A.L.I.G.N. 42001 methodology
Discover A.L.I.G.N. 42001
AI compliance that genuinely protects the business and accelerates deployment
A.L.I.G.N. 42001 is a proprietary methodology for implementing AI compliance in organizations, combining the requirements of the EU AI Act, the GDPR, and key areas of emerging technology law with a systematic approach based on ISO/IEC 42001 (AIMS — AI Management System). It is designed to deliver both legal certainty and operational feasibility – without slowing down innovation.
This is not “a binder of documents.” It is a practical mechanism that enables an organization to deploy AI in a controlled, audit-ready way that is resilient to regulatory, contractual, and reputational risk.
Why A.L.I.G.N. 42001 works
AI implementations most often fail not because of the technology, but because of the lack of coherent risk management and defensible evidence: who made the decision, on what basis, how risks were assessed, how human oversight operates, how the vendor is controlled, how data transfers and security are handled, and how model changes are managed.
A.L.I.G.N. 42001 brings these elements into a single, integrated system. As a result, you:
minimize the risk of fines and disputes (EU AI Act, GDPR, consumer law, sector-specific regulations, cybersecurity, IP, and employment law),
build a predictable AI deployment process instead of “firefighting,”
accelerate deployments by giving teams clear gates and checklists,
become audit- and inspection-ready, with tangible evidence of due diligence.
What makes the methodology stand out (our delivery standard)
Evidence-first – we create only the documentation that is genuinely necessary to protect the business and defend decisions.
Compliance-by-design – legal requirements are embedded into the AI lifecycle: from ideation, through testing, to monitoring and retirement.
Risk-proportionate – the higher the risk (EU AI Act/GDPR), the stronger the controls and the evidentiary record.
Single source of truth – a centralized AI Inventory as the backbone of the system.
Business-safe, not business-stop – legal assurance should enable growth, not slow it down.
A.L.I.G.N. in practice: a five-stage implementation
A - Assess
Diagnosis, AI Inventory, and risk classification
In a short timeframe, we identify all AI use cases (including vendor solutions and GenAI), map data flows, and classify systems against the EU AI Act and the GDPR. This creates a structured picture of what is running, where, for what purpose, and with what level of risk.
Outcome: rapid transparency and clear priorities—without guesswork or “shadow AI.”
L - Legalize
Legal foundations, contracts, and a compliant take-off runway
Each AI use case receives a clear “legal clearance card”: EU AI Act obligations, GDPR processing grounds, required assessments (e.g., a DPIA), transparency rules, as well as contractual safeguards and vendor governance. This is the stage where we build resilience to key risks—liability, data transfers, confidentiality, IP, and vendor auditability.
Outcome: AI becomes legally deployable, and the organization has evidence of due diligence.
I - Implement
Operational controls embedded into delivery (SDLC/MLOps/procurement)
We implement practical control gates and checklists across key processes—from data selection, through quality/bias/robustness testing, to security safeguards (e.g., prompt injection and data leakage risks), human oversight rules, and user communication procedures.
Outcome: compliance and security are not an add-on—they become part of how teams work.
G - Govern
An ISO/IEC 42001–aligned AI Management System (AIMS)
We build the core elements of an AI management system: roles and responsibilities, cyclical risk management, KPIs/KRIs, auditability, training, and management review mechanisms. This is the stage that protects the organization long-term—not only at go-live.
Outcome: AI compliance becomes an organizational capability, not a one-off project.
N - Navigate
Continuous improvement and sustained compliance
AI and regulations evolve—so we implement model change management (what qualifies as a “material change”), post-deployment monitoring (drift, incidents, complaints), and regulatory watch. The system remains live and protects the organization over time.
Outcome: compliance is maintained despite changes in technology, vendors, and regulatory requirements.