Organizations frequently rely on sharing data with third parties to support business operations and drive growth. However, this practice can introduce inherent risks and challenges, particularly concerning data security. To help protect sensitive information, maintain regulatory compliance, and safeguard reputation, security architects and database administrators (DBAs) must establish resilient processes for secure third-party collaboration.

Managing the risks and requirements of third-party data sharing

Sharing information with external partners can expose organizations to wide-ranging risks—from data breaches and unauthorized access to compliance violations and data sovereignty concerns. The ramifications of these risks can be significant, including financial loss, reputational damage, and regulatory penalties.

To address these challenges, organizations must prioritize risk assessment and management. This involves conducting due diligence on third-party vendors, evaluating their security posture, and implementing contractual agreements to help ensure compliance with data protection requirements. By pooling their expertise, security architects and DBAs can identify vulnerabilities, assess potential risks, and formulate effective risk mitigation strategies.

Establishing a secure third-party data sharing framework

Before initiating any data sharing initiative, organizations must assess their specific requirements and objectives. This includes understanding data ownership, identifying data types and sensitivity, evaluating legal and regulatory obligations, and defining access and authorization requirements.

By conducting a thorough assessment of each data sharing initiative, security architects and DBAs can establish tailored and secure processes for engaging with third-party partners. This proactive approach helps ensure that data sharing initiatives align with security objectives and regulatory obligations and can mitigate risks while fostering a culture of trust and compliance.

Three best practices for establishing a secure data sharing framework are:

Define purpose and scope: Clearly define the purpose and scope of the data sharing initiative. Identify the specific data elements that need to be shared and the intended use of that data by the third party.
Understand data ownership and legal obligations: Identify the stakeholders who own and have access to the data and define appropriate authorization levels. Review legal agreements, contracts, and regulations to understand the specific obligations and restrictions surrounding data sharing. Familiarize yourself with relevant data protection regulations, such as GDPR or CCPA, and ensure data sharing practices align with these requirements.
Assess data flow and transfer methods: Evaluate how data flows within your organization and to external parties. Identify the data transfer methods employed, such as APIs, file transfers, or database replication, and assess their security implications. Implement role-based access controls (RBAC) to ensure that only authorized individuals can access sensitive information. Before data leaves your environment, understand its context: what it contains and how it connects to risk, policy, identity, and business processes.

Mitigating risks and ensuring trust in third-party partnerships

After assessing data sharing requirements, organizations should identify and mitigate the risks associated with sharing data with third parties to address potential vulnerabilities and select reliable partners. Effective risk mitigation includes conducting due diligence on potential partners, establishing strong contractual agreements, implementing data anonymization techniques, and monitoring data sharing activities.

Three best practices for mitigating risks in third-party partnerships are:

Establish robust vendor due diligence processes: Implement thorough due diligence processes when selecting and engaging with third-party vendors. Evaluate their security practices, track record, and compliance with regulations. Verify their data protection capabilities, incident response procedures, and overall security posture to ensure they have adequate security measures in place to protect shared data.
Implement strong contractual agreements: Develop and enforce robust contractual agreements with third-party partners that clearly outline data protection responsibilities, obligations, and expectations. Establishing strong contractual agreements helps mitigate risks by ensuring that both parties understand their obligations and are accountable for maintaining the security and confidentiality of shared data.
Conduct regular risk assessments: Regularly assess and evaluate the potential risks associated with data sharing activities. Identify specific risks, such as unauthorized access, data breaches, or regulatory non-compliance, which may arise from sharing data with third-party partners.

Conducting due diligence on third-party partners

Before engaging in data sharing, organizations must conduct thorough due diligence to evaluate potential partners based on their security posture, data protection policies and practices, certifications, compliance with regulations, and the ability to handle security incidents effectively. Prioritize partners with a strong commitment to data privacy and robust security measures.

Three best practices for conducting third-party due diligence are:

Assess security practices and certifications: Evaluate the partner’s security practices and certifications (ISO 27001 or SOC 2) and data protection policies. Review access controls, encryption measures, and incident response plans.
Evaluate compliance and privacy practices: Assess the partner’s compliance with relevant regulations and industry standards. Verify their privacy practices, data handling procedures, and mechanisms for managing data subject requests.
Review incident response capabilities: Assess the partner’s ability to detect, respond to, and recover from security incidents. Ensure their incident response plan aligns with your organization’s expectations and requirements for minimizing impact and responding swiftly.

Establishing strong data protection measures with data classification

Implementing a data classification framework is crucial to secure third-party data sharing. Before data reaches a partner, you need to know what sensitive data you have, what it means, and what risk it carries. A data classification framework can help prioritize data protection, apply appropriate security controls, and manage those risks. By categorizing data based on sensitivity, importance, and compliance requirements, granular access controls, encryption mechanisms, and incident response procedures can be implemented. Data classification can support compliance, minimize data exposure, facilitate contractual agreements, and enhance incident response and recovery.

