AI Governance vs Manifest AI Bill of Materials (AIBOM): Side-by-Side Comparison (2026)

AI Governance

Mid-market and enterprise teams managing ML model sprawl across data science platforms need AI Governance to enforce policy and audit decisions before models drift into production; Domino's tight integration with its MLOps stack means you're not bolting governance onto disconnected infrastructure. The platform covers NIST GV and ID functions around access control and asset management for models, which is where most organizations have blind spots, though it skews toward governance and monitoring rather than the detection capabilities you'd need if your risk tolerance is zero. Skip this if your primary concern is catching adversarial attacks or model poisoning in real time; AI Governance assumes your threats are internal and process-based.

Manifest AI Bill of Materials (AIBOM)

Enterprise security teams drowning in shadow AI deployments need Manifest AI Bill of Materials to actually inventory what's running across the organization; most don't know where their models and training data live, and AIBOM forces that visibility at scale. The tool maps directly to NIST CSF 2.0's asset management and supply chain risk functions, two areas most enterprises fail because they treat AI systems like software when they're fundamentally different beasts. Skip this if your organization has fewer than 500 employees or hasn't shipped an AI application yet; the complexity tax only pays off when you're managing dozens of models across multiple teams.