive customer information. Complicating the situation, the developer had connections with a competing firm, increasing the risk of corporate espionage and further misuse of the stolen data.

Such security gaps could lead to sensitive data exposure, intellectual property theft, compliance violations, and compromised AI models, which is a concerning scenario.

Data security is essential for equipping security teams with the necessary l insights, tools, strategies, and best practice frameworks to protect their organization’s most valuable asset– data.

What is Data Security?

Data security protects an organization's data against unauthorized access, loss, theft, or destruction.

Gartner and NIST’s National Cybersecurity Center of Excellence (NCCoE) define data security as a set of best practices, processes, and tools that help protect data's integrity, confidentiality, and availability at rest and in transit.

Data Integrity

Businesses need reliable, evidence-based data to make informed decisions. Data must be reliable and accurate to achieve that. Data security measures allow businesses to validate the integrity of the data, ensuring it is not compromised or corrupted at any point in its lifecycle.

Confidentiality

The concept of confidentiality in data security means that businesses must ensure that the data is accessible to only authorized users. Implementing the principle of least privilege access across an organization is one such practice that allows for better confidentiality and leads to integrity.

Availability

While data confidentiality is critical for ensuring data protection and integrity, it shouldn’t prevent data from being available to the right users when needed. The security measures taken to ensure the confidentiality of the data should also ensure that the data is accessible to authorized users without any system or administrative delays.

Data security comprises a broad spectrum of information security measures, ranging from sensitive data insights to risk identification, appropriate access policies and controls, optimal configuration settings, and data encryption or masking.

Why is Data Security Important?

With the wide adoption of multi-cloud environments and, lately, GenAI, data security has become increasingly paramount for businesses of all sizes.

The 2023 Annual Data Breach Report by the Identity Theft Resource Center (ITRC) revealed that data compromises soared by 3,205 in 2023, a whopping 78% jump compared to 1,801 cases tracked in 2022. Data breaches continue to rise as businesses are exposed to a wider attack surface than a decade ago. Data security measures equip organizations with the insights and controls to identify security gaps, remediate risks, and prevent breaches.

Businesses must comply with various data protection regulations and industry-specific standards, such as EU GDPR, CPRA, HIPAA, NIST, SOC, etc. These regulations require businesses to follow strict security and administrative measures for enhanced data protection. Businesses can demonstrate their commitment to protecting customer data with a robust data governance program.

Data security is also paramount in enabling organizations to adopt AI safely. As mentioned earlier, LLMs are trained or fine-tuned on high volumes of data, especially unstructured data. This data may contain sensitive information, which is susceptible to exposure if not extracted, sanitized, or masked. Also, it requires strict controls to prevent unauthorized access or data compromise, such as data entitlements, masking, redaction, etc.

Data needs to be accurate, accessible, and useful to various business units across an organization. However, as organizations face challenges with data security and related obligations, only 32% of an organization's data remains useful for making informed decisions. Data security helps organizations make informed business decisions, increase agility, and foster stronger alignment with business leaders.

Data Security vs. Data Privacy

Data security and privacy are widely used terms that often get confused or mixed up. Though data privacy is an integral component of data security strategy, its primary purpose is to ensure ethical and responsible use of data.

Data privacy laws, such as the EU GDPR, CPRA, HIPAA, or GLBA, provide provisions that govern how data is collected, stored, processed, shared, and sold. The provisions include obtaining consent from customers, their privacy rights, risk assessments, and data protection measures, to name a few.

Data security lays the groundwork for compliance with data privacy laws by providing insights into personal and sensitive data, associated risks, and controls to mitigate those risks. It also prevents unauthorized access and consequent data breaches.

Data Security Challenges

Protecting data while ensuring operational efficiency has never been straightforward. From diverse, complex data environments and a growing threat landscape to ever-increasing redundant, obsolete, or trivial (ROT) data and siloed data protection measures, the road to robust data security is barred with challenges.

Let’s examine some of the top operational challenges that hinder organizations’ ability to protect their data.

Lack of Data Visibility

Depending on the industry, 40% to 90% of data is dark. Organizations collect, duplicate, or generate volumes of data throughout the year but use only a fraction of it. This might be the data once stored in legacy systems but inadvertently moved to the cloud during migration. As a result, it failed to show up in the cloud console. Some data might be initially collected for regular business activities but later forgotten. As dark or shadow data is unknown to the IT, it is never scanned for risks, sanitized, or governed.

When organizations don’t know what data lives in their environment since they can’t see or access it, it becomes challenging to establish appropriate policies or security controls to protect the data.

