Trust Center
Your security posture, always current, always answerable.
AnnexIQ Digital and AI Trust Platform
AI Governance
Know every AI system you run, and prove it is governed.
A registry, risk workflow, and policy library built for the way AI actually gets adopted inside a real organization, from the first project idea through to a governed system in production.
Know every AI system you run, and prove it is governed.
AI Governance gives you a single registry of every AI system in use, whether it was built internally, bought from a vendor, or embedded quietly in a product you already own, with a structured lifecycle from registration through risk assessment, evaluation, and governance review before anything goes live.
Risk assessments, technical evaluations, incident tracking, and policy or framework mapping all live against each system's own record, so the question "which AI systems touch customer data, and who approved them" has an answer you can produce in minutes rather than a scramble.
Before an AI initiative ever reaches that registry, AI Project Governance gives it its own lifecycle: a demand is qualified against architecture, technical, and data checks, approved into a phase template your organization defines, and only admitted into the register once it clears every gate. And once a system is live, Responsible AI runs a structured, human-reviewed assessment against seven principles, fairness, transparency, accountability, safety, privacy, security, and sustainability, grounded in the system's own risk tier and red-team findings, not a generic checklist.
Risk assessments, technical evaluations, incident tracking, and policy or framework mapping all live against each system's own record, so the question "which AI systems touch customer data, and who approved them" has an answer you can produce in minutes rather than a scramble.
Before an AI initiative ever reaches that registry, AI Project Governance gives it its own lifecycle: a demand is qualified against architecture, technical, and data checks, approved into a phase template your organization defines, and only admitted into the register once it clears every gate. And once a system is live, Responsible AI runs a structured, human-reviewed assessment against seven principles, fairness, transparency, accountability, safety, privacy, security, and sustainability, grounded in the system's own risk tier and red-team findings, not a generic checklist.
Every AI system, in one place
A living registry of every model, agent, and AI powered tool in use, with risk tier and governance status visible at a glance, so governance covers what's actually running, not just what was declared.
Risk classification that segregates who decides
Every system carries its own risk assessment, logged against a real framework and a likelihood/impact rating, not a gut feel checkbox someone filled in once and forgot.
One policy library, mapped to every system it governs
Maintain your AI policy and control framework in one place, EU AI Act, ISO/IEC 42001, NIST AI RMF, so an auditor or a regulator can see exactly which policy governs which model.
Every AI project qualified before it ever becomes a system
A demand is qualified against architecture, technical, and data checks, then approved into a phase template your organization defines, so a project only reaches the AI registry once it has actually earned the right to.
Responsible AI, scored against seven principles, not a gut check
Fairness, transparency, accountability, safety, privacy, security, and sustainability, assessed manually or AI-enabled, always reviewed by someone other than whoever ran it, and always grounded in that system's own risk tier and red-team findings.