AI systems are becoming multi-modal, adaptive, and autonomous—but governance hasn't kept up.
Continuous model tuning, agent workflows, and distributed AI stacks mean risk changes in real time, while most governance approaches remain manual, siloed, and reactive.
The result: limited visibility, slow approvals, audit gaps, and teams forced to choose between speed and safety.
Enterprise AI World, in partnership with DBTA, held a webinar, Data & AI Control Plane: Continuous Governance for the Agent Era, with Tohsheen Bazaz, principal product manager at OneTrust, who discussed a new vision for AI governance.
“Compliance by design requires around the clock monitoring and enforcement,” Bazaz said. “You cannot automate enforcement beyond your ability to observe.”
AI governance software helps organizations safely scale AI by managing risk, compliance, and accountability across the entire AI lifecycle. It centralizes key capabilities—such as AI inventories, risk assessments, policy enforcement, model monitoring, and automated documentation—so teams can deploy AI with confidence.
AI governance today consists of assessing risks and making sure that you’re compliant. Currently, an organization builds a policy, risk, and AI use-system of record. Whereas AI governance of the future should focus on cross-platform monitoring and programmatic enforcement.
Policy enforcement considerations and critical metadata categories include:
- Model and version: Model identifier and version hash. Training data lineage references Deployment configuration. Model risk classification.
- Audit log and observability: Request/response. Inference time, latency, and resource consumption. Prompt/response traceability for LLMs and agents. Bias, fairness, and accuracy metrics logged at runtime. Event logs for user/system interventions.
- Data provenance and lineage: Data sources feeding the system. Pre-processing and transformation history. Ownership and consent status of data. Retention and deletion status.
- Evaluation/explainability: Feature attribution records, confidence scores, or uncertainty measures per prediction. Rationales or summaries generated for LLM decisions. Counterfactuals or comparable decision points logged.
- Access and security: Identity of the deploying entity Access logs. Policy enforcement logs. API key/token rotation history.
- Governance and approval: Policy compliance check outcomes. Approval workflow status. Exception logs.
According to Bazaz, OneTrust AI Governance is the platform of choice to utilize. OneTrust AI Governance software aligns enterprise governance with technical reality, so teams scale AI faster, reduce risk, and maintain trust.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.