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Rahul Agarwal
Founder | Agentic AI... • 9d
AI governance is not the same as data governance. Data governance is centralized. IT decides who accesses the database. Five people manage permissions. AI governance can’t be centralized. Everyone has the AI now. Everyone uses it differently. A proper AI governance model has three layers. Layer 1: Leadership Direction. Policies set clear boundaries: Gemini can draft emails and summarize docs. It cannot write contracts or financial reports. Sales can use it for research. Legal cannot. Customer-facing content needs manager approval. Boundaries alone don’t enforce themselves. Layer 2: Employee Participation. Someone in Marketing writes a product description with Gemini. She knows to fact-check the output. She must disclose AI usage to her manager. She flags any features that don’t exist. Training is essential. Everyone must understand their accountability. Policies are not enough-behavior must change. Layer 3: Systems & Structures. SSO + role-based access controls who gets Gemini.. Approved sources ensure factual claims and citations. Workflow gates require human review before publishing. Monitoring includes dashboards, QA sampling, and incident escalation. These guardrails catch what humans miss. Not by watching every prompt, but through smart enforcement. Why all three layers matter: Leadership sets boundaries. Employees follow accountability rules. Systems enforce technical controls.
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