Resource
AI governance platform buyer's guide
A capability framework, a weighted scoring model and a procurement playbook for regulated enterprises choosing an AI governance platform.
- Weighted scoring model
- Capability and maturity framework
- Procurement playbook and pilot plan
What's inside this guide
Ten sections, from market context through scoring and architecture to procurement
Table of contents
Executive summary
Why AI governance became an operational job, and what this guide gives you to choose a platform on evidence.
Market context and risk
The three forces shaping the market: regulation moving from principles to enforceable obligations, operational complexity and public scrutiny.
Capability framework and maturity model
A framework covering the full AI lifecycle, and a maturity model to benchmark where your organization stands today.
Governing generative AI and agents
What breaks when AI is bought rather than built, and how to check a platform governs assistants and agents as first-class systems.
Product evaluation criteria and scoring
Weighted evaluation categories totalling 100, so you can compare vendors on the same terms instead of on demos.
Reference architecture
Six conceptual layers connecting policy, operations, technical systems and evidence into a control plane across the lifecycle.
Operating model and RACI
Who owns what across the AI lifecycle, so every decision traces to the person accountable for it.
Integrating the platform with existing systems
How governance connects to identity, CI/CD and MLOps, data and monitoring, rather than running as a parallel process by hand.
Procurement playbook
Four procurement phases and a 6 to 8 week proof of concept, with a decision trail you can show auditors and regulators.
Conclusion
What separates the right platform from the longest feature list, and how to judge fit against your own obligations.
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