Growing Security Threats

The data security threat landscape has significantly transformed over the last few years. It is continuing to do so at this very moment. Malicious actors increasingly adapt to new technologies and exploit innovative methods to circumvent systems, such as AI or Machine Learning (ML) tactics. Similarly, insider threats and third-party data breaches reign among the top concerns in the cybersecurity space. To top it all off, the advent of GenAI has introduced some unique threats to an already evolving landscape. AI toxicity, hallucination, excessive agencies, and sensitive data exposure are some of the prevailing threats unique to LLMs.

Organizations must continuously evolve and adapt new data security measures and tools to overcome this challenge.

Regulatory Compliance

Managing compliance risk is another challenge that hinders security teams' ability to operationalize data security. Almost every country has introduced data privacy and protection laws to govern safe and responsible data management. Most of these laws have overlapping statutes and requirements, making compliance with different jurisdictions difficult. With AI laws, such as the EU AI Act, to the mix, security teams are challenged to safeguard data and ensure that their security measures align with industry laws and frameworks.

Redundant, Obsolete & Trivial (ROT) Data

Data is the crown jewel of innovation and growth. However, over-collection and over-retention of redundant, obsolete, and trivial (ROT) data may put an organization at serious security risks, such as unauthorized access, sensitive data exposure, data loss, etc. Studies reveal that organizations spend as much as $34 million on ROT data that could safely be deleted. Retaining ROT data is not only a security risk but also a regulatory risk that could result in legal penalties and a tarnished business reputation.

With data spread across multiple environments, including on-premise, SaaS, and cloud resources, tracking ROT data and implementing retention policies and related controls has become challenging.

Siloed Security Approach

Imagine a group of firefighters stationed on different floors of the same building. All the firefighters are giving their all to put out flames on their respective floors. However, due to no means of communication, their coordination is disconnected. As a result, the flames continue to spread. This is akin to data security silos. When organizations use different data security tools in silos with little to no integration among these tools, cybersecurity risks tend to arise. The inefficient integration between these tools limits their ability to understand the context of the data or the related threat. This lack of coordination results in inconsistent data security policies or controls and increases the likelihood of data breaches.

Data Security & Compliance Risks

Organizations face several security and compliance risks while protecting sensitive data in their environments. Let’s examine some of the most common risks.

Overprivileged Access

Overprivileged access means the users or applications have gained more permissions than necessary for the job. Overprivileged access is a highly concerning security risk as it could lead to third-party and internal attacks. To put things into perspective, 80% of breaches involve compromised identities. Other statistics reveal that 99% of cloud identities are overprivileged. Organizations must shift to a context-aware, automated approach to data access governance to overcome this risk. It involves having complete visibility of sensitive data, what users or roles have access to the data, their permissions, and the mechanism to implement appropriate access controls.

Security Misconfigurations

Configuration risks occur when system security settings aren’t configured correctly or implemented due to negligence. Misconfigurations are the third most common attack vector, accounting for 11% of attacks. Many common configuration issues may lead to data breaches. For instance, unencrypted or weakly poorly encrypted datasets create enough opportunities for hackers to steal or modify sensitive data. Similarly, the lack of multiple-factor authentication (MFA) may lead to unauthorized access to sensitive data. Clear visibility of data security risks across different environments and unified security controls enable organizations to prioritize remediation efforts efficiently.

OWASP Top 10 Risks for LLMs

On one end, large language models (LLMs) have accelerated faster prototyping and the development of GenAI applications, such as AI chatbots. On the other hand, they have also introduced some unique security risks to data used in GenAI pipelines. In 2023, the OWASP Top 10 Risks for LLMs identified 43 unique threats and narrowed them down to the ten most critical risks. Take, for instance, training data poisoning, where threat actors may feed the LLM harmful data, forcing it to generate unethical output. Similarly, sensitive data exposure is another critical risk when the data used to train the LLM contains sensitive data without proper redaction or masking.

Data Retention

Businesses collect and store volumes of data throughout the year. However, most of this data is not processed or analyzed but forgotten. Not knowing what data they have and what regulatory provisions apply to them could result in compliance violations. Without proper data classification and data mapping, businesses may not know the retention laws apply to the data that lives across their environment. Consequently, over retention of data will not only lead to potential data breaches but also regulatory fines.

Best Practices for Optimal Data Security

Now the question is what best practices organizations should consider to enhance the security posture of their data, better mitigate various security risks, and protect themselves against compliance breaches.

Discover and Classify Data

Data security begins with gaining data visibility, its types, and access information. Security teams must leverage an AI-powered solution that provides intelligence across cloud-native and shadow data assets. The tool should further be capable of identifying different formats, including structured and unstructured data. From there, the data should be properly categorized using an efficient classification process. The classification engine should leverage various classification methods to increase accuracy, such as Named Entity Recognition (NER), Natural Language Processing (NLP), or out-of-the-box classifiers. Data can be classified into public, private, confidential, and sensitive depending on the organization's needs.

