AI governance is the set of policies, controls, and processes that keep AI systems safe, compliant, transparent, and accountable across their lifecycle. It spans data and model lineage, risk assessment, access boundaries, monitoring, human oversight, and audit trails — so an organization can show how and why an AI system behaves the way it does.
Agents take actions, not just generate text, so governance must cover what an agent is allowed to do, on whose behalf, with what data, and with what human checkpoint — and leave an auditable record.
We design grounding, explainability, guardrails, escalation, and human-in-the-loop review using IBM watsonx.governance, aligned to control requirements like SOC 2, HIPAA, and FINRA.
IBM's toolkit to govern AI across its lifecycle — model risk, lineage, drift and bias monitoring, and policy controls with audit trails.
Read definition →ArchitectureThe operating layer to deploy, monitor, secure, and govern an entire fleet of AI agents from one place — managing the agentic estate at scale.
Read definition →GovernanceA design pattern where a person reviews, approves, or can override an AI system's decisions at defined, high-stakes points.
Read definition →Agentic AIAI systems that take action — applying logic, making decisions, and running multi-step workflows end to end across enterprise systems, with human oversight.
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