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Banking AI Governance Assessment Review

Section-by-section review of the banking-specific AI governance assessment implemented for financial institutions. The assessment is designed to evaluate operational governance maturity, not only route creation.

Review sections Back to banking governance

Assessment section

Institution Profile

Which best describes your organization?
Which function is primarily completing this assessment?

Assessment section

AI Inventory & Visibility

How complete is your inventory of AI systems, copilots, models, agents and AI-enabled vendor tools?
How does your organization identify shadow AI or unmanaged AI use?
Which high-impact workflow areas currently use or are evaluating AI?

Assessment section

Agentic AI Governance

Does your organization currently use or pilot autonomous AI Agents?
Can AI Agents execute multi-step business workflows or delegated actions?
Are AI Agent owners, permissions, actions, audit trails and production workflows traceable?

Assessment section

Governance & Accountability

How clearly is accountability assigned for AI systems and AI-assisted workflows?
Which governance bodies review material AI use cases?
How well do AI policies become repeatable operating controls?

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Risk, Data & Third Parties

How consistently do teams identify customer data, confidential data or regulated data exposure in AI workflows?
How are third-party AI providers and AI-enabled vendor tools governed?

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Audit & Evidence Readiness

If internal audit requested AI governance evidence today, how quickly could teams assemble it?
How mature are audit trails for AI intake, approvals, risk decisions, exceptions and periodic reviews?
Which materials could you produce reliably for a regulator, supervisor or board-level review?

Assessment section

Operating Model & Resilience

How integrated is AI governance with technology risk, security, privacy, resilience and change management?
What is the highest-priority governance improvement for the next 6 months?

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Share your governance benchmark intake

No score or automated report is generated. Responses are used as structured governance context for enterprise follow-up.

Implemented sections

Complete section list and question summary

1. AI Inventory and Visibility

Questions confirm whether the bank has a current AI inventory, which systems and workflows are in scope, how business context is captured, and whether unmanaged or shadow AI can be detected across departments.

  • Can the institution identify AI systems, copilots, agentic workflows and external tools in use across business units?
  • Are AI use cases classified by business function, materiality, regulated data exposure and operational dependency?
  • How are shadow AI activity, employee-introduced tools and unapproved workflows surfaced for governance review?

2. Agentic AI Governance

Questions evaluate whether autonomous AI Agents are in use or pilot, whether they can execute delegated multi-step workflows, and whether owners, permissions, actions, audit trails and production workflows are traceable.

  • Does the organization currently use or pilot autonomous AI Agents?
  • Can AI Agents execute multi-step business workflows or delegated actions?
  • Are AI Agent owners, permissions, actions, audit trails and production workflows inventoried and traceable?

3. Ownership and Accountability

Questions test whether accountable owners, control owners and review paths are assigned for AI systems, model-enabled workflows, AI-generated records and third-party services.

  • Does each AI system or workflow have an accountable business owner and governance owner?
  • Are approval, escalation and exception paths defined for high-impact banking workflows?
  • Can ownership be evidenced when AI-supported decisions, records or outputs are challenged?

4. Compliance and Regulatory Readiness

Questions assess whether AI governance maps to financial-services obligations, regulatory readiness, internal policies and records needed for supervisory or compliance review.

  • Which AI systems process customer, personal, confidential, regulated or transaction-related data?
  • How are EU AI Act, DORA, operational resilience and internal governance expectations reflected in the operating model?
  • Can compliance teams retrieve the evidence needed for periodic reviews and regulatory inquiries?

5. Risk Management

Questions evaluate how the bank identifies, rates and prioritizes AI risks across business impact, customer exposure, operational resilience, model dependency and control maturity.

  • Are AI use cases risk-rated using consistent criteria across business lines?
  • How are higher-risk use cases routed to risk, compliance, legal, security or internal audit review?
  • Are risk decisions, mitigations, residual risk and review dates preserved as durable records?

6. Legal and Regulated Data Processing

Questions focus on legal review triggers, data processing boundaries, customer data controls, retention expectations, explainability needs and contractual exposure.

  • Are legal review triggers defined for customer-facing, decision-supporting or regulated-data AI workflows?
  • Can teams identify where prompts, documents, embeddings, outputs or logs may contain regulated data?
  • Are retention, deletion, access and cross-border processing expectations connected to AI records?

7. Third-Party AI Providers

Questions review vendor dependency, outsourced service oversight, contractual controls, data handling commitments, resilience and evidence from external AI providers.

  • Which AI workflows depend on third-party models, copilots, APIs, platforms or managed services?
  • Are vendor obligations, data use restrictions, audit rights and resilience commitments documented?
  • Can third-party AI dependencies be reported by business owner, process, risk tier and evidence status?

8. Security and IT Governance

Questions verify whether IT and security teams can govern access, integrations, data movement, approved tools, monitoring signals, change management and lifecycle controls.

  • Are approved AI tools, integrations and access paths governed through IT control processes?
  • Can security teams identify sensitive-data exposure, unmanaged integrations and policy exceptions?
  • Are lifecycle changes, tool approvals and decommissioning decisions connected to the AI inventory?

9. Internal Audit and Evidence Readiness

Questions determine whether the institution can produce audit-ready records showing inventory coverage, ownership, approvals, risk decisions, policy exceptions and review history.

  • How quickly can internal audit assemble evidence for AI governance, risk and control reviews?
  • Are decisions, approvals, exceptions, assessments and review artifacts centrally retrievable?
  • Can audit teams compare evidence completeness across business units or control domains?

10. Governance Operating Model and Policy Coverage

Questions assess whether policies are operationalized into roles, committees, intake workflows, review cadences, control ownership, reporting routines and executive oversight.

  • Which committees, functions and control owners participate in AI governance decisions?
  • Do policies cover generative AI, copilots, model-enabled workflows, third-party AI and shadow AI?
  • Are governance decisions converted into records that support management reporting and benchmarking?

Coverage confirmation

Required functional domains are covered

Compliance
Risk
Legal
Internal Audit
Security
AI Governance
IT Governance

Banking topics

Banking-specific governance topics are included

AI inventory Agentic AI governance Autonomous AI Agents Ownership and accountability Shadow AI Third-party AI providers Regulated data processing Audit readiness Governance operating model Risk management Policy coverage Evidence and records Regulatory readiness

Benchmarking readiness

Collected data is suitable for future comparative reporting

The assessment structure uses repeatable sections, scored maturity indicators and normalized topic flags so results can support future benchmarking by institution type, function, geography and governance maturity band.

Institution profile: geography, institution type, business lines and respondent function.
Inventory coverage: system/workflow count bands, owner coverage, risk-tier coverage and review status.
Control maturity: scored responses for policies, evidence, risk routing, vendor oversight, security and IT governance.
Governance evidence: record availability, audit-readiness timeline, exception handling and review cadence.
Comparative dimensions: normalized section scores, maturity bands, topic flags and cross-functional coverage indicators.