What are the security requirements for generative AI?

Generative AI (GenAI) security requirements can be broadly categorized into three areas: data security, model safety, and overall system safeguards. Here’s a breakdown of each:

Data Security:

• Data Source Vetting: Source data for training GenAI models needs careful vetting. This includes checking for leaks, sensitive information, and adherence to data privacy regulations.
• Data Governance: Implement strong data governance practices. This involves data classification, access controls, and data retention policies to ensure sensitive information isn’t misused.
• Data Sanitization: Sanitize training data to remove any traces of Personally Identifiable Information (PII) or other confidential details before feeding it to the model.

Model Safety:

• Prompt Safety: Design safeguards to prevent malicious prompts that could manipulate the model into generating harmful content or leaking sensitive information. This may involve prompt scanning for suspicious keywords or patterns.
• Bias Detection and Mitigation: Continuously monitor GenAI outputs for potential biases inherited from the training data. Implement techniques like fairness metrics and bias mitigation training to address identified biases.
• Explainability and Transparency: Develop mechanisms to explain the reasoning behind the model’s outputs. This helps developers identify potential issues and users understand the limitations of the AI.

Overall System Safeguards:

• Access Controls: Implement robust access control mechanisms to restrict who can use GenAI tools and what data they have access to. This prevents unauthorized access and misuse.
• Monitoring and Auditing: Continuously monitor GenAI outputs for security risks, biases, and unexpected behavior. Regularly audit the training data and model performance to identify and address any emerging issues.
• Incident Response Plan: Develop a plan for how to respond to security incidents involving GenAI, including data breaches, model malfunctions, or malicious attacks.

Additionally, consider these points:

• Standardization: Emerging standards for GenAI safety, like the “Basic Safety Requirements for Generative Artificial Intelligence Services” (a draft from China), can provide a framework for secure development and deployment.
• Third-Party Providers: If using GenAI services from a third party, thoroughly vet their security practices and data handling policies.
• User Education: Educate users about the limitations and potential risks of GenAI tools. This empowers them to use the technology responsibly and avoid accidentally exposing sensitive information.

By implementing these security requirements, organizations can leverage the power of GenAI while mitigating the associated risks and building trust with users.