Faced with the boom in uncontrolled uses and increasing regulatory pressure, the ability to anticipate, document and control the impacts of these technologies becomes a central governance issue. A new discipline is essential: IA risk mapping.
AI projects everywhere, zero visibility
In many organizations, business departments initiate AI projects without formal coordination with cybersecurity, legal or IT teams. Access facilitated powerful tools – co -pilotes, conversational assistants, generative AI platforms in SaaS mode – encourages local experiments, often invisible at the central level.
This phenomenon makes the identification of risks particularly complex. Without precise inventory of use cases, it is impossible to assess exposure to data leaks, algorithmic biases or ethical drifts. This opacity alone constitutes a first major level of risk.
A four -step method
To respond to this situation, a structured approach is essential. It is articulated around four key steps:
1. Identify active and latent use cases
The first phase consists in drawing up a functional mapping of current AI initiatives, including internal pilots or solutions purchased directly by trades. This audit must integrate:
- Model suppliers (Openai, Mistral, Hugging Face, etc.)
- Tools integrated into workflows (Microsoft Copilot, AI concept, internal agents)
- Targeted functions (support, HR, finance, marketing, etc.)
2. Classify the types of data processed
Each use case must be analyzed from the angle of data manipulated: personal data, sensitive data, industrial secrets, confidential documents, etc. This classification makes it possible to measure the legal (GDPR, business secrets) and technical (encryption, pseudonymization, traceability) issues.
3. Assess the risks linked to models and their autonomy
The analysis must relate to:
- The nature of the model used (LLM open source or closed, pre-trained or fine-tuna)
- The associated risks: prompt injection, hallucination, climbing of privileges, data persistence, bias
- The level of autonomy granted (simple suggestion or direct action)
Standards emerge to structure this analysis, as Atlas miter (for IA threats), OWASP TOP 10 LLM (for model vulnerabilities), or the standard ISO 42001dedicated to the management of AI systems.
4. Build an evolutionary risk matrix
Last step: formalize a matrix crossing criticality, probability of occurrence, potential impact and mitigation capacity. This cartography must be alive, updated as projects are evolving. It makes it possible to prioritize technical controls, training actions, and strategic arbitrations.
Shared responsibility, governance to consolidate
Risk mapping IA is not exclusively cybersecurity. It must associate business departments, CIO, Legal Directorate and Compliance. This transversality is the condition of effective governance.
In a context where regulators are preparing to impose strong obligations (AI European act, ISO, sectoral charters), having a formal cartography becomes an element of evidence. It is also a strategic steering lever for companies wishing to take advantage of AI without sacrificing their operational or regulatory integrity.
4 steps to build a risk mapping IA
| Stage | Objective | Concrete actions | Useful tools / standards |
|---|---|---|---|
| 1. Identify existing or current AI use cases | Have a clear vision of the AI projects in the organization (even the trades poc) | Remember the tools used (co -pilots, chatbots, agents), the teams involved, the suppliers | Internal audit, business interviews, IT inventory, observability tools |
| 2. Categrate manipulated data types | Evaluate sensitivity levels and associated legal obligations | Classify data flows: personal, sensitive, confidential, public | RGPD cartography, internal classification policies, DLP |
| 3. Evaluate the risks linked to models and their use | Identify vulnerabilities linked to models and contexts of use | Analysis of biases, injection attack risks, hallucinations, dependence on the supplier | OWASP TOP 10 LLM, MITRE ATLAS, adversarial assessment, Revue Code Model |
| 4. Call the risks and plan mitigation actions | Formalize a clear and scalable ia governance framework | Develop a risk / impact / control / managers matrix | Risk matrix, criticality score, ISO 42001 frame of reference, cyber dashboards |
