AI Security & Governance
Deploy AI that is EU AI Act and GDPR compliant, explainable and defensible, with governance that keeps your models in production.
Responsible AI is not an option.
It is a prerequisite.
An AI that does not pass the audits will not go into production. Security, compliance, explainability and governance are not after-the-fact constraints: they are the foundations on which trust is built. Together we build the framework that makes your AI deployable, defensible and durable.
Adservio at La Matinale IT for Business
An editorial live show bringing together IT leaders around data, AI and governance. Alongside EY, Salesforce and Informatica, Adservio speaks on EU AI Act compliance.
Our founder and CEO Anis Zouaoui details the Adservio method: mapping high-risk systems, model governance, continuous monitoring and operational compliance.
Four Audits
Governance is not declared, it is proven: four audits (EU AI Act compliance, fairness, explainability, security) that turn every risk into a defensible action plan.
EU AI Act compliance
Risk classification, Annex IV documentation, prioritised compliance roadmap.
Bias & fairness audit
Fairness metrics, debiasing, continuous drift monitoring across your populations.
Explainability (XAI)
SHAP, LIME, counterfactuals. Standardised model cards, ready to publish for regulators.
Model & IP security
AI red teaming, OWASP LLM Top 10, protection against data leakage and prompt injection.
Une gouvernance IA pensée pour vos équipes
Tout ce qu'il faut pour cartographier, contrôler et prouver la conformité de vos systèmes IA.
Cartographie continue
Repérez et classifiez vos systèmes IA sur l'ensemble de votre stack.
Surveillance continue
Détectez les dérives de conformité en continu, sur tous vos systèmes IA.
Auditable par conception
Chaque décision IA documentée, chaque contrôle traçable, prêt pour l'audit.
Conforme AI Act
Classification des risques et mise en conformité outillée, à l'échelle de l'entreprise.
From audit to continuous governance
Audit
AI mapping, EU AI Act classification, prioritised remediation plan.
Action plan
Model cards, explainability, bias monitoring, AI committee and review process.
Continuous governance
Reviews, drift monitoring, regulatory updates, team training.
Why Secure and Govern Your AI?
Anticipate the EU AI Act, GDPR, DORA and NIS 2 without slowing down your projects. We classify your systems by risk level, produce the required documentation (Annex IV) and maintain a compliance file ready to present to regulators and auditors alike.
A model that gets it wrong for the wrong populations exposes you legally and reputationally. We measure and correct bias, set guardrails on outputs, trace every inference and monitor drift continuously.
Your sensitive data and intellectual property face threats specific to AI: prompt injection, data exfiltration, model inversion, supply-chain compromise. AI red teaming, OWASP LLM Top 10, access compartmentalisation and secrets management.
Without a framework, AI scatters into shadow AI and debt accumulates. We put in place the AI committees, RACI, model cards and audit trail that make your use cases manageable, a setup that holds up at group scale.
Pass every audit?
Book an AuditPlatforms in production
Cyber risk mapped by method rather than by intuition
14 EBIOS RM scenarios · GDPR requests under 72 hours
With no solid methodological framework, prioritising cyber investment came down to intuition rather than analysis. Controls existed, but their overall consistency against the most critical scenarios was never formalised.
The ANSSI EBIOS RM method applied across the whole information system, from strategic to operational scenarios: 14 active scenarios, full coverage validated by management and the CISO, and an operational path for handling members' rights.
A diagnostic model that assists without deciding
96% model accuracy · −95% charting time
Designing a deep learning solution that is reliable, fast, compliant with health data hosting rules and GDPR, and that fits the existing practice workflow without replacing the medical decision.
Automated identification of teeth, anomalies and implants on panoramic X-rays, with native explainability and viewable bounding boxes. The practitioner keeps the decision; the model brings precision and traceability.
Unified supervision instead of siloed monitoring
99.99% SLO met · ×4 user capacity
Monitoring was organised by technical layer, which said nothing about what a customer actually experienced. Scaling towards 200,000 active users demanded a view that followed the business journey rather than the infrastructure.
Supervision covering infrastructure, application, network and end-user experience, correlated by business journey: login, transfer, card payment. Peaks are anticipated rather than discovered.
Insights & Perspectives
Securing LLMs in 2026: attack vectors and defence
Six attack vectors against LLMs, prompt injection, GCG, PoisonedRAG, MCP exploits, agentic, and a self-hosted ModernBERT defence at 35 ms.
Testing AI Systems: Building Trust in Non-Determinism
Testing non-deterministic AI systems: data quality, performance thresholds, LLM judges, red teaming and continuous monitoring to build measurable trust.
How Do We Put the Human at the Center of AI?
Tiankai Feng's 5C framework to avoid AI project failures: the right human at the right moment, rigorous testing and governance that is actually owned.
The rest of the AI journey
ExpertiseAI Strategy
Maturity diagnostic, prioritised use cases and an actionable roadmap, from idea to measured results.
ExpertiseAI Agents
AI agents that reason, decide and act, orchestrated and governed in production.
Secure and govern your AI
Book an EU AI Act audit. Together we assess your systems and build your AI governance framework.
Frequently asked questions
It depends on the risk class of each use case, not on your company. The first step is therefore to classify what you already run: a recruitment scoring tool and an internal writing assistant carry very different obligations. Classification comes before any remediation plan.
With a map: which models are in use, on which data, by whom, and for which decisions. Most organisations discover use cases nobody had declared. Without that inventory, any policy applies to a perimeter you cannot see.
Business, IT, legal and security, with a named decision-maker. A committee made only of experts validates technically and blocks nothing; a committee with no technical voice decides on things it cannot assess. Both fail in practice.
By measuring outcomes per population rather than inspecting the model. You need a reference dataset, thresholds agreed in advance and a scheduled review. A bias that appears after six months of drift is only visible to whoever kept measuring.
A short document stating what a model does, on what data it was trained, its known limits and who owns it. It is what allows a decision to be explained a year later, when the person who built the model has left.
Governance decided upfront costs a framing phase. Governance retrofitted costs a rebuild. What slows teams down is not the rule but the rule discovered late, once the use case is already in production and has to be taken apart.
Through scheduled reviews, drift monitoring and a register kept up to date as regulation moves. Governance written once and filed away describes a system that no longer exists six months later.
Yes. A hybrid gateway routes each request by data sensitivity: sovereign or on-premise models for confidential data, external providers for the rest. Sovereignty is a routing decision taken at design time, not a constraint discovered later.
They overlap more than they conflict. DORA covers operational resilience in finance, NIS 2 the security of critical entities, the AI Act the risk of the models themselves. A single register of systems, controls and evidence serves the three instead of three parallel efforts.