Collaboration between security architects and DBAs is key to defining a comprehensive data classification scheme that helps ensure adequate protection of sensitive data shared with third parties throughout the data lifecycle.

Three best practices to establish a strong foundation for data classification are:

Identify data categories: Categorize the types of data your organization handles (for example, PII, PHI, financial data, and intellectual property). Automated classification can identify more than 70 sensitive data types across the estate, helping provide a clearer understanding of the data landscape and enabling more focused protection measures.
Determine sensitivity levels: Assess the sensitivity of each data category and assign appropriate levels (for example, high, medium, and low), considering potential impact and the need for data anonymization. This enables more targeted security controls based on data sensitivity.
Define access controls: Implement granular access controls based on data classification levels. Use RBAC and least privilege principles to restrict access to sensitive data, allowing only authorized individuals or systems to access specific data sets. This can help minimize the risk of unauthorized access or data leakage during third-party data sharing.

Implementing access controls and encryption

To help ensure secure third-party data sharing, prioritize granular access controls and encryption mechanisms. Adopt the least privilege model to restrict access to necessary data for each role. Enhance security with strong authentication methods like multi-factor authentication. Protect data in transit and at rest through end-to-end encryption and tokenization. Strong encryption algorithms and robust key management practices help keep compromised data secure and unreadable to unauthorized parties. These measures can help uphold data integrity and confidentiality, even in the face of potential breaches.

Three best practices for strong access controls and encryption mechanisms are:

Role-based access controls (RBAC): Implement RBAC to grant authorized individuals access to specific data sets based on their roles and responsibilities. Regularly review and update access privileges to align with changing requirements.
Data encryption: Apply encryption techniques at rest and in transit to help protect the confidentiality of shared data. Use hardware security modules or key management systems to help safeguard encryption keys.
Multi-factor authentication (MFA): Enforce MFA for accessing shared data systems or platforms. MFA adds an additional layer of security by requiring users to provide multiple factors, such as a password and a one-time verification code.

Staying vigilant through monitoring and auditing

Continuous monitoring and audits are crucial for maintaining data security in third-party collaborations. Security architects and DBAs should establish logging systems, utilize real-time monitoring tools, leverage automation and analytics, and regularly review logs to detect anomalies, identify threats, and ensure compliance. Proactive monitoring can support swift response to emerging threats and help safeguard valuable data assets.

Three best practices to effectively monitor and audit data sharing activities are:

Comprehensive logging and monitoring: Track data sharing activities, including access attempts and modifications. Monitor network traffic, system logs, and user activities to detect suspicious behavior. Regularly review collected data for potential security incidents, and continuously detect sensitive data sprawl and policy drift as the data estate evolves.
Regular security assessments and audits: Evaluate the effectiveness of data sharing controls, including access policies and encryption measures. Assess compliance with regulations. Conduct assessments and audits through independent third parties or internal security teams.
Strong data governance and accountability: Define roles, responsibilities, and policies for data sharing. Enforce procedures for access privileges, data handling, and incident response. Monitor compliance and raise awareness among employees and third-party partners. Connect policies to existing enforcement tools through open APIs so governance actions can happen automatically.

Securing third-party data sharing: Building trust and safeguarding confidentiality

In today’s era of extensive third-party collaborations, prioritizing the security of shared data is essential to maintaining trust, protecting sensitive information, and preserving reputation. By adhering to the recommended best practices outlined in this guide, security architects and DBAs can establish a robust framework for secure third-party data sharing. Thorough assessment, diligent risk mitigation, careful partner selection, granular data classification, strong data protection measures, and vigilant monitoring are key to navigating the complex landscape of data sharing with confidence.

Empowering data protection with Everpure Data Intelligence

As third-party collaboration becomes more widespread, organizations can leverage Everpure Data Intelligence to help strengthen their data protection practices. The data intelligence platform continuously discovers, classifies, and contextualizes sensitive data across cloud, on-premises, and mainframe environments, helping you understand what you’re sharing, with whom, and what risk it carries. With a semantic knowledge graph and a built-in policy engine, Everpure Data Intelligence helps security architects and DBAs apply data protection policies, identify anomalies, and respond to potential breaches more quickly. By harnessing Everpure Data Intelligence, organizations can enhance their data security posture and more confidently engage in secure third-party data sharing.

By implementing the best practices discussed in this blog post and utilizing Everpure Data Intelligence, security architects and DBAs can strengthen data security, help ensure regulatory compliance, and foster strong relationships with third-party partners.