The progressive implementation of the EU AI Act marks a major shift for organizations that develop, integrate, deploy, or use artificial intelligence systems. Beyond meeting regulatory obligations, the challenge is to transform legal requirements into operational, measurable, and auditable processes.
This is precisely the role of an Artificial Intelligence Management System (AIMS). An AIMS provides a structured framework for organizing AI governance, operationalizing AI risk management, demonstrating compliance, and establishing a sustainable process for continuous improvement.
For Compliance Officers, Data Protection Officers, Chief Information Security Officers, Risk Managers, and Chief AI Officers, the challenge is no longer limited to understanding the AI Act. Organizations must now build an AIMS for AI Act compliance that can withstand an audit, govern AI use effectively, and support responsible innovation.
In this article, we explain the practical steps required to move from AI Act requirements to an operational AIMS, drawing on AI governance principles, AI risk management practices, and the ISO 42001 standard.
Why the AI Act Requires Organizations to Structure Their AI Governance?
The AI Act does not simply impose technical obligations. It requires organizations to establish structured governance capable of identifying, controlling, and documenting their use of AI.
The Objectives of the EU AI Act
The AI Act aims to:
- Protect individuals’ fundamental rights, health, and safety
- Strengthen trust in artificial intelligence
- Promote the adoption of human-centric AI
- Reduce the risks associated with AI systems
- Define the responsibilities of AI system providers and deployers
- Support innovation
- Harmonize the European market
The regulation is based on a risk-based approach. The greater the potential impact of an AI system on health, safety, or fundamental rights, the more extensive the applicable compliance obligations become.
New Responsibilities for Organizations
Organizations must now be able to demonstrate:
- Which AI systems they use
- In which contexts those systems are deployed
- What risks they generate
- Which controls are applied
- How incidents are handled
This requirement to provide evidence of responsible AI use makes a formal AI governance framework increasingly necessary.
Why Ad Hoc Approaches Are No Longer Sufficient
Many organizations already use AI assistants, generative AI models, scoring systems, predictive analytics tools, or automated decision-making solutions.
The problem is that these tools are often adopted without a centralized inventory, formal risk assessment, or clearly defined oversight.
As regulatory obligations increase, fragmented approaches become more difficult to manage, monitor, and audit. An operational AIMS provides the centralized structure required to address this challenge.
What Is an Artificial Intelligence Management System (AIMS)?
Summary: An AIMS is a management framework for organizing governance, risk management, and compliance across all AI systems used by an organization.
Definition of an AIMS
An Artificial Intelligence Management System (AIMS) is a structured system that brings together:
- AI policies
- Roles and responsibilities
- AI risk management processes
- Internal controls
- Monitoring mechanisms
- Performance indicators
The purpose of an AIMS is to ensure the continuous management of risks associated with artificial intelligence.
An AIMS is not limited to a collection of procedures or compliance documents. It provides a structured governance framework covering the entire lifecycle of AI systems, from the identification of tools and models to their continuous monitoring.
By centralizing decisions, risks, controls, evidence, and performance indicators, an AIMS helps organizations demonstrate AI Act compliance while ensuring the consistent, sustainable, and measurable management of their AI initiatives.
The Core Principles of an AIMS
Like other management systems based on standards such as ISO 27001 or ISO 9001, an AIMS is built around several fundamental principles:
| Principle | Objective |
| Policy | Define AI governance rules |
| Planning | Identify risks and establish objectives |
| Control | Verify the effectiveness of measures |
| Audit | Assess compliance and performance |
| Continuous improvement | Address gaps and improve processes |
The Relationship Between AIMS and AI Governance
AI governance defines the rules, principles, and responsibilities that govern the use of artificial intelligence within an organization.
An AIMS transforms these strategic principles into concrete actions through formalized processes, operational controls, clearly assigned responsibilities, measurable indicators, and auditable evidence.
In practice, AI governance defines what must be done, while an AIMS ensures that these requirements are implemented, measured, monitored, and continuously improved.
This is what enables organizations to demonstrate AI Act compliance, manage AI-related risks, and establish genuinely operational AI governance.
The Foundations of an AI Act-Compliant AIMS
Summary: Before launching assessments or audits, organizations must establish the foundations of their Artificial Intelligence Management System.
AI Governance and Accountability
Effective AI governance relies on a clear structure for making decisions, managing risks, and demonstrating compliance.
