How to Run Your Entire GRC Function With AI — Without Hiring More People
Pelaris5 min read·Just now--
Risk registers, control testing, compliance monitoring, board reports. All of it. Here is the system.
8 min read · GRC & Compliance · Pelaris
The average GRC professional spends 75–85% of their time on work that AI can substantially reduce: drafting policies, writing risk register entries, producing committee reports, monitoring regulatory developments, documenting control testing results.
The 10–15% that remains — risk judgment, regulatory positions, control failure escalations, board-level risk opinions — is where all the actual value sits. And it is the part that almost no GRC professional has enough time to do properly.
The AI-augmented GRC function is not a smaller team doing the same work. It is the same team doing better work — with the production tasks removed and the judgment tasks expanded.
Here is the system.
The Fundamental Shift
Before getting into the specific workflows, one principle needs to be established: AI does not replace GRC judgment. It eliminates the administrative labour that prevents GRC professionals from exercising judgment.
The risk acceptance decision, the regulatory breach determination, the control failure escalation — these belong to you. AI produces the context and the structure. You make the call.
With that established, here is where AI creates the most value in GRC:
1. Risk Assessment Automation
A comprehensive risk assessment for a mid-sized organisation typically takes a GRC team 4–8 weeks. With AI, the same assessment takes 1–2 weeks — because the production work (risk identification, description drafting, control mapping) is compressed from days to hours.
The workflow has eight steps:
• Paste your business model description and regulatory context into Claude. Ask it to generate a comprehensive risk universe organised by category (strategic, operational, regulatory, financial, technology).
• For each identified risk, use Claude to draft formal descriptions in cause-event-consequence format.
• Paste each risk description with your rating scale and ask Claude to propose inherent risk ratings with reasoning. Review and calibrate each rating.
• For each rated risk, ask Claude to identify the key controls that should exist, organised by control type.
• Compare Claude’s expected control list against your actual controls. Ask Claude to identify gaps and rate their significance.
• Ask Claude to propose residual risk ratings reflecting your actual control environment. Apply your judgment to each.
• Ask Claude to compile all outputs into formatted risk register entries.
• Ask Claude to produce the risk report narrative from the completed register, calibrated for your specific audience.
THE RISK UNIVERSE PROMPT — start every risk assessment here
Using my organisation context: [paste your context — type, sector, regulatory framework, key products and services, geographies], generate a comprehensive risk universe. Organise risks into: (1) Strategic, (2) Operational, (3) Financial, (4) Regulatory and compliance, (5) Technology and cyber, (6) Reputational, (7) Emerging risks. For each category provide 6–8 specific, relevant risks. Avoid generic risks — make each one specific to my organisation type and operating environment.
2. Control Testing Efficiency
Control testing is the most documentation-heavy GRC activity. Test programmes, workpapers, exception documentation, management actions — all of it consumes time that could be spent on actual testing and analysis.
AI compresses the documentation without compromising the professional standard:
• Test programme design: give Claude the control objective, description, and risk being mitigated. It produces a complete test programme with specific procedures, sample sizes, and evidence requirements.
• Workpaper shells: Claude produces complete workpaper structures for each control being tested — ready for evidence attachment and conclusion completion.
• Exception documentation: when exceptions are identified, describe what you found and ask Claude to document it in 5C format (Condition, Criteria, Cause, Consequence, Corrective Action).
• Control opinion: after completing testing, paste all results and ask Claude to draft an overall control effectiveness opinion for audit committee submission.
3. Compliance Monitoring on Autopilot
Regulatory monitoring is the GRC activity most transformed by AI. The volume of regulatory output from EU institutions alone makes manual monitoring unreliable for any team without significant resource.
The system: set up free RSS feeds from your key regulatory sources (EBA, ECB, ESMA, EDPB, FATF, your national regulator). Review them weekly — 15 minutes. Flag relevant items. Paste the flagged items into Claude with your organisation context and ask it to assess relevance, urgency, and whether action is required.
What used to take a compliance analyst half a day every week takes 45 minutes with this system. And the coverage is more comprehensive than manual monitoring.
THE REGULATORY DIGEST PROMPT — run weekly
Assess the following regulatory developments for relevance to [organisation type] regulated under [framework]: [paste flagged items from your RSS review]. For each item: (1) is it relevant to us — yes/no with one-sentence explanation, (2) if yes — what specifically needs to change (policy, process, system, or training), (3) urgency — immediate action required / plan within 90 days / monitor only, (4) regulatory submission required — yes/no. Produce a prioritised action list for the compliance team.
4. Board and Committee Reporting
GRC reporting is where the most senior professionals spend the most time on the lowest-value activity: formatting, drafting, and compiling information that AI can produce in minutes.
The workflow: compile your raw data (risk register changes, control testing results, incidents, regulatory developments, management action updates). Paste it into Claude with your report template and audience specification. Claude produces the full report narrative in 3–5 minutes. You review for accuracy, add your professional assessment, and submit.
What used to take 90 minutes of senior GRC time takes 20 minutes. The output quality is consistent — no more blank-page paralysis on a Friday afternoon.
What This Means for GRC Teams
The GRC function that adopts this system does not need to grow headcount to handle increasing regulatory complexity. It handles more — because AI absorbs the production work that would otherwise require additional resource.
More importantly, it handles it better. Consistent documentation. Comprehensive monitoring. Board reports that are produced on time and to a consistent standard. These are the outputs that build regulatory confidence and board trust.
The GRC professional in this model spends their time on what AI cannot do: the judgment calls, the regulatory relationships, the risk escalations, the board engagement. That is where the value is. That is where you should be spending your time.
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