AI Governance FAQ

AI governance is the framework of policies, processes, and controls that ensure AI systems are used responsibly, transparently, and in compliance with regulations. It includes: traceability (recording every AI interaction), human oversight (human review and approval), audit documentation (regulatory-ready logs), risk management, and regulatory compliance (AI Act, DORA, GDPR).

AI governance is critical for regulated industries because:

• Regulatory requirements: AI Act and DORA mandate transparency and human oversight for AI systems
• Financial penalties: Non-compliance can result in fines up to 4% of global revenue under GDPR, or €35 million under AI Act
• Audit requirements: Financial institutions must demonstrate audit trails for regulatory examinations
• Board accountability: Organizations must demonstrate responsible AI use to boards and stakeholders
• Legal defense: Without governance, AI decisions cannot be defended in audits or legal proceedings

AI governance differs from traditional IT governance in several key ways:

• Probabilistic outputs: AI produces non-deterministic results, requiring different validation approaches
• Explainability: AI decisions must be traceable to source data and reasoning
• Hallucination risk: AI can produce false information, requiring source attribution
• Regulatory specificity: AI Act imposes requirements that don't exist for traditional software
• Human oversight: High-risk AI decisions require human validation in ways standard IT systems don't

AI traceability means every AI-generated output can be traced back to its source documents, the specific prompt used, the model version, and the human validators involved. A complete traceability framework captures:

• Input: What question was asked, by whom, and when
• Data: What documents or data sources were consulted
• Process: How the AI reached its conclusion
• Output: What the AI generated or recommended
• Validation: Who reviewed and approved the output, and when

The EU AI Act is European regulation that sets requirements for AI systems based on their risk level. Key provisions include:

• Risk classification: AI systems are categorized as minimal, limited, high, or unacceptable risk
• High-risk requirements: Transparency, documentation, human oversight, traceability (effective August 2026)
• Prohibited practices: Certain AI applications are banned outright
• Enforcement: National authorities with inspection and sanctioning powers

Penalties: Up to €35 million or 7% of global annual turnover for prohibited AI practices.

DORA (Digital Operational Resilience Act) is EU regulation requiring financial institutions to manage ICT risks. For AI systems, DORA requirements include:

• ICT risk management: Documented frameworks for identifying and managing ICT risks
• Audit trails: Complete logs for critical systems and decisions
• Resilience testing: Regular testing of operational resilience
• Third-party risk: Management of risks from ICT third-party providers

AI governance platforms like InnooForge provide the documentation and human oversight required for AI systems under DORA.

The EU AI Act imposes significant penalties for non-compliance:

• Prohibited AI practices: Up to €35 million or 7% of global annual turnover
• High-risk AI violations: Up to €15 million or 3% of global annual turnover
• Incorrect information: Up to €7.5 million or 1% of global annual turnover
• Additional sanctions: Market withdrawal, mandatory corrective actions, public naming

GDPR applies to AI systems that process personal data. Key requirements include:

• Lawful basis: Clear legal basis for processing personal data
• Data minimization: Collecting only necessary data
• Purpose limitation: Using data only for stated purposes
• Right to explanation: Data subjects can request explanation of automated decisions
• Data subject rights: Access, rectification, erasure, and portability
• Privacy by design: Data protection built into systems from the start

Penalties: Up to €20 million or 4% of global annual turnover for serious violations.

Human-in-the-loop (HITL) AI is a design pattern where AI outputs require explicit human approval before being acted upon. Key characteristics:

• Explicit approval: Consequential decisions require human sign-off
• Qualified reviewers: Humans with appropriate expertise validate outputs
• Audit trail: Complete record of who approved what and when
• Accountability: Clear responsibility for each decision

Use cases: Financial provisions, compliance determinations, customer communications, contract analysis.

Yes, AI governance can be added to existing AI systems through a governance layer. InnooForge integrates with:

• ChatGPT: OpenAI's chatbot with added governance
• Microsoft Copilot: Microsoft's AI assistant with traceability
• Internal LLMs: Custom models with governance wrapper
• Local AI: vLLM, Ollama, LM Studio with oversight

The governance layer captures interactions, records decisions, and manages human approval workflows without replacing existing AI investments.

AI governance is essential for any industry where AI decisions have significant consequences or regulatory oversight:

• Financial services: Banking, asset management, fund administration, insurance
• Legal services: Law firms, contract analysis, compliance reviews
• Healthcare: Diagnostic AI, treatment recommendations, patient data
• Government: Public sector decisions, citizen services
• Manufacturing: Safety-critical processes, quality control
• Any GDPR/Data subject: Organizations processing personal data

ChatGPT and Copilot are general-purpose AI assistants without built-in governance. InnooForge adds:

Yes. InnooForge supports multiple deployment options:

• On-premise: Full data sovereignty on your infrastructure
• EU cloud: Hosted through partners like Orange Business and Mistral AI
• Hybrid: Mix of on-premise and cloud based on data sensitivity

All deployment options maintain full audit trails and human oversight capabilities.