An AIMS governance structure will typically involve:
- An executive sponsor responsible for strategic oversight
- An AI governance committee responsible for major decisions
- An AI Compliance Officer or AIMS Manager responsible for coordinating the framework
- A DPO responsible for personal data protection matters
- A CISO responsible for cybersecurity matters
- Business and technical owners responsible for individual AI systems
To make the AIMS operational, these responsibilities must be formally documented, communicated, and integrated into the organization’s existing processes. Each stakeholder should understand their role in the management, use, monitoring, and oversight of AI systems.
Corporate AI Policy
A corporate AI policy is the reference document governing the use of artificial intelligence within the organization. It translates AI governance objectives into concrete rules that can be applied by business, technical, compliance, risk, and security teams.
The policy should define:
- Authorized and prohibited AI uses
- Approval criteria for new AI projects
- Human oversight requirements
- Documentation and traceability requirements
- Risk assessment requirements
- Escalation and incident management procedures
By establishing a common framework, the AI policy encourages the consistent, responsible, and compliant use of AI across the organization.
AIMS Risk Management Framework
AI risk management is at the heart of an Artificial Intelligence Management System. The AIMS risk management framework should identify, assess, monitor, and address risks throughout the AI system lifecycle.
It should cover:
- Regulatory risks associated with the AI Act
- Operational risks affecting business activities
- Cybersecurity risks associated with models and data
- Privacy and data protection risks
- Ethical risks
- Reputational risks affecting stakeholder trust
A structured AIMS risk management process supports informed decision-making, helps prioritize compliance activities, and demonstrates the continuous management of AI-related risks.
Documentation and Traceability
Every significant decision made within the AIMS should leave a documented record.
This includes:
- AI risk assessments
- AI Act classification decisions
- Governance decisions
- Controls performed
- Identified issues
- Reported incidents
- Corrective actions
- Approval and review history
Documentation and traceability are essential for demonstrating that the AIMS is operational and not simply a collection of theoretical policies.
AIMS Internal Controls
Internal controls are one of the core components of an AIMS.
Their purpose is to ensure that AI governance requirements, AI Act obligations, and internal policies are applied consistently over time.
For controls to be effective, they should be:
- Systematic
- Documented
- Repeatable
- Assigned to a specific owner
- Monitored through appropriate indicators
Examples of AIMS controls include:
- Approval of new AI use cases before deployment
- Evaluation of AI suppliers
- Periodic reviews of AI systems
- Verification of access rights
- Monitoring of system performance
- Review of AI-generated outputs
- Control of compliance documentation
Internal controls help organizations identify gaps, implement corrective actions, and demonstrate control over their AI systems during an audit or regulatory review.
Step 1: Build a Centralized AIMS Inventory
Mapping AI systems is the starting point of an effective AI Act AIMS. An organization cannot govern what it has not identified.
Identify AI Use Cases
The first step in implementing an AIMS is obtaining a complete view of all the AI systems used within the organization.
This exercise must go beyond officially declared projects. Many AI tools are now adopted directly by business teams without prior approval from compliance, risk, IT, or security departments.
Organizations should identify uses such as:
- Generative AI
- Chatbots
- HR tools
- Predictive analytics
- Fraud detection systems
- Marketing tools
- Scoring solutions
- Automated decision-making systems
- AI-enabled cybersecurity tools
This mapping exercise provides the foundation of the AIMS. It enables the organization to evaluate risks, determine applicable AI Act obligations, and implement appropriate controls for each system.
Example
While creating its AIMS inventory, an organization discovers that its marketing team uses content-generation tools, its HR department uses a resume-screening solution, and its sales department relies on a conversational AI assistant.
None of these systems had previously been inventoried or assessed against the AI Act.
This discovery demonstrates why a centralized AIMS inventory is necessary before launching a wider compliance program.
Classify AI Systems Under the AI Act
For each system included in the AIMS inventory, the organization should:
- Determine its risk category.
- Identify the organization’s role in relation to the system.
- Determine the applicable obligations.
- Document the classification decision.
- Define the required controls and monitoring activities.
Structure the AIMS Inventory
A centralized AIMS inventory should include:
| Element | Description |
| System name | Tool, application, or model used |
| Supplier | Provider, vendor, or developer |
| Use case | Business function and intended purpose |
| AI Act classification | Applicable risk category |
| Organizational role | Provider, deployer, importer, or distributor |
| Business owner | Person accountable for the use case |
| Technical owner | Person responsible for technical oversight |
| Risks | Identified regulatory, operational, and security risks |
| Controls | Measures implemented to manage the risks |
| Status | Assessment, approval, and monitoring status |
Step 2: Conduct AI Risk Assessments Within the AIMS
Summary: AI risk assessments transform the AIMS inventory into a decision-making tool and help organizations prioritize their compliance efforts.
