Governing Generative AI: Foundational Mechanisms for Responsible Adoption

Generative AI is evolving at a rapid pace – from automating document extracts to reviews to powering customer interactions. But with speed comes greater risks, including privacy leakage, intellectual property leakage, bias in outputs, regulatory frameworks, and unclear accountability.

Unlike traditional IT systems, generative AI adoption is often bottom-up with employees’ vibe coding with models, making it hard to control technically while foundation models evolve unpredictably. Governance, therefore, needs to shift from static policies to dynamic mechanisms that balance innovation with safeguards.

Around the world, regulators are adapting these principles into sector-specific frameworks – for instance, the Reserve Bank of India’s FREE-AI Framework outlines six governance pillars (infrastructure, policy, capacity, governance, protection, and assurance) that mirror these international best practices.

The Foundation: Understanding Generative AI’s Unique Ethical and Governance Challenges

Internal factors: Organizational culture, AI capabilities, projects (routine vs. knowledge intensive), and overall strategy influence (whether generative AI is treated as a strategic asset or a risky experiment) and governance maturity

External factors: Geographical context, regulations, ethical AI guidelines and industry norms along with internal and partner ecosystem

Cascading Standards: NIST and ISO

Two pivotal reference points stand out prominently in the landscape of standards: the National Institute of Standards and Technology (NIST) and the International Organization for Standardization (ISO), NIST AI Risk Management Framework (AI RMF, 2023).

1. NIST AI Risk Management Framework (AI RMF, 2023)

Key functions include govern, map, measure, and manage. These are essential imperatives for achieving success and driving results.

Key governance priorities involve clear accountability, continuous monitoring, and contextual risk controls.

2. ISO/IEC 42001 (AI Management System Standard, 2023) and ISO/IEC 23894 (AI Risk Management)

Require organizations to treat AI governance similar to quality or information security management.

Emphasize policies, documented roles, continuous risk assessment, and stakeholder communication.

These standards pave the way for a shift from random experimentation to a cohesive AI governance approach that aligns seamlessly with enterprise’s strategic goals.

Building Robust Generative AI Governance Frameworks: A Proactive Approach

Organizations can borrow from IT governance and data governance, but generative AI requires some additional mechanisms. At a foundational level, responsible AI should include:

1. Decision Rights and Accountability

• Define who can procure, evaluate, fine-tune, and deploy generative AI tools.
• Use a RACI model (Responsible, Accountable, Consulted, Informed) for clarity.
• Encourage joint accountability between business, IT, and risk functions.

2. Policies and Guidelines

• Policy for employees experimenting, and integrating generative AI into use cases
• Data handling rules and data management, governance policy
• Model usage disclosures, flagging AI-generated outputs in line with responsible AI frameworks

3. Monitoring and Intervention Mechanisms

• Continuous logging of model calls, usage, inference and decisions on responsible AI
• Regular audits for bias, hallucinations, along with other dimensions like model and data drift
• Incident response playbooks for model failures, model calls hitting thresholds, and integration with human queues

4. Risk Assessment and Controls

• Conduct periodic AI impact assessments (e.g. DPIAs under data privacy law)
• Assess risks across fairness, robustness, explainability, and security
• Maintain an AI registry of all models, use cases and metadata

5. Relational and Communication Mechanisms

• Ethics council or AI board with cross-functional participation on decisions
• Regular training for staff, senior management, on safely leveraging generative AI
• External communication including transparency with customers, shareholders, and regulators

6. Formalize Generative AI Operations Across Multiple Lines of Defense

• First line: business teams using generative AI, control function, data office, privacy office
• Second line: risk, infosec, compliance, and legal
• Third line: internal/external audit

Navigating the Evolving Regulatory frameworks and Future-Proofing Responsible AI

AI governance often begins at the project level (“let’s test this chatbot safely”) but must evolve:

Project governance that is focused on immediate benefits

At this stage, AI governance is tactically focused: “Can this generative AI assistant improve document reviews?” Controls for responsible AI emphasize secure infrastructure, pilot environments, and basic policies such as acceptable use and data handling. The goal is to realize immediate project-level benefits while reducing risks of exposure or non-compliance.

Organizational AI governance that balancing risks and returns at enterprise scale

As generative AI adoption improves in an organization, and AI governance function is formalized, the siloed implementation from bottom-up shifts to enterprise-wide use. AI governance here balances risk and return to maximize productivity and innovation while managing legal nuances, regulatory frameworks, data privacy, and reputational risks along with ethical AI nuances. Mechanisms in AI tools such as AI registries, risk impact assessments, and oversight committees make it seamless to scale generative AI integrated across business operations. This stage emphasizes decision rights and accountability across stakeholders, i.e., who evaluates models, who approves deployments, who monitors usage, and who intervenes when risks emerge or control weakness emerge.

Responsible governance aligning with corporate governance

The final stage extends governance beyond the walls of the organization. Here, the focus shifts to societal responsibility, joint accountability, and continuous learning. Governance mechanisms expand to include transparent disclosures in annual reports, alignment with corporate governance, external assurance and audits, and capacity-building programs for staff, management, and even regulators. At this level, organizations embrace a Complex Adaptive System (CAS) view where generative AI implementation evolves through feedback loops from employees, customers, auditors, and regulators. AI governance must be dynamic, learning-oriented, and capable of adapting to new risks such as bias, model collapse, or regulatory change.

Why This Progression Matters

• From compliance to value: AI governance must move beyond project-level risk checks to create enterprise-wide accountability that delivers business value.
• From inward controls to outward trust: oversight should not stop at internal efficiency; it must extend to building confidence with customers, regulators, and society.
• From static rules to adaptive frameworks: AI governance cannot remain a one-time exercise; it must continuously evolve in response to shifting risks, technologies, and stakeholder expectations.