Implement Least-Privileged Access

Intellectual property (IP) information or data subject to regulations must be accessible only to authorized users. Security teams must gain complete intelligence into sensitive data access across their data landscape, including who has access to it and how it is accessed. It further includes continuous monitoring for abnormal access patterns or inactive users. Security teams must first understand which users across different departments truly need the access.

Conduct Continuous Risk Assessment

Regularly evaluate your data security and compliance posture through automated risk assessments. These assessments give businesses complete visibility of security gaps and risks across their security and compliance posture, allowing them to fix the gaps before it is too late. Risks can be given different scores or ratings that enable teams to prioritize remediation based on the understanding of the sensitivity. By focusing on the most critical vulnerabilities, organizations can allocate resources efficiently and implement timely remediation actions to prevent incidents or reduce their impact.

Map Data Flows

Mapping data flows is one of the most essential components of data security and privacy. It helps visualize how data moves externally or internally across an organization, allowing for an accurate assessment of its transformation. By understanding the source, destination, scope, purpose, or context of data processing, teams can identify gaps in an organization’s data privacy and security efforts.

Implement  AI & Data Security Controls

Unstructured data is the black gold for GenAI applications. However, using unstructured data for LLMs opens it to a whole new set of security and compliance risks. Security teams must build their data security strategy while considering the safe use of data for AI agents or applications. To achieve that, data quality should be maintained by ensuring the data isn’t duplicated or outdated. Sensitive data should be masked or redacted before it is used for GenAI applications. Similarly, entitlements of unstructured data should be preserved to ensure only authorized users have access to GenAI prompts.

Data Compliance Regulations

While discussing data security, it is also crucial to understand data compliance. Compliance demonstrates how well a business treats the security and privacy of the sensitive data collected from users or customers. Compliance necessitates appropriate data classification, data retention, and risk management, ensuring an organization’s security measures meet legal requirements. Let’s take a look at some of the most recognized data protection laws.

General Data Protection Regulation (GDPR)

General Data Protection Regulation (GDPR) is amongst the most comprehensive laws. The law has inspired other nations across the globe to formulate their regional data protection laws, such as the California Privacy Rights Act (CPRA) in the US. The law protects the data and privacy rights of European Union (EU) data subjects. The law requires organizations to consider various security controls to ensure compliance with the regulations. For instance, the law holds the controller and processor responsible for personal data loss. Hence, businesses must set up technical safeguards to prevent data loss or breach, such as a data loss protection (DLP) tool or a more refined data security posture management (DSPM) solution. Similarly, preventing data from unauthorized access is another security measure required under the GDPR.

Health Insurance Portability and Accountability Act (HIPAA)

The Health Insurance Portability and Accountability Act (HIPAA) is a federal regulation in the United States. The act applies to entities that collect and process a patient's medical records or protected health information (PHI), including hospitals, health plan providers, and insurance services. The act is categorized into different categories, including the Privacy Rule, Safeguard Rule, Breach Notification Rule, and Omni Bus Rule. As a part of compliance, covered entities must assess the likelihood and impact of potential risks to PHI. Appropriate security measures should be taken to address those identified risks. The entity must implement effective policies to control access to PHI to prevent unauthorized access.

Payment Card Industry Data Security Standard (PCI DSS)

The Payment Card Industry Data Security Standard (PCI DSS) is a security standard that provides strategic guidelines for helping covered entities protect customers' payment card data. Among its key objectives, PCI DSS requires covered entities to protect sensitive cardholder data and prevent financial fraud and data breaches. As part of PCI DSS compliance, covered entities must ensure that they adhere to the 12 critical requirements of the regulation. For instance, covered entities must implement effective network security controls, such as firewall installation or intrusion detection systems, to prevent data breaches. Appropriate security configurations should be applied across all sensitive system components. Apply the least privilege access to limit users' access to critical systems and cardholder data.

NIST Cybersecurity Framework

The National Institute of Standards and Technology (NIST) is an agency operating under the U.S. Department of Commerce. Its role is to help federal agencies and other businesses understand, manage, identify, and mitigate risks to sensitive networks or data. The NIST Cybersecurity Framework is one such endeavor that provides a set of strategic guidelines to achieve those objectives. For instance, the latest framework, 2.0, requires businesses to conduct risk assessments, identify those risks, and create an action plan to reduce those risks. Furthermore, businesses should implement strong authentication measures to safeguard data in transit. Security teams must also implement strict access controls to prevent data loss.

