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Protecting your sensitive data is paramount in any machine learning project. Modal provides robust data encryption and access control mechanisms to ensure the security and privacy of your information, empowering you to build and manage your projects with confidence.

 Modal encrypts data stored at rest within its infrastructure to protect it from unauthorized access. Encryption keys are securely managed and separated from the data itself.

 All data transmitted between your Modal projects and other services is encrypted in transit using industry-standard protocols (e.g., HTTPS) to protect it from eavesdropping.

 Modal employs RBAC to restrict access to your projects based on user roles and permissions. You can define specific roles with varying levels of access, limiting who can view, modify, or delete sensitive data.

  You can set up project-level access controls to determine which users or teams can access specific projects. This ensures that only authorized individuals can work with your data and models.

   Modal allows you to grant fine-grained permissions to access specific resources within your projects (e.g., models, data files). This ensures a controlled and secure environment.

Key Benefits of Data Encryption and Access Control

 Protecting your data from unauthorized access, ensuring that only authorized individuals can view or modify it.

 Ensuring the integrity and authenticity of your data, preventing unauthorized modifications or data tampering.

  Meeting regulatory requirements for data security and privacy (e.g., GDPR), enhancing compliance and trust.

  Minimizing the risk of data breaches and other security incidents, safeguarding your valuable information. 

  Use strong and unique passwords for your Modal account and other services related to your projects.

  Enable 2FA for your Modal account to add an extra layer of security.

   Change your passwords at regular intervals to enhance security.

  Avoid sharing your account credentials with others. 

By leveraging Modal's robust data encryption and access control mechanisms, you can implement strong security measures to safeguard your machine learning data and build trust in your projects. This ensures the confidentiality, integrity, and availability of your information, even in the face of potential threats.

Understand how Modal encrypts data at rest and in transit, and provides granular access controls.

Data Encryption and Access Control

Modal is committed to adhering to industry-recognized compliance standards and certifications, demonstrating its commitment to protecting customer data, ensuring the security and privacy of your machine learning workflows.

 Modal undergoes regular audits to achieve SOC 2 Type II certification, demonstrating its compliance with rigorous standards for security, availability, processing integrity, confidentiality, and privacy.

 Modal complies with the General Data Protection Regulation (GDPR), a framework for protecting the personal data of individuals in the European Union.

 Modal may also have other compliance certifications, depending on the specific industry and regulatory requirements. Check Modal's website or contact their support team for the latest information.

 Modal uses robust encryption techniques to protect data at rest and in transit, ensuring the confidentiality and integrity of your information.

 Modal provides comprehensive access control mechanisms to restrict access to your data and projects based on user roles and permissions.

  Modal conducts regular security assessments and penetration testing to identify and mitigate potential vulnerabilities.

  Modal follows clear data deletion and retention policies to ensure that data is managed responsibly and disposed of securely when no longer needed.

  Compliance certifications demonstrate Modal's commitment to security and privacy, building trust with customers and partners.

  Meeting compliance standards helps Modal operate within legal and regulatory frameworks, such as GDPR and other data privacy regulations.

  By adhering to compliance standards, Modal reduces the risk of data breaches, security incidents, and legal liabilities.

Modal's compliance certifications and security standards are essential for organizations that require a secure and compliant platform for managing sensitive data and deploying machine learning models.  These certifications demonstrate Modal's commitment to protecting your data and ensuring a trustworthy and reliable environment for your machine learning work.

Learn about the compliance certifications and standards Modal adheres to, including SOC 2 and GDPR.

Compliance Certifications and Standards

Modal is committed to providing a secure and reliable platform for your machine learning workflows. This article outlines security best practices to ensure the protection of your data, code, and models within the Modal environment.

  Grant only the necessary permissions to users and systems, adhering to the principle of least privilege. This minimizes the potential impact of compromised accounts or unauthorized access.

  Keep your Modal platform, operating systems, and related tools up-to-date with the latest security patches to mitigate vulnerabilities and ensure a secure environment.

  Modal employs data encryption at rest and in transit to protect your sensitive data. Understand how Modal encrypts your data and leverage its security features to enhance protection. 

  Use strong and unique passwords for your Modal account and other services used in conjunction with Modal. Implement password managers to store and manage passwords securely.

 Enable 2FA for your Modal account to prevent unauthorized access, even if your password is compromised. 

 Configure granular access control policies for your Modal projects to restrict access to specific users and groups. Avoid sharing your account credentials.

 Configure network security groups to restrict incoming and outgoing traffic to your projects, limiting potential attack vectors.

  Implement secure coding practices to prevent vulnerabilities like cross-site scripting (XSS) or SQL injection. 

  Maintain regular backups of your data and models to prevent data loss in the event of a security incident or system failure.

  Only store and process data that is strictly necessary for your machine learning tasks.  

  Redact or disguise sensitive data before storing or processing it in Modal, whenever possible.

 Establish clear data retention policies to delete sensitive data when it's no longer needed.

  Ensure that your data handling practices comply with relevant regulations and privacy laws (e.g., GDPR, CCPA) to maintain compliance.

By adhering to Modal's security best practices and implementing robust security measures, you can mitigate risks, protect your valuable data and models, and build secure and reliable machine learning solutions.

Explore Modal's security policies, practices, and measures to ensure data privacy and security.

Security & Compliance

Data Encryption and Access Control

Compliance Certifications and Standards

Modal's Security Best Practices