What is the difference between AI Governance vs Riscosity AI Data Firewall?

**AI Governance**: AI governance platform for managing and monitoring AI/ML model lifecycle. built by Domino Data Lab. headquartered in United States.. **Riscosity AI Data Firewall**: Agentless AI data firewall for governing data flows to AI services. built by Riscosity. Core capabilities include Agentless, no-code deployment, AI vendor discovery and cataloging, Shadow AI detection.. Both serve the AI Governance market but differ in approach, feature depth, and target audience.

Who makes AI Governance vs Riscosity AI Data Firewall?

**AI Governance** is developed by Domino Data Lab. **Riscosity AI Data Firewall** is developed by Riscosity. Vendor maturity, funding stage, and team size can be important factors when evaluating long-term viability and support quality.

Is AI Governance a good alternative to Riscosity AI Data Firewall?

AI Governance and Riscosity AI Data Firewall serve similar AI Governance use cases: both are AI Governance tools, both cover AI Governance. Review the feature comparison above to determine which fits your requirements.

AI Governance vs Riscosity AI Data Firewall (2026) | Compare AI Governance Tools

AI Governance vs Riscosity AI Data Firewall: 2026 Comparison

AI Governance is a commercial ai governance tool by Domino Data Lab. Riscosity AI Data Firewall is a commercial ai governance tool by Riscosity. Compare features, ratings, integrations, and community reviews side by side to find the best ai governance fit for your security stack.

CST Verdict

Based on our analysis of NIST CSF 2.0 coverage, core features, company size fit, deployment model, here is our conclusion:

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.

Data verified Apr 2026