AI governance and accountability: best practices for enterprise agents

Enterprise AI governance is presented as essential for managing the regulatory, financial, and reputational risks of increasingly autonomous AI systems, particularly given reported gaps in traceability and explainability. The framework centers on five pillars: transparency and explainability, fairness and bias mitigation, privacy and security, human oversight, and continuous monitoring, supported by defined ownership, lifecycle-based approval gates, documentation, audit trails, and alignment with standards such as the EU AI Act, GDPR, NIST AI RMF, and ISO/IEC 42001. Agentic AI requires additional safeguards, including explicit task limits, real-time policy guardrails, sandbox testing, escalation rules, kill switches, and performance measures covering value, errors, interventions, and behavioral drift. A composite banking example illustrates how system inventories, risk classification, mandatory reviews, and continuous monitoring can improve regulatory response times and identify bias issues before customer harm occurs. The material argues that governance can support not only compliance but also faster AI adoption, while noting that tools such as Dataiku can automate documentation, workflow, monitoring, and audit functions but cannot replace human accountability or organizational policy.

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