Policy is necessary, but it does not control a runtime system
The Financial Stability Board’s 2026 consultation proposes sound practices spanning organisation-wide AI governance and lifecycle management. The emphasis is important: AI risk has to be managed through the organisation and throughout development and deployment, not through a single approval document (FSB, 2026).
Similarly, the EBA’s 2026 risk assessment links AI use with operational resilience, cyber risk, third-party dependency, data security and DORA obligations. These risks are operational and interconnected, which means governance must connect to the systems where those risks materialise (EBA, 2026).
Every governance principle needs an executable expression
'Human oversight' should become a defined approval point. 'Data minimisation' should become an access policy. 'Model monitoring' should become measurable thresholds and alerts. 'Accountability' should become named owners and escalation routes.
This translation from principle to mechanism is the difference between governance documentation and governance capability. It also makes audit more meaningful because the organisation can show evidence of control operation rather than only policy existence.
- 01Policy
- 02Decision rule
- 03Technical control
- 04Runtime evidence
- 05Exception
- 06Governance update
Governance becomes operational when policy can be enforced and evidenced.
Runtime evidence should update governance
AI systems change: model versions, prompts, retrieval data, tools and user behaviour evolve. A control design that was adequate at launch may become insufficient. Governance therefore needs feedback from evaluation results, incidents, override patterns, drift and changing business context.
The governance system should be able to tighten or relax controls based on evidence and risk. That creates adaptive governance rather than one-time certification.
Arvec view: governance is part of architecture
For production AI, governance requirements should appear in architecture diagrams and interface contracts, not only in policy documents. Identity, evidence, authority, evaluation and observability are technical concerns as well as governance concerns.
AIF7™ treats Governance & Trust as one capability pillar, but it must interact with Engineering, Data, Organisation and People. That is why enterprise AI governance is best understood as an operating system for decisions.
Key implications
- 01
Translate every important AI policy principle into an enforceable mechanism.
- 02
Collect evidence that controls operated, not just that policies exist.
- 03
Feed incidents, evaluation and override data back into governance decisions.
- 04
Include governance interfaces in architecture and delivery design.
Related AIF7™ capabilities
AI Governance & Trust
Defines the policy, assurance and accountability layer.
AI Engineering & Delivery
Implements governance as runtime mechanisms.
AI Organisation & Operating Model
Assigns control ownership and escalation.
AI Data Foundation
Enforces data access, provenance and evidence requirements.
Sources & further reading
01 · FSB 2026
Sound Practices for Responsible Adoption of Artificial Intelligence: Consultation reportFinancial Stability Board · 10 June 2026
02 · EBA 2026
Risk Assessment Report – June 2026European Banking Authority · June 2026
03 · BIS 2025
Governance of AI adoption in central banksBank for International Settlements · 29 January 2025