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Agentic Ai security standard

1. Governance and Compliance

Establish an AI governance framework.

Identify relevant regulations and compliance requirements.

Assign responsibility for AI security oversight.

Conduct regular audits and assessments of AI systems.

2. Risk Assessment

Perform a comprehensive risk assessment for AI systems.

Identify potential threats and vulnerabilities.

Evaluate the impact of identified risks on stakeholders.

Prioritize risks based on likelihood and impact.

3. Data Management

Implement data classification and labeling protocols.

Ensure data integrity and accuracy through validation checks.

Establish data access controls and permissions.

Regularly review and update data management policies.

4. Model Security

Secure AI model training environments.

Use version control for AI models to track changes.

Implement measures to prevent model tampering or poisoning.

Regularly monitor model performance for anomalies.

5. Deployment and Operations

Ensure secure deployment practices for AI systems.

Implement logging and monitoring for AI operations.

Establish incident response protocols for AI-related incidents.

Conduct regular security assessments of deployed AI systems.

6. User Training and Awareness

Develop training programs focused on AI security best practices.

Conduct regular awareness sessions for employees.

Provide resources and guidelines for secure AI usage.

Encourage reporting of security incidents and concerns.

7. Continuous Improvement

Establish a feedback loop for AI security practices.

Regularly update security measures based on new threats.

Participate in industry forums and share lessons learned.

Review and revise the checklist annually to reflect changes in technology and regulation.

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