Governance & Risk Management

Governance is often seen as a brake pedal, but in AI, good governance is the highway guardrail that allows you to drive fast without crashing.

We implement a Responsible AI framework that addresses Security, Privacy, and Quality.

AI Governance Framework

graph TD
    Level1[Principles & Policy]
    Level2[Process & controls]
    Level3[Technical Implementation]

    Level1 --> Level2
    Level2 --> Level3

    subgraph L1 ["Level 1: Principles"]
        P1[Fairness]
        P2[Transparency]
        P3[Accountability]
        P4[Safety]
    end

    subgraph L2 ["Level 2: Controls"]
        C1[Human-in-the-loop]
        C2[Data Usage Review]
        C3[Model Validation]
    end

    subgraph L3 ["Level 3: Tech"]
        T1[PII Masking]
        T2[Audit Logs]
        T3[Role-Based Access]
    end

    style L1 fill:#e3f2fd,stroke:#1565c0
    style L2 fill:#f3e5f5,stroke:#4a148c
    style L3 fill:#fff3e0,stroke:#e65100,stroke-dasharray: 5 5

Risk Categories & Mitigation

AI introduces specific risks that traditional software governance may miss.

Risk Category	Example	Mitigation Strategy
Data Privacy	Employee pastes customer PII into public ChatGPT.	Technical: Enterprise gateways that mask PII (e.g., Azure OpenAI private instance). Policy: “No public AI for internal data.”
IP Leakage	Proprietary code is used to train a public model.	Contractual: Use “Zero Retention” policies where vendors do not train on your data (standard in Enterprise tiers).
Hallucination	AI generates a correct-looking but legally dangerous contract clause.	Process: Mandatory “Human-in-the-Loop” review for all generated deliverables.
Bias	Hiring agent filters out candidates based on demographics.	Testing: Bias detection datasets and regular audits of model output.

Human Oversight Levels

Not all AI use cases require the same level of oversight.

Low Risk (Internal coding helper): Minimal oversight. Developer is the reviewer.
Medium Risk (Internal document search): Moderate oversight. RAG systems must respect existing document permissions.
High Risk (Customer-facing advice): Maximum oversight. Strict testing, automated guardrails, and potentially human sign-off before sending.

The “Black Box” Problem

Never deploy an AI decision-making system where you cannot explain why a decision was made, especially in regulated industries (Finance, Health). Always favor Explainable AI architectures.

Key Takeaways

Block Public AI, Provide Private AI: If you just block ChatGPT without providing a secure alternative, employees will use it on their personal phones. You must provide a secure, sanctioned internal tool.
Zero Training Policy: Ensure all vendor contracts stipulate that your data is NOT used to train their base models.
Automate Compliance: Use tools to scan prompt outputs for toxic content or PII automatically.