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Practices for assessing AI systems, including testing, red teaming, risk assessment, auditing, and compliance verification.
Also in Organisation
Upon deployment, the governance framework shifts to emphasize security, compliance, and accountability, ensuring models are integrated responsibly within operational systems. Compliance checks confirm adherence to governance policies, protecting organizational standards and mitigating security risks. Regular audits and reporting mechanisms enhance transparency for both internal and external stakeholders.
Reasoning
Compliance checks and regular audits verify adherence to governance policies post-deployment.
robust compliance controls
Deploy models with robust compliance controls, operational security, and accountability mechanisms.
2.3 Operations & Securityenforce policies
Ensure governance policies are enforced across systems.
2.1.3 Policies & ProceduresIdeation and Planning
During ideation and planning, governance focuses on strategic alignment with ethical standards and data management principles. At this foundational phase, the organization’s core values and ethical commitments are embedded in the GenAI project’s purpose, objectives, and design. Clear guidelines on ethical GenAI practices and data governance establish a strong foundation for responsible GenAI development.
2.4.2 Design StandardsIdeation and Planning > strategic alignment
Establish strategic alignment with ethical principles, data management policies, and organizational objectives.
2.1.3 Policies & ProceduresIdeation and Planning > Embed governance
Embed governance from the outset by defining clear values, risks, and desired AI outcomes.
2.1.3 Policies & ProceduresData Collection, Exploration, and Preparation
At the data collection and preparation stage, the governance framework prioritizes data integrity, privacy, and security, essential for responsible GenAI development. Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle, ensuring that information remains unaltered and trustworthy across collection, storage, processing, and analysis. Governance ensures data is representative, accurate, and responsibly sourced, particularly in regulated sectors. Robust data lineage and quality assurance practices enhance transparency and address potential biases early on, promoting equitable GenAI outcomes.
2.4.2 Design StandardsData Collection, Exploration, and Preparation > privacy compliance
Prioritize data integrity, quality assurance, and privacy compliance.
2.1.3 Policies & ProceduresData Collection, Exploration, and Preparation > data governance
Establish strong data governance, including representativene ss, sourcing standards, and lineage tracking
2.1.3 Policies & ProceduresApproaches to Responsible Governance of GenAI in Organizations
Joshi, Himanshu; Hassani, Shabnam; Gandhi, Dhari; Hartman, Lucas (2025)
The rapid evolution of Generative AI (GenAI) has introduced unprecedented opportunities while presenting complex challenges around ethics, accountability, and societal impact. This paper draws on a literature review, established governance frameworks, and industry roundtable discussions to identify core principles for integrating responsible GenAI governance into diverse organizational structures. Our objective is to provide actionable recommendations for a balanced, risk-based governance approach that enables both innovation and oversight. Findings emphasize the need for adaptable risk assessment tools, continuous monitoring practices, and cross-sector collaboration to establish trustworthy GenAI. These insights provide a structured foundation and Responsible GenAI Guide (ResAI) for organizations to align GenAI initiatives with ethical, legal, and operational best practices.
Deploy
Releasing the AI system into a production environment
Deployer
Entity that integrates and deploys the AI system for end users
Govern
Policies, processes, and accountability structures for AI risk management
