AI Governance · AI Management System
Build AI you can explain. Use AI you can control.
Whether you’re building AI products or adopting AI tools across your organisation, an AI management system is what turns experimentation into reliable, defensible, and compliant results.
Why AI Governance matters
AI governance pays back from day one
Every organisation using AI — building it or buying it — is accumulating decisions that will eventually need to be explained: who approved this model; what data it was trained on; what happens when it drifts. AI governance removes ambiguity. When those answers are in the AI management system, your team spends less time negotiating and more time building and launching.
Defensible AI systems
Evidence generated automatically as your team works
Clear accountability
Every decision, output, and artifact has a name behind it
Resilience to change
Controls and training update in sync as models evolve
Built-in security and privacy
Safeguards designed into the AI lifecycle from the start
Faster market access
Governance that maps to what regulators and buyers expect
Team alignment from day one
Documented roles, training paths, and decision records
What AIMS is and who needs one
Manage AI compliance to build responsible innovations and retain market trust
An AI Management System connects AI principles to AI engineering. It turns values — privacy, safety, fairness — into processes your team runs daily: model validation, bias testing, risk reviews, lifecycle records.
A shared AI governance foundation applies to every organisation working with AI. What you add to it depends on whether you build AI or use it.
foundation
You build AI products
You use AI tools
End-to-end support for AI compliance
AI-powered system to manage your compliance
The platform
Integrated workspace where AI governance runs alongside Quality, Information Security, and Privacy, meaning shared controls, shared audit trail, shared users across every domain.
- Policies, procedures, and SOPs with approval flows
- Risk management and impact assessment
- Role-based training and competence tracking
- Supplier and third-party governance
- Cross-domain traceability and audit evidence
AI compliance SOPs ready to use out-of-the-box
The know-how set
Templates that turn regulatory requirements into procedures your team can use as a baseline.
- AI governance policies, objectives, and control documentation
- Impact assessment and risk management
- Data governance, privacy, and sensitive data handling
- Transparency, explainability, and bias management
- Lifecycle SOPs from inception through post-market monitoring
AI compliance and engineering experts on-demand
Consulting services
Whether you’re catching up on compliance or starting a new product, our regulatory consultants and product engineers close compliance gaps and design compliant AI innovations.
- Gap assessments and remediation roadmaps for existing AI
- Compliant-by-design architecture for new AI products
- AI risk classification and impact assessment
- Certification preparation and market access
Making AIMS operational inside your stack
ins2outs is designed so you can be operational within hours, instead of disruptive, year-long overhauls. Most teams take one of two paths:
Integrating and Migrating AIMS
If you already use a QMS or ISMS, you don’t need a separate system. We migrate your existing setup into ins2outs and layer the AI-specific extensions on top. Your audit history stays intact.
Configuring AIMS from scratch
If you have no compliance history, we will set up an AIMS before your next release: pick the scope, and make the ISO 42001 foundation go live immediately with pre-drafted policies and templates.
AI Management System implementation with ins2outs
Explore our step-by-step path to making AIMS real inside your organisation, whether you build AI products or rely on them. We can start at any point and stay for as long as you need.
01
Define AI purpose in context
Clarify what the AI system or tool is meant to do, where it’s used, and who it impacts — customers, users, or internal teams.
What we do:
Use case definition, intended purpose documentation, stakeholder and impact mapping
02
Classify risk based on real usage
Assess risk level based on how the AI is applied in practice — from product features to internal decision-making support.
What we do:
Risk classification, mapping use cases to regulatory categories, risk validation workshops
03
Map the path to compliance
Define what needs to happen before launch, scale, or wider adoption — including key checkpoints, approvals, and market requirements.
What we do:
Regulatory roadmap, market access planning, compliance milestone definition
04
Establish your AIMS foundation
Set up an AI Management System aligned with ISO/IEC 42001 and integrated into existing processes.
What we do:
ins2outs setup, governance structure definition, AIMS know-how set configuration, policy setup
05
Operationalize across teams
Embed policies, controls, and responsibilities into product development, data workflows, or day-to-day AI usage.
What we do:
Integration with the product team’s stack, team training, role and ownership setup
06
Engage notified bodies
Prepare for and coordinate with regulators or notified bodies where conformity assessment is needed.
What we do:
Notified body coordination, conformity assessment preparation, documentation support
07
Navigate audits and certification
Support audit readiness with complete documentation, traceability, and evidence generated through your AIMS.
What we do:
Audit preparation, evidence collection, certification support
08
Leverage post-market support
Implement AI monitoring and vigilance framework to ensure sustained compliance, model reliability, and safety throughout the system’s operational life.
What we do:
Post-market monitoring, drift and bias detection, incident response planning, regulatory reporting
09
Continuously evolve the system
Keep AIMS and AI governance aligned with product changes, regulation shifts, organisational growth, and AI usage expansion.
What we do:
ins2outs support, know-how set implementation, AI-powered regulatory intelligence
Drive consistent innovation with a structured foundation for AI governance
AI Management System
Master the framework for establishing a robust AIMS using the ISO 42001:2023 standard as a foundation.
Learn to bridge high-level ethical principles with day-to-day operations through concrete policies and workflows.
Ethical ML & bias mitigation
Adopt “Good ML” practices to build transparent, fair, and accountable systems
Understand how algorithmic fairness audits and diverse training datasets can help ensure your AI operates equitably for all end users
AI cybersecurity & data privacy
Implement a holistic security strategy spanning the entire lifecycle, from data preparation to deployment.
Explore Privacy by Design techniques like data minimization and encryption to meet rising consumer expectations and regulatory standards.
Ready to turn AI governance into a strategic asset?
Our experts are here to help you build resilient, transparent, and trustworthy AI systems where innovation and accountability go hand in hand.