What is data security compliance?
Data security compliance is a critical aspect of data governance that involves adhering to the security-centric rules and regulations set forth by supervisory and regulatory bodies, including federal agencies. These rules and regulations cover a broad spectrum of data security, including technical and organizational security measures, data security tools and solutions, and data breach prevention techniques and strategies.
Data compliance vs. data security compliance
Many people confuse data security compliance with data compliance. While the fundamental principles of both areas of regulatory compliance are the same, there are important distinctions. It’s important to remember that data security compliance is a component of data compliance.
Data compliance includes laws and regulations that encompass the entire data lifecycle. This includes ingestion, storage, management, stewardship, discoverability, and transparency. In short, data compliance regulations cover what data businesses possess, where they get that data from, and what they do with it.
On the other hand, data security compliance zeroes in on security. While other aspects of compliance are also related to security, these laws and regulations specifically look at how well enterprises protect their data and, by extension, the customers and collaborators who share valuable and sensitive data with them.
Why does data security compliance is important to the business?
According to IBM’s Cost of a Data Breach 2023 report, failure to adhere to compliance regulations was one of the biggest amplifiers of data breach costs. Another influential data security compliance factor involves whether enterprises operate in environments with high or low levels of regulations. In low-regulation regions and sectors, businesses resolve 64% of compliance costs within the first year after a data breach. However, in high-regulation environments, costs increase by 58% after the first year.
Now let’s take a look at 6 critical reasons why data security compliance is of unparalleled importance:
Evolving threat landscape: The threat landscape has never been more perilous. According to The Independent, threat actors caused more than 290 million data leaks in 2023, and data breaches affected more than 364 million individuals. Trends and trajectories suggest that cybercrime will keep rising at staggering rates. Cybercriminals’ primary target is almost always enterprise data, making data security compliance increasingly complex and challenging.
Cloud compliance: Most businesses operate out of complex cloud environments. Some businesses self-host cloud environments while others use third-party IaaS, PaaS, and SaaS offerings from providers like AWS, GCP, Azure, and Alibaba. From a data security compliance standpoint, the main challenge lies in identifying intricacies of the shared responsibility model. (The shared responsibility model highlights what data security responsibilities lie with the cloud provider.) Furthermore, businesses using services from disparate cloud providers may have to navigate multiple data security-related cloud compliance challenges.
New laws and regulations: It’s challenging enough for businesses to comply with existing compliance regulations. Now, due to rising cybercrime and myriad industry-specific compliance catastrophes, regulators are going on the offensive and implementing new rules. Organizations have to understand when new rules begin to apply, how they overlap with existing rules, and what data security measures they must implement to satisfy growing compliance needs.
Complex data ecosystems: The complexity of data ecosystems depends on an enterprise’s scale, industry, and overarching objectives. Since enterprises are trying to unlock and use their data in new ways, data ecosystems are becoming more complex and convoluted. In complex data ecosystems, there’s a higher percentage of shadow data, which is data that falls outside the visibility and stewardship of IT and security departments. Complex data ecosystems can be a powerful launchpad for an organization’s data projects, but they also demand more data security compliance.
DevOps initiatives: Most businesses embrace agile and accelerated operational models and methodologies. Developers push software development lifecycles (SDLCs) into overdrive, increasing the speed with which they leverage data. Developing and implementing applications at speed and scale can potentially compromise data security and deprioritize regulatory compliance.
International data projects: As borders blur in the business world, new data security compliance challenges arise. For example, consider the data security compliance problems of a company based in Europe with primarily European customers that hosts its data in US-based data centers. In a case like this, the enterprise has to navigate the different, overlapping, and sometimes contradictory data security regulations of multiple countries, each with its unique approach to data security. Furthermore, geopolitical tensions and seismic political events can have major impacts on data security compliance for international data projects.
Real-life example: In April 2023, the Irish Data Protection Commission (DPC) fined Meta $1.3 billion for transferring the data of EU-based individuals to US-based servers.
8 data security regulations and standards
The following are 10 important regulations, industry standards, and compliance frameworks to keep in mind:
GDPR: The General Data Protection Regulation (GDPR) is EU legislation centered on information privacy.
SOX: The Sarbanes-Oxley Act (SOX) is a U.S. federal law for financial reporting.
PCI-DSS: The Payment Card Industry Data Security Standard (PCI-DSS) is a set of information security standards for U.S. credit card information. PCI-DSS compliance failures can cost companies anywhere from $5,000 to $100,000 per month.
CCPA: The California Consumer Privacy Act (CCPA) is a state statute to enhance and ensure consumer protection.
HIPAA: The Health Insurance Portability and Accountability Act (HIPAA) is a set of national standards to protect U.S. healthcare information.
FISMA: The Federal Information Security Modernization Act (FISMA) is legislation that includes frameworks for securing U.S. government data.
PIPEDA: The Personal Information Protection and Electronic Documents Act (PIPEDA) is a Canadian law centered on information privacy.
ISO/IEC: The International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) standards are a set of global standards to ensure information safety and optimize security management systems.
