AI Governance vs Pebblo (Safe Agent/Safe Infer): 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.

Pebblo (Safe Agent/Safe Infer)

Security teams deploying AI agents across engineering and operations need Pebblo's Safe Agent/Safe Infer to block prompt injection and data exfiltration before they happen, not after; the inline inspection model catches sensitive content in real time rather than logging violations in retrospect. The platform's MCP Gateway sandboxing and centralized policy enforcement across user, document, and application context directly address NIST PR.AA and PR.DS, the two identity and data controls most teams botch in agent deployments. Skip this if your organization runs a handful of internal chatbots with minimal tool integration; Pebblo's complexity justifies itself only when you have heterogeneous agent workloads, third-party MCP servers, and compliance obligations that demand audit trails.