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The Three Pillars of GRC
Governance
Governance — who decides, approves, and signs
Risk
Risk — identify, measure, and treat AI risks
Compliance
Compliance — laws, standards, and RFPs
Governance
Who decides. Which roles in the organization are accountable for AI? Who approves new AI use cases? Who signs vendor contracts? Who represents the organization before a regulator? Without clear role definitions, AI risks fall through the cracks between functions.
Risk
Which risks does the organization carry, how are they identified, measured, and treated? AI risks fall broadly into four categories:
- Technical: low accuracy, hallucinations, bias, instability across model versions
- Data: leakage through prompts, personal data used in training, loss of control over information that leaves the organization
- Regulatory: compliance with the EU AI Act, privacy regulations, and sector-specific standards
- Reputational: problematic outputs becoming public, algorithmic discrimination, legal liability
An AI risk you cannot see cannot be managed. A risk you cannot measure cannot be reported. Without mapping, even the best AI Council operates blind.
Compliance
Which laws, standards, and guidelines apply to the organization, and how do you verify adherence? Compliance is the outcome of good governance and risk management — not something that can be bolted on at the end.
AI GRC Programme Framework — Five Modules
- 1
Mapping
An inventory of AI systems, vendors, departments, and use cases. Without this, there is no foundation for anything else.
- 2
Policy & Standards
AI Use Policy, an approval pathway for new tools, and the organization's position on generative AI usage.
- 3
Operational Controls
Updated DPAs, human oversight mechanisms, activity logging, and an incident reporting process.
- 4
Training
Core modules for staff — what not to upload, how to recognize hallucinations, and who to report a problem to.
- 5
Monitoring & Improvement
KPIs, annual internal audit, policy refresh. A living programme that evolves with your AI footprint.
How the Portal Helps Each Module
- Mapping: a structured questionnaire completed in 15-30 minutes, with automatic output to AI tool, automation, and vendor registers.
- Policy: automatic gap detection against the existing policy and improvement suggestions based on questionnaire responses.
- Controls: flagging systems that require human oversight, DPAs due for renewal, and incidents that should be reported.
- Training: data on which departments carry higher exposure — the basis for targeting training where it matters most.
- Monitoring: an AI insights dashboard that updates on every questionnaire save, running an automatic findings-and-recommendations pipeline.
Starting Without a Large Budget
If there is no budget for an external consultant, you can get started independently with a short sequence:
- Week 1: AI asset register — go through each department and record what is in use: tool name, vendor, data types processed. The portal manages this for you.
- Weeks 2-3: write a one-page basic policy — what is permitted, what is prohibited, and who to notify. Route it for management approval.
- Week 4: update contracts with your top 3-5 AI vendors to include appropriate data processing terms.
- After month 1: complete those three steps, use the portal to assess remaining gaps, and prioritize your next 90 days. For a deeper programme track, see ISO/IEC 42001 and the AI risk assessment guide.
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content.layout.faqCountWhat is the difference between classic GRC and AI GRC?
Classic GRC focuses on data, processes, and infrastructure. AI GRC adds a layer of risks unique to artificial intelligence: algorithmic bias, lack of explainability (black-box decisions), hallucinations in large language models, managing AI vendors whose terms change frequently, and personal data exposure through prompts. Organizations already running ISO 27001 or ISO 9001 know the governance structure — they need to add the AI-specific controls on top.
Who in the organization owns AI GRC?
No single role covers the entire domain. In practice: the CISO or CTO owns technical risk, legal counsel owns regulatory compliance, department managers own day-to-day practice, and the DPO owns privacy dimensions. More mature organizations establish an 'AI Council' that centralizes decisions. In a mid-sized organization this is typically 3-5 people who meet monthly.
What are the first critical controls to put in place?
Four controls every organization should start with: (1) AI asset register — a single inventory of every AI system in use. (2) Use policy — what may be uploaded to an external AI tool and what is prohibited. (3) Updated DPAs with key AI vendors. (4) An oversight process for systems that make decisions with personal impact.
How much does this cost?
It depends on size and maturity. An organization of 50-200 employees can complete an initial mapping and policy build in 3-4 months. Consultant cost: roughly $8,000-$22,000. Our portal compresses the mapping phase from weeks to minutes, so the consultant arrives more focused and total project cost typically drops 30-50%.
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