Practical steps to achieving data security compliance
1. Understand Regulatory Requirements
Identify applicable regulations: Based on your business sector and location, identify relevant regulations (e.g., GDPR, HIPAA, CCPA, PCI DSS) that dictate how data should be handled.
AI-specific regulations: If AI systems are in use, consider specific AI regulations, such as those addressing fairness, transparency, and bias in AI-driven decision-making. The EU’s AI Act is one example, mandating responsible use of data for AI models.
Align with security frameworks: Adopt recognized frameworks such as ISO 27001, NIST Cybersecurity Framework, or SOC 2 to standardize your approach to data security compliance.
2. Gain Data Visibility
Data discovery: Start by using data discovery tools to identify and map out all the sensitive and regulated data within your organization. Understanding where your data resides is a crucial first step in compliance efforts.
AI datasets: Include data used for AI models in the discovery process. Often, AI systems rely on extensive datasets for training and validation, which may contain sensitive information.
Automated data classification: Deploy tools that automatically classify data based on sensitivity (e.g., PII, financial data). This classification helps manage compliance by ensuring you can immediately identify and protect sensitive data.
Continuous visibility: Implement solutions like Data Security Posture Management (DSPM) to maintain ongoing visibility into your data environment, including AI training datasets and operational data flows.
3. Catalog and Manage Data
Data catalog implementation: Establish a data catalog to create an inventory of all data assets. This catalog should provide an organized, searchable index of datasets, including sensitive information and AI-specific data used in model training or decision-making.
Metadata management: Use metadata management to track data's origin, usage, and governance rules, especially for datasets feeding AI models. Ensuring you know where data came from and how it’s being processed is essential for regulatory compliance.
Tag sensitive data: Apply appropriate tags to data based on its sensitivity and regulatory requirements. This tagging should cover both traditional data and datasets used in AI workflows.
Automated updates: Ensure that the data catalog is dynamically updated as new data enters the system or is modified. For AI applications, this means keeping an updated record of all datasets, inputs, and outputs.
4. Track Data Lineage and Traceability
Data lineage tracking: Track how data moves within your systems from collection to processing and storage. This is essential for proving compliance and for auditing AI models, as it allows you to show how AI-driven decisions are made.
AI traceability: Ensure that AI models and their datasets are fully traceable, especially when used in compliance-critical sectors like finance or healthcare.
5. Implement Strong Data Encryption and Access Controls
Encryption: Encrypt data both at rest and in transit. For AI applications, ensure that training data and any outputs containing sensitive information are also encrypted.
Access control: Implement role-based access control (RBAC) and least privilege access to limit who can access sensitive data. For AI systems, ensure only authorized personnel can access training data, models, and outputs.
Multi-factor authentication (MFA): Use MFA for access to systems handling sensitive data, including AI tools or environments where models are trained.
6. Ensure Data Minimization and Anonymization
Data minimization: Collect and store only the minimum amount of data necessary for your operations. This principle applies to training AI models as well—use synthetic data or anonymized data wherever possible to minimize privacy risks.
Anonymization and pseudonymization: Apply anonymization techniques to remove personally identifiable information (PII) from datasets used in AI training or other data processes, ensuring compliance with privacy laws.
7. Implement AI-Specific Security Controls
Secure AI models: AI models can be vulnerable to adversarial attacks, where malicious inputs cause models to make incorrect decisions. Implement security measures to detect and prevent these attacks.
Data poisoning protection: Ensure that datasets used to train AI models are secured and monitored to prevent data poisoning attacks that can compromise AI outputs and lead to non-compliance.
Model governance and explainability: Ensure AI models used in sensitive areas such as healthcare or finance have mechanisms for explainability and auditability to maintain trust and compliance with regulatory standards.
8. Continuously Monitor Risk and Audit Data Activity
Implement continuous monitoring: Monitor data access, movement, and usage across systems in real time. Use cloud-native solutions like DSPM to gain visibility into data flows, including those involving AI data pipelines.
Audit AI models: Periodically audit AI systems, ensuring the integrity of the models and the data they rely on. Include audits of training data to ensure it meets security and compliance standards.
Data logging: Maintain detailed logs of data access and usage. For AI, ensure that logs are kept of who accessed training datasets, which models were trained on which datasets, and what decisions or outputs were generated.
Incident response plan: Have a well-defined incident response plan for addressing data breaches, including AI-specific data breaches. Ensure that the plan includes roles, responsibilities, and communication strategies.
How Wiz can help with data security compliance
Wiz’s DSPM solution allows enterprises to perform agentless scans of their entire data landscape, prioritize data risks based on deep and intricate contexts, and prevent data exposure by addressing toxic combinations and attack paths. Wiz extends its DSPM capabilities to encompass AI data, which is one of the most important security requirements in today’s AI-driven world.
With broad cloud coverage and compatibility with myriad cloud platforms and technologies, Wiz is the ultimate tool for comprehensive and unified data security. With Wiz’s DSPM tool, you can easily follow and adhere to the most complex and challenging data security and privacy laws.
Get a demo today to see how Wiz can protect your data and ensure compliance.
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