Big data security is a critical concern as organizations collect, store, and analyze large volumes of data. Digitization means that today all businesses (regardless of how big or small they are) have high volumes of varied data types. As you review and revise your attack surface management strategy it’s important to take big data security issues and risks into account.
Here are some of the primary data security risks associated with big data and steps that companies can take to mitigate them:
Unauthorized Access
Risk: Unauthorized users gaining access to sensitive data.
Mitigation:
- Implement strong access controls, authentication, and authorization mechanisms.
- Use encryption to protect data at rest and in transit.
- Regularly review and update access privileges.
Data Breaches
Risk: The exposure of sensitive data due to security breaches.
Mitigation:
- Employ intrusion detection and prevention systems.
- Encrypt sensitive data both in storage and during transmission.
- Conduct regular security audits and penetration testing to identify vulnerabilities.
Data Privacy Compliance
Risk: Violating data privacy regulations (e.g., GDPR, CCPA) when handling customer data.
Mitigation:
- Understand and comply with relevant data privacy laws.
- Implement data anonymization and pseudonymization techniques.
- Maintain clear data handling policies and provide employee training on data privacy.
Insider Threats
Risk: Malicious or careless employees or contractors accessing or mishandling data.
Mitigation:
- Monitor user activities and behavior.
- Implement role-based access control and conduct thorough background checks.
- Educate employees about security best practices and the consequences of data breaches.
Data Quality and Integrity
Risk: Inaccurate or tampered data affecting analytics and decision-making.
Mitigation:
- Establish data quality standards and validation processes.
- Use checksums and hashing to detect data tampering.
- Implement version control for critical data sets.
Scalability Challenges
Risk: Inadequate security measures as data volume grows.
Mitigation:
- Design security measures that can scale with the data.
- Use cloud-based solutions with built-in security features.
- Regularly review and update security policies.
Data Storage Risks
Risk: Data stored in various locations and formats may not be adequately protected.
Mitigation:
- Centralize data storage where possible and apply consistent security measures.
- Encrypt data at rest and use secure protocols for data transfers.
Third-Party Risks
Risk: Entrusting data to third-party vendors who may have weaker security measures.
Mitigation:
- Vet third-party providers thoroughly for security compliance.
- Sign robust data protection agreements and monitor their adherence to security standards.
Data Lifecycle Management
Risk: Inadequate management of data throughout its lifecycle.
Mitigation:
- Define clear data retention and disposal policies.
- Automate data archiving and deletion processes.
- Ensure that all copies of data are protected.
Lack of Security Awareness
Risk: Employees and stakeholders not fully aware of security best practices.
Mitigation:
- Provide ongoing security training and awareness programs.
- Encourage a culture of security within the organization.
To effectively mitigate these big data security risks, companies should also stay informed about emerging threats and continually adapt their security strategies to address new challenges in the ever-evolving threat landscape. Additionally, having an incident response plan in place can help minimize the impact of security incidents when they do occur. It's also important to remember that different countries and their states or provinces may have their own data protection laws that need to be considered when establishing data loss prevention best practices.
Applying best practice guidelines to mitigate big data security risks
Big data security is a big issue to tackle, but there are multiple best practice frameworks and guidelines you can apply to help mitigate big data security risks effectively. These frameworks provide structured approaches to developing and maintaining robust security measures. Here are the 10 most prominent ones:
- NIST Cybersecurity Framework—Developed by the National Institute of Standards and Technology (NIST), this framework provides a set of guidelines, standards, and best practices for managing and reducing cybersecurity risks, including those related to big data. It consists of five core functions: Identify, Protect, Detect, Respond, and Recover.
- ISO/IEC 27001—This international standard outlines a systematic approach to information security management. IT professionals can use ISO/IEC 27001 to establish, implement, maintain, and continually improve an Information Security Management System (ISMS) tailored to their organization's needs.
- CIS Critical Security Controls (CIS Controls) —The Center for Internet Security (CIS) offers a set of prioritized actions for enhancing an organization's cybersecurity posture. These controls cover a wide range of security areas, including data protection, and can be applied to big data environments.
- GDPR (General Data Protection Regulation) Compliance—For organizations dealing with data from European Union citizens, adhering to GDPR guidelines is essential. GDPR sets stringent requirements for data protection, privacy, and consent, which can serve as a robust framework for securing big data.
- HIPAA (Health Insurance Portability and Accountability Act)—If your organization handles healthcare-related big data, complying with HIPAA is crucial. It provides specific security standards and safeguards for protecting electronic protected health information (ePHI).
- CMMI (Capability Maturity Model Integration) —CMMI is a framework that focuses on process improvement and maturity assessment. Organizations can use it to assess and enhance their security processes and practices related to big data.
- Cloud Security Alliance (CSA) Security Guidance—For organizations leveraging cloud-based big data solutions, CSA provides comprehensive guidelines and best practices for ensuring security in cloud environments. Their Cloud Controls Matrix is particularly useful.
- Apache Ranger and Apache Sentry—These are open-source projects designed to manage and enforce authorization policies for Hadoop-based big data ecosystems. They can help IT professionals control access to sensitive data within the big data infrastructure.
- OWASP (Open Web Application Security Project) —While primarily focused on web application security, OWASP resources can be valuable for securing big data applications and ensuring that data is not exposed through vulnerabilities in web interfaces or APIs.
- Vendor-specific Guidelines—Many big data platform vendors, such as AWS, Microsoft Azure, and Google Cloud, provide their own security best practice guides and resources tailored to their platforms. IT professionals should consult these guides when working with specific cloud services.
It's important for IT professionals to tailor their security practices to their organization's unique requirements, industry regulations, and the specific big data technologies they use. The Cavelo platform can help map best practice benchmarks like NIST standards, the CIS (Center for Internet Security) Controls, and more.
Using automated data discovery to discover and classify big data
The first step to risk mitigation and data protection is data discovery. Automated data discovery helps you find, classify, and manage data across the organization, including big data stores. For example, the Cavelo platform helps you classify data by type and gain visibility into data vulnerabilities and data access, all with configurable policies.
Take a self-guided tour of the Cavelo platform today and see how it can help your team mitigate big data security risks.
