How we govern the AI systems we build
Framework
A practical framework for responsible AI
RealAIzation has adopted an AI safety, security and compliance framework designed around the management-system principles of ISO/IEC 42001:2023. It is tailored to the work we actually do: engineering production AI systems, integrating models and tools, operating systems for customers, advising leadership teams, and enabling organizations to use AI well.
The goal is straightforward: move quickly without leaving safety, security, data protection, or accountability until the end.
Designed around ISO/IEC 42001:2023
ISO/IEC 42001:2023 is an international standard for AI management systems. It provides a structured way to govern how organizations develop, provide, or use AI.
We use its management-system principles to guide our own approach: accountable ownership, risk assessment, lifecycle controls, performance review, and continual improvement. RealAIzation is not ISO/IEC 42001 certified, and we do not claim independent certification, audit, or blanket conformity with every requirement of the standard.
In practice
What this looks like in practice
Start with the use case
Before material work begins, we define the intended use, users, data sensitivity, deployment model, foreseeable misuse, customer requirements, and the human role around the system.
The level of review rises with the impact of the use case. Sensitive data, material autonomy, high-impact decisions, new AI tools, and significant regulatory uncertainty receive elevated review before work proceeds.
Control the data and tools
Customer information stays in approved systems and workflows. Access is individual, role-based, and limited to what the work requires. We minimize data use and do not use production data for development, testing, demonstrations, or model improvement unless the customer has expressly authorized it.
Before a new model, AI API, SaaS tool, or material configuration receives real data, we assess its intended use, data handling, provider terms, training settings, security and privacy posture, and relevant customer requirements.
Test the system, not just the demo
Production AI needs more than a convincing prompt. We evaluate systems in proportion to their risk and intended purpose, including functional quality, error modes, hallucination, prompt injection, data exposure, unsafe tool use, harmful outputs, resilience, and fairness where relevant.
Our engineering baseline includes least-privilege access, multi-factor authentication, approved secret management, private repositories, required human code review, CI/CD controls, vulnerability checks, environment separation, controlled releases, and appropriate monitoring.
Keep people accountable
AI does not remove human responsibility. We define the review, intervention, override, escalation, and approval mechanisms that fit the engagement.
For systems that may affect people’s rights, finances, safety, employment, education, access to services, or legal interests, we require additional governance before delivery. Material decisions and exceptions are escalated to engineering and executive leadership.
Operate, monitor, improve
We monitor AI-enabled systems in a way that fits the service and operating model. This can include availability, security signals, model behavior, quality, tool actions, anomalies, and customer feedback.
RealAIzation personnel must report actual or suspected confidentiality, security, credential, or data incidents immediately and no later than two hours after discovery. Material findings, remediation, and lessons learned feed back into management review and system improvement.
Environments
Built for your environment
We work across RealAIzation-managed, customer-managed, and shared environments. When a customer operates the infrastructure, we document the shared-responsibility model so it is clear who owns access, monitoring, logging, incident response, and evidence.
We are model- and vendor-agnostic. We select technology based on the use case, technical fit, data controls, safety and security requirements, customer terms, and operational needs. Commercial affiliations do not replace technical or risk review.
Regulation
EU AI Act and GDPR: assessed when relevant
Our core AI safety framework applies to all in-scope work. Where an engagement involves EU or EEA factors, we perform a documented applicability assessment rather than assuming that every project has the same legal obligations.
The assessment considers the intended use of AI outputs, the roles of RealAIzation and the customer, personal-data processing involving people in the EEA, cross-border data flows, contractual requirements, and relevant sector rules. Where the EU AI Act, GDPR, or other requirements apply, we incorporate the appropriate controls into the engagement and seek specialist input where needed.
This is not a claim of blanket legal compliance. It is a practical process for identifying and addressing obligations that apply to a specific system and context.
Due diligence
For customer due diligence
We can explain our approach to:
Customer-specific commitments and assurance evidence are governed by the relevant contract, statement of work, security addendum, and data-processing terms. We protect customer confidentiality and do not disclose customer identities, confidential system configurations, datasets, or internal assurance records without appropriate authorization.
Bring AI into production with accountability
Whether you are exploring a use case, reviewing an acquisition target, building a production system, or rolling out AI across a portfolio, we help make governance part of the delivery path.