In brief
What an AI governance platform is, and what changes when AI acts.
01
The four outputs an AI governance platform should produce, and why most tools cover only two.
3 min read
02
Why agents that call tools and act on systems need governance at the level of actions, not only answers.
3 min read
03
Why discovery comes first for many programs, what a good AI register records, and which platforms describe it.
3 min read
What the EU AI Act, ISO/IEC 42001 and NIST AI RMF ask for, and how one control serves several.
04
The structure of the EU AI Act in plain terms, and the software capabilities each obligation calls for.
3 min read
05
What ISO/IEC 42001 is, how its clauses are organized, and where software helps an AI management system.
3 min read
06
Govern, Map, Measure and Manage explained, with the platform capabilities that support each one.
2 min read
07
How to write a control once and map it to the EU AI Act, ISO/IEC 42001, NIST AI RMF, OWASP and MITRE ATLAS.
3 min read
Turning a policy into a control, a test result into evidence, and vendor AI into a register.
08
How a written AI policy becomes a guardrail that acts on live traffic, and what to check in a vendor's description.
3 min read
09
How adversarial testing before and after launch produces evidence an auditor can use, and what a usable finding looks like.
2 min read
10
How to register and assess AI from vendors and SaaS tools, and which platforms describe a third-party AI register.
3 min read
Who owns AI governance and how the work is shared.
11
A practical split of AI governance work between legal and compliance, security, product and engineering, and how platforms map to each owner.
2 min read