Regulatory Risks
Regulatory risks concern the organization’s ability to meet the legal requirements applicable to its AI systems.
Under the AI Act, these risks may arise from:
- Incorrect classification of an AI system
- Incomplete documentation
- Insufficient risk assessments
- Failure to implement applicable obligations
- Lack of evidence demonstrating that controls are effective
A structured AIMS reduces these risks by ensuring decision traceability, centralizing compliance evidence, and continuously monitoring applicable obligations.
Operational Risks
Operational risks are among the most common risks associated with the use of AI systems. They concern the direct impact that models may have on business processes, decision quality, operational performance, or service delivery.
Operational risks can include:
- Hallucinations producing incorrect information
- Errors in automated decisions
- Bias affecting certain users
- Inconsistent outputs
- Model performance degradation
- Excessive dependence on automated processes
Identifying and monitoring these risks allows organizations to implement appropriate controls and ensure that AI systems remain reliable and aligned with business objectives.
Cybersecurity Risks
Cybersecurity is a critical component of AIMS risk management. AI systems introduce new attack surfaces that must be considered during design, deployment, and operation.
The CISO and security teams should assess risks such as:
- Leakage of sensitive information
- Prompt injection attacks
- Data poisoning
- Model manipulation
- Unauthorized access to AI tools
- Unauthorized access to training or operational data
- Vulnerabilities introduced by third-party AI providers
Continuous monitoring and appropriate security controls help reduce these risks and increase trust in the organization’s AI systems.
Ethical and Reputational Risks
Ethical and reputational risks can be more difficult to measure than technical or regulatory risks, but their consequences can be equally significant.
An incident involving an AI system can rapidly affect the trust of customers, employees, partners, investors, or supervisory authorities.
A decision perceived as biased, opaque, unfair, or insufficiently supervised may lead to negative media coverage, damage the organization’s reputation, and affect business performance.
An operational AIMS should therefore include monitoring and assessment mechanisms designed to anticipate these risks and protect stakeholder trust.
AIMS Risk Register Best Practice
Organizations should maintain a centralized AI risk register containing:
- Risk description
- Affected AI system
- Risk category
- Likelihood
- Potential impact
- Existing controls
- Residual risk
- Risk owner
- Remediation plan
- Target completion date
- Review status
Step 3: Implement AI Act Controls Through the AIMS
Summary: AIMS controls provide evidence that identified AI risks are effectively understood, monitored, and managed.
Technical Documentation
Technical documentation plays a central role in AI Act compliance and AIMS audit readiness.
It should demonstrate that each AI system is understood, controlled, and used within a clearly defined framework.
The documentation should explain:
- The purpose of the AI system
- Its intended use
- How the system operates
- The data used for training, testing, or operation
- Its known limitations
- Its identified risks
- Its conditions of use
- Its human oversight mechanisms
- Its monitoring arrangements
Beyond regulatory compliance, technical documentation supports transparency, traceability, internal controls, and preparation for internal or external audits.
Human Oversight
Human oversight is a central aspect of the AI Act and should be formally integrated into the AIMS.
The organization should define:
- Who validates AI-generated outputs
- Who monitors system performance
- Who reviews abnormal behavior
- Who can override a decision
- Who has the authority to suspend or deactivate the system
- How human intervention is documented
Effective human oversight helps limit errors, identify unexpected behavior, and strengthen confidence in AI-assisted decisions.
AIMS Performance Monitoring
The organization should regularly monitor:
- Accuracy
- Error rates
- False-positive and false-negative rates
- Bias indicators
- Drift indicators
- System availability
- Human override rates
- Reported incidents
- User complaints
The frequency and depth of monitoring should be proportionate to the system’s risk level and intended use.
AI Incident Management
Even with robust preventive controls, no AI system is entirely free from incidents.
An effective AIMS should include a formal incident management process that covers:
- Detection
- Initial reporting
- Qualification
- Severity assessment
- Escalation
- Containment
- Remediation
- Regulatory notification, where applicable
- Root-cause analysis
- Lessons learned
Each incident should result in a structured review to identify its causes, strengthen existing controls, and improve the organization’s overall AI risk management framework.
