Embed AI risk, ethics and lifecycle controls aligned to the global AI management standard.
Move fast with AI and prove to regulators, clients and your board that your AI governance framework is under control.
An AI Governance and ISO/IEC 42001-aligned framework that embeds AI risk management into your operating model. We assess current governance against 42001 requirements, design the structure (committees, RACI, policies, standards), embed controls into project lifecycle, procurement and change management, then prepare evidence for audit or certification where you want it.
ISO/IEC 42001 is the international standard for AI Management Systems, published in 2023. It defines requirements for organizations that develop, provide or use AI systems to manage AI risks responsibly across the full lifecycle. It matters because clients, partners and regulators are beginning to require evidence of trustworthy AI practices. Being aligned with 42001 is also increasingly relevant as the EU AI Act and other regulations begin to affect global enterprises and their supply chains.
The key is building governance that runs alongside AI delivery rather than as a sequential gate. Practical approaches include: a lightweight AI impact assessment that takes two to four hours per initiative rather than weeks; a pre-approved fast lane for low-risk, well-understood use cases like document summarization and analytics; and an AI steering committee that meets monthly to review the portfolio rather than approving each use case individually. Governance designed as a service to delivery teams protects the business without creating bottlenecks.
The highest-priority AI risks for enterprises are: model error risk (an AI producing an incorrect output that affects a safety, cost or operational decision); data privacy risk (AI systems trained on or accessing sensitive client, partner or employee data); vendor risk (dependence on AI vendors who may change pricing, discontinue services or have data security weaknesses); and change management risk (AI deployed without adequate training or process change, resulting in low adoption or unsafe workarounds). We design governance frameworks that address these four risk categories first, then build out to full 42001 alignment.
AI governance extends existing enterprise risk management disciplines. Every AI system that touches a business decision needs the same rigor applied to any other mission-critical system: what happens if it produces a wrong output, who is accountable, and how is the error detected and corrected? We integrate AI risk into existing risk registers and change management processes rather than creating a separate bureaucracy.
Yes, at minimum three policies should precede broad AI deployment: an AI Acceptable Use Policy covering which AI tools are approved and how they may be used; an AI Impact Assessment Policy defining which new AI initiatives require a formal risk review before deployment; and a Data Use Policy covering what data can be used to train or fine-tune AI models. These three documents can be drafted in two to four weeks and provide the governance baseline that prevents the most common AI incidents before they happen.
Formal ISO/IEC 42001 certification requires: a documented AI Management System covering all mandatory control areas (AI policy, risk management, impact assessment, lifecycle controls, supplier governance); an internal audit against the standard; a management review; and a third-party audit by an accredited certification body. Organizations with an existing ISO 27001 system typically complete certification in twelve to eighteen months; those starting from scratch take eighteen to twenty-four months. We guide the full process from gap assessment through certification readiness.
A generative AI governance framework extends traditional AI governance to address the specific risks of large language models and generative tools: hallucination (generating plausible but incorrect output), intellectual property exposure (models trained on or reproducing proprietary data), and scale of deployment (generative tools being used by thousands of staff without centralized oversight). Enterprises deploying generative AI for document summarization, content drafting, or copilot experiences need a governance framework that defines approved model providers, data use boundaries, output review requirements, and incident response procedures. ISO/IEC 42001 provides a standards-aligned structure to govern both predictive and generative AI within a single management system.
Agentic AI systems take autonomous actions (creating tickets, sending communications, updating project records) rather than simply generating outputs for human review. An agentic AI governance framework must address three additional requirements beyond standard AI governance: action authorization policies (defining what categories of action an agent may take autonomously, and which require human approval); audit trail requirements (every agent action must be logged with the triggering data, decision logic, and output); and incident response for autonomous errors (a defined process for detecting when an agent has taken an incorrect action and correcting it at scale). We design agentic governance frameworks that integrate with ISO/IEC 42001 so enterprises manage all AI modalities under a single, coherent system.
An AI data governance framework defines how data used to train, fine-tune, or operate AI systems is collected, maintained, accessed, and retired. Typical components include: data ownership (who approves data being used for AI training); quality standards (what data quality thresholds must be met before data enters an AI system); access controls (which AI systems can access which data sets, and under what conditions); and retention and deletion policies (how long AI training data is held and when it must be purged). An AI data governance framework is a component of the broader AI governance framework, and aligns directly with ISO/IEC 42001 control areas covering data management and privacy.