THE GOVERNANCE GAP
An organisation cannot govern AI it cannot see
AI may be introduced through approved projects, embedded software features, external platforms, customer products, analytics tools, or individual employee accounts.
Without a reliable inventory, leadership cannot consistently determine which systems need assessment, who owns them, what data they use, or how their outputs affect people and business decisions.
The first step towards an effective AIMS is a clear view of how the organisation develops, provides, and uses AI.
AIMS STRUCTURE
Bring AI decisions into one governed management system
ISO/IEC 42001 establishes requirements for creating, implementing, maintaining, and continually improving an AI management system within the organisation's context.
ORGANISATIONAL ROLES
Responsibilities change according to how the organisation works with AI
AI developers
Organisations that design, train, customise, test, or materially modify AI systems.
Areas of responsibility
- Design decisions
- Data and model choices
- Testing and validation
- Technical documentation
- Release and change controls
AI providers
Organisations that place AI enabled products, platforms, services, or capabilities into use for customers or other parties.
Areas of responsibility
- Intended use
- Information for users
- Service monitoring
- Customer responsibilities
- Issue management
AI users
Organisations that procure, configure, integrate, or use AI systems within their operations.
Areas of responsibility
- Use case approval
- User competence
- Input controls
- Human oversight
- Output review
Many organisations hold more than one role. Scope and responsibilities must reflect the complete AI value chain.
AI SYSTEM LIFECYCLE
Governance must remain active after deployment
AI risk changes as a system moves from an initial idea into development, use, monitoring, and retirement. Each stage needs defined ownership, decisions, and evidence.
Propose
Document the business purpose, intended users, expected value, ownership, and proposed use of AI.
RISK AND IMPACT
Technical risk and real world impact need separate attention
AI risk assessment
Examine events and conditions that could affect the organisation, its objectives, the AI system, or its users.
Assessment areas
- Security and resilience
- Data quality
- Reliability and performance
- Misuse and unintended use
- Supplier dependency
- Legal and contractual exposure
- Operational failure
- Model and system change
AI system impact assessment
Examine how the intended use and foreseeable use of an AI system may affect individuals, groups, customers, employees, and other interested parties.
Assessment areas
- Nature of the decision or output
- People and groups affected
- Scale and duration of impact
- Human review and recourse
- Transparency requirements
- Potential unfair outcomes
- Accessibility and inclusion
- Social or organisational consequences
GOVERNANCE AREAS
Core AI governance areas
AI policy and accountability
Define leadership expectations, decision rights, responsibilities, escalation routes, and acceptable AI practices.
AI system inventory
Maintain an accurate record of AI systems, purposes, owners, data, suppliers, and status.
AI risk management
Establish consistent criteria for identifying, evaluating, treating, and accepting AI related risks.
AI impact assessment
Review potential consequences for individuals, groups, organisations, and society where relevant.
Data governance
Manage data provenance, quality, preparation, access, suitability, retention, and documented limitations.
Human oversight
Define when people must review, challenge, approve, intervene, or stop an AI enabled process.
Third party AI
Assess external models, platforms, datasets, service providers, contractual responsibilities, and change notifications.
Monitoring and improvement
Review performance, incidents, complaints, changes, control effectiveness, and corrective actions.
OUR SERVICES
Our ISO 42001 services
ISO 42001 gap assessment
Compare existing AI governance and management practices with applicable ISO/IEC 42001 requirements.
Typical outputs
- Clause level findings
- Governance observations
- Evidence gaps
- Prioritised readiness roadmap
AIMS design and implementation support
Define the management system scope, governance model, AI policy, processes, documentation, and operating responsibilities.
Typical outputs
- AIMS implementation plan
- Governance structure
- Responsibility matrix
- Required documentation plan
AI inventory, risk, and impact assessment
Create visibility across AI systems and establish repeatable assessment and approval processes.
Typical outputs
- AI system register
- Risk assessment methodology
- Risk register
- Impact assessment records
AI lifecycle and third party governance
Establish controls for development, procurement, deployment, monitoring, change, suppliers, and retirement.
Typical outputs
- Lifecycle control framework
- Supplier review process
- Approval checkpoints
- Monitoring requirements
Internal audit and certification readiness
Independently review AIMS implementation, operating evidence, management oversight, and readiness for an external certification audit.
Typical outputs
- Internal audit plan
- Audit findings
- Nonconformity register
- Certification readiness report
Unsure whether you need a gap assessment or full AIMS implementation?
Discuss your AI governance scopeEVIDENCE
Evidence behind responsible AI governance
AI DECISION RECORD
Important AI decisions should be traceable
Traceability gives internal teams, customers, auditors, and leadership a clear view of why a decision was made.
ENGAGEMENT PROCESS
How the engagement works
Discover AI use
Identify approved and embedded AI systems, organisational roles, business purposes, owners, data, and providers.
Define AIMS scope
Confirm the business units, AI systems, products, services, locations, processes, and dependencies covered by the management system.
Assess governance gaps
Compare current practices, controls, documentation, and evidence with applicable requirements.
Build and operate
Implement governance processes, assessment methods, lifecycle controls, monitoring, documentation, and assigned responsibilities.
Review and prepare
Complete internal audit, management review, corrective actions, and readiness activities before external certification.
Outcome: A working AI management system supported by clear decisions and current evidence.
CERTIFICATION READINESS
Preparing for certification
Organisational readiness
Before engaging a certification body
Independent certification process
Certification body process
Stage 1 audit
The certification body examines scope, documentation, management system design, and readiness for the main assessment.
Stage 2 audit
The certification body evaluates implementation and whether the AIMS operates in line with the applicable requirements.
Surveillance and recertification
The organisation continues operating and improving the AIMS and participates in the certification body's ongoing audit cycle.
ISO does not certify organisations. Certification is carried out by independent certification bodies. Digisecuritas provides consulting, internal audit, gap assessment, and certification readiness support.
ENGAGEMENT TRIGGERS
When organisations need ISO 42001 support
Scaling AI across the organisation
Move from isolated experiments and team specific practices to a consistent governance model.
Preparing for certification
Build, test, and improve an AIMS before engaging an independent certification body.
Responding to customer scrutiny
Provide stronger evidence of AI governance during procurement, due diligence, and enterprise customer reviews.
Introducing high impact AI use cases
Establish risk, impact, oversight, monitoring, and approval requirements before deploying AI in important decisions or services.
AI governance has to survive contact with real operations
Digisecuritas connects management system requirements with the way AI is developed, procured, configured, used, monitored, and changed.
Our assessments are grounded in evidence and operating context. Leadership receives a clear view of responsibility, exposure, priorities, and the work required before certification.
Independent assessment
Receive an objective view without AI product or platform bias.
AI and cybersecurity perspective
Assess governance alongside data security, privacy, access, third parties, resilience, and incident readiness.
Business aligned scope
Focus the AIMS on relevant systems, services, decisions, users, and dependencies.
Practical documentation
Create records that reflect actual AI use and management decisions.
Executive reporting
Present material AI risks, impacts, responsibilities, and priorities in clear language.
FAQ
Frequently asked questions
Build accountable AI into the organisation
Understand your ISO 42001 gaps, define AIMS priorities, and prepare for independent certification with a practical implementation plan.