
SEAT measures a security awareness program across four pillars, Strategy, Engagement, Assessment and Training, each on a 1 to 5 maturity scale.
This connector lets your AI administer the 21-question assessment one pillar at a time, score the result, and show the five questions holding the program back along with what the next level looks like. From there it maps the result to any of 16 compliance and AI governance frameworks, including NIST CSF 2.0, ISO 27001, NIS2, DORA, CMMC, PCI DSS 4.0, SOC 2, GDPR, the EU AI Act and NIST AI RMF.
Example path
https://mcp.humanrisk.com/mcpSEAT is the Security Awareness Maturity Framework by Jason Hoenich / HumanRisk. It scores a security awareness program on four pillars (Strategy, Engagement, Assessment, Training) from 21 questions, each answered 1 to 5.
Typical flow: , then one pillar at a time, then once all 21 answers are in. After scoring, returns what to do next for each pillar, serves practitioner guides by pillar, builds a crawl/walk/run program plan, shows progress between two answer sets, applies one of 16 framework adapters, and lists published research mapped to SEAT outcomes. returns outcome scores; use those when applying a research source.
How the published instrument is meant to be run: one question at a time with all five options; if the respondent is between two levels the lower one is recorded; the respondent answers every question themselves; questions are not added, removed or reworded.
Data handling: and receive the 21 numeric answers (no names, no company details). They are processed in memory and not stored, and the server does not log answer contents. The same arithmetic can be run locally from https://github.com/jason-hoenich/seat-assessment.
Research sources explain exposure. They do not change a score, create a gap, or establish compliance status; only the framework adapters decide met or gap.
None in the store listings or on the server