Create a checklist for AI Security including Generative and Predictive AI
1. Governance and Compliance
2. Risk Assessment
- Gather relevant documentation on AI systems.
- Identify stakeholders and involve them in the assessment.
- Define the scope of the risk assessment.
- Use a structured framework for evaluation (e.g., NIST, ISO).
- Review historical data on AI vulnerabilities.
- Conduct brainstorming sessions with experts.
- Analyze attack vectors specific to AI models.
- Consider both internal and external threat sources.
- Create a risk matrix to categorize risks.
- Assess potential consequences of each risk.
- Determine the probability of occurrence for each threat.
- Document findings for transparency and future reference.
- Rank risks according to their assessed impact and likelihood.
- Identify appropriate mitigation strategies for high-priority risks.
- Allocate resources for implementing mitigation plans.
- Establish timelines for monitoring and reassessing risks.
3. Data Security
- Utilize secure servers for storage.
- Apply strict access controls.
- Regularly update security protocols.
- Use secure file transfer methods.
- Monitor and log access activities.
- Use strong encryption algorithms.
- Encrypt sensitive data at rest.
- Secure data transmission with TLS/SSL.
- Regularly update encryption keys.
- Ensure compliance with encryption standards.
- Define user roles and permissions.
- Implement multi-factor authentication.
- Regularly review access logs.
- Limit access based on necessity.
- Train users on access policies.
- Conduct periodic data quality assessments.
- Verify data source reliability.
- Implement checks for data integrity.
- Document audit findings.
- Address any identified issues promptly.
- Use data masking methods.
- Remove personally identifiable information.
- Apply tokenization for sensitive data.
- Ensure anonymization does not compromise usability.
- Review regulations regarding anonymization.
4. Model Security
- Follow established coding standards.
- Implement input validation and sanitization.
- Use secure libraries and frameworks.
- Conduct peer reviews of code.
- Document security considerations in the development process.
- Establish a schedule for updates.
- Keep inventory of all software and libraries.
- Monitor for security advisories and patches.
- Test updates in a staging environment.
- Deploy updates promptly to production.
- Implement logging and monitoring tools.
- Analyze model outputs for unusual patterns.
- Conduct periodic vulnerability assessments.
- Engage in red teaming exercises.
- Stay informed about emerging attack vectors.
- Adopt a version control system for models.
- Document changes and reasons for each version.
- Ensure easy access to previous model versions.
- Test rollback procedures regularly.
- Train team on versioning protocols.
- Define performance metrics and benchmarks.
- Schedule routine performance evaluations.
- Use automated tools for anomaly detection.
- Review results with the team.
- Adjust models based on assessment findings.
5. Access Control
- Define access levels for users based on roles.
- Restrict access to sensitive data and functionalities.
- Enforce multi-factor authentication for access.
- Utilize encryption for data at rest and in transit.
- Identify user roles and their required permissions.
- Assign roles to users based on job functions.
- Implement the principle of least privilege.
- Document role definitions and access rights.
- Schedule periodic audits of user access rights.
- Remove access for users who no longer need it.
- Update roles and permissions based on changes in job responsibilities.
- Maintain logs of access reviews for accountability.
- Implement logging for all access events.
- Use automated tools for real-time monitoring.
- Set up alerts for suspicious activities.
- Regularly analyze logs for anomalies and trends.
6. Incident Response
- Identify potential AI security threats.
- Outline roles and responsibilities for the incident response team.
- Define procedures for identifying, containing, and mitigating AI security incidents.
- Establish criteria for escalating incidents to higher authorities.
- Document the plan and ensure accessibility to all stakeholders.
- Define key contacts for reporting incidents.
- Set guidelines for internal and external communications.
- Ensure timely notifications to relevant stakeholders.
- Create templates for incident reporting and updates.
- Regularly test communication channels for effectiveness.
- Develop training materials focused on AI-specific threats.
- Schedule regular training sessions and workshops.
- Include real-world examples of AI security incidents.
- Encourage a culture of security awareness and vigilance.
- Evaluate training effectiveness through assessments.
- Design realistic scenarios that mimic potential AI threats.
- Involve all relevant personnel in the simulation.
- Evaluate the response time and actions taken during the simulation.
- Gather feedback to identify areas for improvement.
- Document outcomes and update response plans accordingly.
- Schedule regular review meetings to assess the plan.
- Incorporate lessons learned from past incidents and simulations.
- Stay informed about emerging AI security threats.
- Adjust the plan to reflect changes in technology and business operations.
- Ensure all stakeholders are notified of updates.
Use in Manifestly