Implement GenAI Governance: AI Security & Compliance

Identify generative AI vendors and assess security, privacy, and GDPR compliance. Perform due diligence, monitoring, audits, contracts to manage data protection, data sharing, biases, and legal risks for Genesis II.

Navigate contract management for secure cloud services, defining scope, timelines, and compliance, then monitor performance, amendments, and renewals with transparent records and strict data protection.

Identify and classify sensitive data across on-premises and cloud assets, implement role-based access control and multi-factor authentication, monitor usage, and educate employees to prevent leakage in generative AI.

Learn how encryption and data masking protect sensitive AI data across at-rest and in-transit stages, with key management, access controls, and ongoing staff training.

Identify and catalog intellectual property assets, including patents, trademarks, copyrights, and trade secrets, then secure and enforce IP through legal protections, encryption, access controls, DRM, licensing, and audits.

Implement GenAI governance by aligning with GDPR, CcpA, HIPAA, and industry guidelines. Establish data protection policies, privacy impact assessments, security protocols, and regular audits for ongoing compliance.

Define the audit scope and objectives, collect logs and configurations, assess data handling and regulatory compliance, and report findings with recommendations for continuous improvement.

Implement GenAI governance by combining RBAC and ABAC to manage roles and attributes, enforce policies, monitor access, and audit permissions in real time.

Implement real-time monitoring and auditing of AI system access by logging every access event, applying rbac and abac, analyzing logs with advanced analytics, and coordinating incident response and training.

Educate users on Gen AI fundamentals, capabilities, limitations, security, ethics, and data handling to boost safe, productive adoption. Train for decision making, error reduction, and compliant, collaborative AI use.

Define objectives and criteria, assess data quality, measure model performance, and test scalability and robustness to ensure reliable, compliant, secure ai applications with ongoing monitoring.

Lead the end-to-end approval process for ai applications, from initial assessment and technical evaluation to pilot testing, security checks, user acceptance testing, documentation review, and deployment.

Implement identity management to enforce least privilege with RBAC and MFA, monitor user activity, conduct audits, manage identity lifecycles, and employ robust authentication methods to secure gen ai environments.

Explore risk assessment techniques for GenAI governance, including qualitative and quantitative methods, FMEA, bowtie analysis, Monte Carlo simulations, and sensitivity analysis to identify and mitigate AI system threats.

Mitigate data risk with encryption and access controls to prevent unauthorized access, supported by security audits, patch management, and GDPR and CcpA compliance reviews.

Explore data governance frameworks such as Mbock, Cobit, ISO IEC 38505, CMMi for data management, and NIST to align data practices, ensure quality, privacy, and compliance with GDPR and CCPA.

Explore how to establish data governance policies that protect privacy and security, ensure data quality, and govern access, usage, retention, compliance, and data integration for gen ai systems.