AI Supplier Management
Supplier management is a major compliance and risk management issue, particularly when organizations rely on AI models, platforms, or services developed by third parties.
This area is frequently overlooked when implementing an AIMS.
Organizations should regularly assess:
- Contractual commitments
- Security safeguards
- Data protection measures
- AI Act compliance information
- Documentation provided by the supplier
- Incident notification procedures
- Audit rights
- Certifications and independent assurances
- Use of subcontractors
- Conditions for changing or discontinuing the service
Effective supplier governance helps organizations manage risks transferred to external parties and secure their broader AI ecosystem.
Step 4: Operationalize AI Act Compliance with an AIMS
Summary: An AIMS should not be treated as a one-time compliance project. It should become a permanent governance and management system. Implementing an AIMS is only the first step. Its real value lies in its ability to operate over time. To meet AI Act requirements and maintain a consistent level of compliance, the AIMS must become a permanent mechanism for governance, control, monitoring, and continuous improvement.
The objective is to monitor changes in AI systems, associated risks, and regulatory obligations through structured processes supported by measurable indicators.
AIMS Indicators and Reporting
Effective AIMS management relies on indicators that measure the performance of governance and compliance processes.
Relevant AIMS KPIs may include:
- Number of inventoried AI systems
- Percentage of systems with an assigned owner
- AI risk assessment completion rate
- Number of open incidents
- Number of overdue remediation actions
- Percentage of evaluated AI suppliers
- Number of completed controls
- Percentage of high-risk systems reviewed on schedule
- Number of identified compliance gaps
- Average remediation time
These indicators provide an objective view of the organization’s AI governance maturity. They also facilitate prioritization, executive decision-making, and reporting to senior management.
AIMS Governance Reviews
AI governance must be actively managed so that it can adapt to technological, regulatory, and business developments.
Regular AI governance committee reviews should examine:
- New AI use cases
- Changes to existing systems
- Emerging risks
- Reported incidents
- Control results
- Compliance gaps
- Remediation plans
- Regulatory developments
These reviews support active oversight of the AI portfolio and help ensure that governance decisions remain aligned with organizational objectives.
AIMS Internal Audits
Internal audits play an essential role in evaluating the effectiveness of an AIMS.
They help verify that:
- Procedures are actually followed
- Controls produce the expected results
- Responsibilities are understood
- Compliance evidence is complete
- Risk assessments are up to date
- Corrective actions are implemented
In addition to preparing for external audits or future ISO 42001 certification, internal audits help identify weaknesses and opportunities for improvement.
AIMS Continuous Improvement
Like any management system, an AIMS should follow a continuous improvement approach.
Each audit finding, incident, control failure, or compliance gap should lead to an action plan with:
- A clearly defined corrective action
- An assigned owner
- A target completion date
- Documented follow-up
- Verification of effectiveness
This process progressively improves AI risk management, strengthens compliance, and allows the organization’s governance framework to adapt to new AI uses.
Why ISO 42001 Is an Effective Framework for Structuring an AIMS?
Summary: ISO 42001 provides the management structure that is often missing from purely regulatory compliance programs.
What ISO 42001 Covers
ISO 42001 has become a major reference for organizations seeking to establish a robust AI governance framework and operational AIMS.
It covers:
- AI governance
- Leadership and accountability
- Policies
- Planning
- AI risk management
- Controls
- Performance evaluation
- Internal audits
- Continuous improvement
Synergies Between the AI Act and ISO 42001
| AI Act | ISO 42001 |
| Regulatory requirements | Management system framework |
| Compliance obligations | Operational governance processes |
| Legal obligations | Documented procedures |
| Risk-related requirements | Structured risk management |
| Human oversight | Defined accountability mechanisms |
| Compliance evidence | Controlled documentation |
| Post-market monitoring | Continuous performance evaluation |
An ISO 42001-aligned AIMS provides an effective structure for organizing and maintaining the evidence required to support AI Act compliance.
Preparing for Future ISO 42001 Certification
Even when ISO 42001 certification is not an immediate objective, applying the principles of the standard can help organizations structure their AI governance over the long term.
This approach enables organizations to:
- Formalize AIMS processes
- Clarify roles and responsibilities
- Implement consistent controls
- Improve decision traceability
- Strengthen risk assessments
- Prepare for internal and external audits
- Increase stakeholder confidence
Beyond compliance, progressively aligning an AIMS with ISO 42001 can strengthen the organization’s credibility with customers, partners, investors, and supervisory authorities.
