Govern AI
Implement an AI governance model that aligns with EU AI Act, business priorities, ethics, and cybersecurity.
The EU AI Act: A legal framework for innovative, trustworthy, and responsible AI.
The EU AI Act is the world’s first comprehensive regulation on artificial intelligence. It introduces a risk-based approach to ensure that AI systems developed and used in Europe are ethical, transparent, and aligned with fundamental EU values.
Regulatory harmonization
Establish a unified legal framework for the development, marketing, and use of AI systems across the EU.
Fundamental rights
Ensure safety, transparency, and accountability in AI technologies.
Fostering innovation
Promote the adoption of trustworthy AI without hindering the competitiveness of European businesses.
Three Building Blocks of AI Governance
AI Lifecycle Governance
Embed compliance and risk management throughout AI system design, development, procurement, deployment, and evolution. Incorporate automation, incident handling, and change management to ensure ongoing adherence and innovation.
AI Governance Organisation
Structure teams around dedicated roles (AI GRC Specialist, Compliance Officer, Ambassador), and establish a central AI hub to unify oversight, portfolio management, and knowledge sharing.
AI Governance Compliance
Implement a dynamic conformity assessment framework, adapt internal audit methodologies for evolving AI systems, and maintain open channels with regulators through standardized reporting and proactive communication.
Who Falls Under the Scope of the EU AI Act?
The regulation applies to four main types of organizations involved in the AI value chain:
Provider: Develops or places an AI system or general-purpose AI model on the EU/EEA market.
Importer: Brings an AI system into the EU market from a non-EU country.
Distributor: Makes an AI system available within the EU as part of the supply chain, without being its developer or importer.
Deployer: Uses an AI system under their authority in a professional or institutional context.
AI System Classification by Risk Level
1
Unacceptable risk: Prohibited AI systems (e.g., social scoring, cognitive manipulation, real-time facial recognition in public spaces).
2
High risk: Subject to strict compliance requirements (e.g., AI used in recruitment, credit scoring, critical infrastructure). High-risk AI systems pose significant risks to fundamental rights and must undergo third-party conformity assessments before deployment.
3
Limited risk: Subject to transparency obligations (e.g., chatbots, deepfakes).
4
Minimal risk: Freely usable under good practices.
Implementation Timeline
Ban Comes Into Force
Prohibitions and general provisions of the regulation on unacceptablerisk AI are applied.
GPAI Compliance Begins
Obligations for general-purpose AI (GPAI) models will come into effect.
Core Regulation Applies
Main body of the regulation will be effective, except for certain provisions related to high-risk AI systems.
High-Risk AI Obligations Enforced
Obligations imposed on high-risk AI systems become applicable.
What is GPAI?
General-Purpose AI (GPAI) refers to AI models capable of performing a wide range of tasks and being integrated into various downstream systems, regardless of how they are distributed.
Key obligations for GPAI providers include:
Conducting Fundamental Rights Impact Assessments (FRIAs) and conformity checks
Implementing risk and quality management systems
Ensuring transparency and AI content labelling
Testing for accuracy, robustness, and cybersecurity
Enforcement and Penalties
The EU AI Act includes significant enforcement powers and financial penalties for non-compliance:
Prohibited AI Violation
Up to 7% of global annual turnonver or 35 million euros
Misleading Information
Up to 1% of global annual turnonver or 7.5 million euros
Other Violations
Up to 3% of global annual turnover or 15 million euros
National market surveillance authorities will monitor compliance, report annually to the European Commission, and take corrective actions where needed.
AI Governance Lifecycle – Cybersecurity-Driven Approach
The EU AI Act redefines AI governance as a core element of digital risk management. Here’s how to build a secure, compliant, and scalable AI governance framework.
Requirement Scoping
Define clear guidelines aligned with the AI Act and cybersecurity objectives.
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Set governance criteria and entry points for AI
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Prioritise high-impact, high-risk use cases
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Align AI with security and compliance strategy
Secure Development
Embed compliance and security controls throughout the AI lifecycle (for AI providers).
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Include regulatory checks in design and testing
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Document decisions, datasets, and model logic
Third-Party Risk
Assess the compliance and security posture of external AI solutions (for AI deployers).
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Integrate AI requirements into procurement
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Evaluate vendors’ transparency, auditability, and risk management
Operationalisation
Implement standardised, automated controls to ensure continuous compliance.
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Classify systems by risk level
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Deploy repeatable governance processes
Incident Management
Prepare for AI-related failures, misuse, or security breaches.
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Establish incident response workflows
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Escalate and mitigate AI compliance or safety issues
Change Management
Control the impact of changes to AI systems over time.
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Review updates before deployment
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Train teams on responsible AI evolution
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Maintain traceability
Build a Robust and Sustainable AI Governance Framework
Stroople helps you implement an AI governance model that not only meets the requirements of the EU AI Act, but also aligns with your business priorities, ethical standards, and cybersecurity posture.
We follow a four-phase approach, adaptable to any industry or organizational size:
1. Baselining
We begin with a thorough assessment of your current AI usage, governance maturity, and risk landscape. This includes reviewing data management, decision processes, compliance controls, and any existing AI practices. The goal is to create a tailored foundation that integrates with your broader governance systems (data, cybersecurity, regulatory, etc.).
Our AI risk assessments use data-driven FAIR™ frameworks to identify threats, quantify impacts, and develop mitigation strategies that safeguard your business and users. Where relevant, we apply FAIR to provide tangible financial and operational risk metrics tailored to your AI systems.
2. Design
We help you define the core elements of your AI governance framework: policies, practical guidelines, and detailed requirements. These address key principles such as transparency, data privacy, algorithmic accountability, and ethical risk management. Roles and responsibilities are clearly established.
3. Operationalisation
This phase focuses on embedding governance into day-to-day operations. We integrate your new standards into workflows, and implement tools to automate compliance (e.g. audit trails, bias detection, risk classification). AI governance becomes part of your operational DNA.
4. Adaptation
We support the cultural transformation needed to make AI governance sustainable. This includes employee training, awareness sessions, and empowerment programs. The objective is to build AI literacy and encourage responsible innovation across the organization.
Cybersecurity in the age of AI
Download our white paper to gain the clarity and perspective you need.
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La cybersecurité à l'ère de l'IA
Comment maîtriser les modèles d’IA et les menaces émergentes ?
Quels standards et framework de sécurité pour adopter les meilleures pratiques d’intégration ?
Comment la gouvernance et les réglementations anticipent l’évolution des rôles clés en cybersécurité ?
Notre livre blanc vous apportera les 1ères réponses pour vous permettre de naviguer en confiance dans le monde passionnant de l’IA !