EU AI Act

The European Union's (EU) AI Act offers a legal framework to entities that develop and use AI systems and applications, enabling them to ensure their safe development, deployment, and usage. The framework necessitates that the AI systems be classified based on risk profiles. The act further establishes strategic guidelines for high-risk AI applications, enabling organizations to ensure responsible and ethical use. Article 10 of the EU AI Act requires businesses to ensure robust data governance for training, validation, and testing datasets. Among other requirements, businesses must make sure that the datasets are error-free and contextually accurate to prevent biased responses.

The advent of AI has brought a wide array of unprecedented risks. As a result, governments worldwide are proposing AI regulations and frameworks to govern data and AI to enable its safe use. Amongst the most prominent AI laws and standards are the EU AI Act, the Executive Order on the Safe, Secure & Trustworthy Development and Use of AI, and Canada’s Artificial Intelligence and Data Act (AIDA).

The Advanced Data Security Solutions

The types of data security solutions vary depending on an organization's scope and needs. A well-rounded data security approach safeguards an organization’s data landscape and ensures regulatory compliance and customer trust. The following are critical data security capabilities that should be part of your overall data governance program.

Data Security Posture Management (DSPM)

Data Security Posture Management (DSPM) provides a holistic picture of an organization’s data security landscape through comprehensive visibility into its sensitive data, access patterns, and risk ratings. A robust DSPM includes the following capabilities.

Data Discovery & Classification

The solution scans the cloud environment to discover native and shadow data assets. The discovery engine also automates locating structured and unstructured data assets, providing a holistic view of the data landscape. The tool classifies the data according to sensitivity, regulatory requirements, and other business relevance. For instance, the data may be labeled according to the data elements, such as financial data, intellectual property data, or business data. Similarly, the data may also be labeled according to applicable regulations like PCI DSS, GDPR, CPRA, etc.

Data Access Intelligence & Governance

DSPM also monitors and tracks granular insights into sensitive data access and usage. The data access intelligence provides security teams with relevant insights, allowing them to set up appropriate access policies and controls. By leveraging the same insights, governance teams can efficiently implement a least privileged access model.

Configuration Risk Management

DSPM helps organizations carry out effective risk assessments. This is achieved by continuously monitoring data environments against various vulnerabilities like misconfiguration or unauthorized access. By assigning risk scores, DSPM helps security teams prioritize remediation effectively.

Data Lineage Tracking

Data transformation occurs at different instances of data lifecycle, i.e., from its ingestion to deletion. DSPM tools provide organizations the ability to track this transformation across the data lifecycle, providing better visibility into its accuracy, integrity, and confidentiality.

ROT Data Minimization

Statistics suggest that 40% to 90% of the data is dark. This dark data often contains redundant, obsolete, and trivial (ROT) data that is not tracked or monitored by IT teams. This data may present a huge security risk due to lack of governance and security guardrails. An organization’s data security stack should include ROT data minimization policies and controls to prevent security, regulatory, and operational risks. These policies should cover duplicate data identification, data retention, data deletion, delegation and quarantine.

AI Security & Governance

Globally, there's an increased adoption of GenAI across various sectors. Providing the significant transformative abilities of this emerging technology, businesses must enable comprehensive visibility and establish controls to ensure the safe use of GenAI. This can be achieved using a robust AI Security & Governance solution, providing a framework to organizations that can help them get complete visibility of AI use, relevant risks, and pertinent controls to ensure the safe use of data and AI.

Compliance Management

When it comes to data compliance, it is traditionally seen that businesses often rely on manual processes that are error-prone, time-consuming, and costly. To further complicate matters, AI governance laws are also on the rise, requiring organizations to embrace effective safeguards to demonstrate compliance. An automated compliance management tool can streamline compliance with data and AI laws using standardized templates, tests and controls.

Breach Management

A data breach prevention and response management system is also a critical component of your data security tech stack. It provides a comprehensive view of the data breach radius, the impacted identities, financial impact, and regulatory obligations. The tool should further enable breach notification automation, a critical requirement of most data protection and privacy laws.

Protect Your Data & AI Everywhere with Securiti

Securiti Data+AI Command Center simplifies security for data and AI everywhere, including on-premise data stores, SaaS applications, AI models, and cloud environments. The platform replaces the piecemeal approach to data security by unifying all the key capabilities under one window, including but not limited to data discovery and classification, data lineage, access governance and control, security posture management, compliance management, data minimization, and AI security & governance.

Check out a quick demo video to learn more about how Securiti can help you streamline data security.

Frequently Asked Questions (FAQs) about Data Security