Skip to content
AtelaMind
Capability

EU AI Act & ISO/IEC 42001 Readiness

Operational readiness for organizations responding to AI regulation — practical management systems, testable controls, and continuous evidence embedded into delivery workflows.

  • Testable control libraries mapped to EU AI Act and ISO/IEC 42001 requirements
  • Evidence-by-design: captured continuously in delivery, not retrofitted for audit
  • Governance that bridges engineering, legal, privacy, and security — without blocking delivery
Request a scoping brief
EU AI Act & ISO/IEC 42001 Readiness
When to bring this in

This is typically needed when:

AI policies exist on paper but lack operational hooks — teams sidestep them, and compliance gaps go undetected until external review.

Tracking AI systems, models, data flows, and suppliers is a manual, retroactive exercise that frustrates developers and regulators alike.

Approval authority is ambiguous — nobody is sure who approves what, under which conditions, for which risk level.

EU AI Act enforcement timelines are approaching and the organization has no structured path to demonstrable readiness.

Governance is managed as a separate layer from delivery, creating friction and shadow workarounds.

What the engagement covers

Scope

A principal-led engagement that builds the management system, control library, and evidence approach needed for EU AI Act and ISO/IEC 42001 readiness — embedded into delivery workflows, not bolted on as a separate compliance layer.

AI Management System design: roles, decision forums, escalation paths, ownership, and supplier governance — structured to scale across teams and vendors
Control library mapped to EU AI Act obligations and ISO/IEC 42001 requirements — designed to be testable and evidenced, not aspirational
Risk classification and proportionate controls for privacy, security, misuse, and third-party risk
Evidence system and documentation standards: inventories, model/system docs, evaluation records, and audit trails — minimum viable evidence that fits delivery reality
Delivery integration: release gates, policy-as-code where useful, and CI/CD-aligned checks that automate evidence capture
Mappings to NIST AI RMF and relevant ENISA guidance where applicable
Executive reporting cadence: CIO/CTO/CISO/CFO visibility without distorting delivery priorities
Training assets for teams, reviewers, and approvers
Typical outputs

What the engagement produces

What changes afterwards

After this engagement

Decision rights are unambiguous — who approves what, under which conditions, for which risk level.

AI systems, owners, data boundaries, and suppliers are tracked in a consistent inventory across the organization.

Evidence is captured continuously as part of delivery — not reconstructed retroactively for audit.

Approvals become faster and more predictable because controls are testable and gates are embedded in workflows.

Engineering, legal, privacy, and security functions work from shared criteria instead of parallel review processes.

What this is not

✕Legal advice or regulatory interpretation
✕A compliance paperwork exercise detached from delivery
✕A hands-on engineering implementation team
✕A generic AI governance framework without operational hooks
✕A one-time audit preparation exercise
Common questions

FAQs