As AI Act requirements and AI governance expectations continue to evolve, an ISO 42001-aligned AIMS can become a strategic advantage.
Organizations can improve their maturity, reduce non-compliance risks, and prepare for formal certification when the time is right. This is the approach supported by Naaia.
Common Mistakes When Implementing an AIMS
AIMS implementation failures rarely result from a lack of regulatory knowledge. They usually result from a lack of operationalization.
Focusing Only on Compliance Documentation
Documents without real processes do not provide sustainable risk management. An effective AIMS must demonstrate that policies are applied, controls are performed, responsibilities are assigned, and corrective actions are monitored.
Overlooking AI Suppliers
External models, tools, and platforms introduce risks that are frequently underestimated. Supplier assessments should be an integral part of the AIMS rather than an isolated procurement exercise.
Failing to Define Responsibilities
Without clear ownership, governance remains theoretical.
Each AI system, risk, control, incident, and remediation action should have an identified owner.
Failing to Measure Control Effectiveness
A control that is not measured cannot be effectively managed. Organizations should define how each significant control is performed, how often it is reviewed, and how its effectiveness is demonstrated.
How an AI Governance Platform Can Accelerate AIMS Implementation
Summary: Specialized AI governance platforms help centralize evidence, automate assessments, and operationalize AIMS controls.
Centralizing AIMS Evidence
An AI governance platform can centralize:
- AI inventories
- AI Act classifications
- Risk assessments
- Internal controls
- Supplier assessments
- Compliance documentation
- Audit records
- Incident reports
- Remediation plans
- Governance indicators
This provides teams with a shared and consistent source of information.
Automating AIMS Assessments
Structured questionnaires and workflows can significantly reduce the administrative burden associated with assessments.
They can also help standardize:
- AI use case registration
- Risk assessments
- Supplier evaluations
- Control reviews
- Approval workflows
- Periodic reassessments
Continuous AI Risk Oversight
A governance platform gives compliance and risk teams a consolidated view of:
- AI systems
- Identified risks
- Compliance gaps
- Applicable obligations
- Control implementation
- Remediation progress
This enables AIMS stakeholders to move from periodic, document-based compliance to continuous governance.
Practical AIMS Use Case
A Compliance Officer is responsible for overseeing dozens of AI systems used by different business functions.
Without an AIMS platform, information remains scattered across spreadsheets, emails, local documents, and separate questionnaires.
With a specialized solution such as Naaia, inventories, assessments, controls, evidence, and governance indicators can be centralized in a single repository.
Why Naaia Provides a Ready-to-Use AIMS for AI Act Compliance?
Implementing an Artificial Intelligence Management System compliant with the AI Act requires much more than writing procedures or creating an AI risk register.
Organizations must centralize their AI inventory, formalize risk assessments, document controls, track remediation plans, and demonstrate compliance over time.
This is precisely what Naaia was designed to support.
The platform helps organizations transform AI Act requirements and ISO 42001 recommendations into an operational, auditable, and measurable AIMS.
While many organizations still depend on spreadsheets, fragmented questionnaires, emails, and manual processes, Naaia provides a single repository for governing the entire AI lifecycle.
Naaia enables organizations to:
- Map and maintain a centralized inventory of AI systems
- Conduct AI risk assessments aligned with the AI Act
- Structure an ISO 42001-aligned AIMS
- Formalize roles, responsibilities, and decision-making processes
- Centralize compliance evidence and required documentation
- Manage controls, audits, and remediation plans
- Monitor AI governance and risk management indicators
- Evaluate AI suppliers
- Prepare for internal audits and regulatory reviews
- Support preparation for ISO 42001 certification
Naaia does not simply support isolated compliance activities. It provides the foundational components of an AI governance framework and a complete Artificial Intelligence Management System.
For organizations seeking to move quickly from theory to execution, Naaia provides the operational layer connecting the AI Act, AI governance, AI risk management, AIMS, and ISO 42001 within a single measurable and scalable framework.
Assess the maturity of your AI governance framework
Is your organization already using artificial intelligence tools but lacking visibility into risks, responsibilities, or compliance requirements?
Conduct an AI governance assessment with Naaia to identify your AI use cases, inventory your systems, evaluate risks, and implement a governance framework aligned with applicable AI requirements, including those introduced by the AI Act and ISO/IEC 42001.
