Permissions, audit logs, confidence scoring, source attribution, human review, escalation, and data perimeter controls—explained for executives who need to evaluate AI deployments.
AI governance is not compliance theater. When implemented correctly, governance controls protect your organization from the real risks of AI deployment: incorrect outputs, data exposure, unauthorized decisions, and inability to explain what happened.
This guide breaks down each governance component in plain executive language so you can evaluate whether your AI deployments have appropriate controls—or identify what's missing.
Role-based access determines who can view AI outputs, override decisions, access training data, or modify configurations. Permissions should align with job function, not be blanket-access.
Every AI query, response, override, and human review action is logged with timestamps, user IDs, input data, and output data. Logs should be immutable and retention-compliant.
AI outputs include confidence scores indicating reliability. Low-confidence outputs route to human review. Thresholds should be set based on decision consequence, not arbitrary defaults.
AI responses cite the source documents, data points, or knowledge bases that support each claim. Users can verify and audit the reasoning chain.
Critical decisions require human approval before execution. The system identifies when human judgment is needed and provides the context reviewers need to make good decisions quickly.
Sensitive data stays within defined boundaries. AI cannot access data sources outside its authorized scope. Data masking and tokenization protect PII and confidential information.
Request a use-case review to assess whether your AI deployments have appropriate governance controls for your risk profile and compliance requirements.
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