Drel vs. GRC platforms for AI governance
GRC platforms manage enterprise-wide compliance programs — control libraries, risk registers, audit workflows, policy management. Drel produces the AI-specific clearance record your AI Committee signs before a system reaches production. The scope is different, the artifact is different, and the buyer is different.
Enterprise GRC platforms are built for breadth: they manage controls across ISO 27001, SOC 2, GDPR, and dozens of other frameworks simultaneously, across every system in the organisation. They are the right tool for that problem.
The AI Committee's problem is different. It is not “how do we manage compliance across the enterprise?” It is “can this specific AI system reach production, and what is the defensible record that we reviewed it before it did?” That requires a different artifact — one shaped around the AI system's architecture, its threat model, its five-state clearance decision, and its re-assessment triggers.
GRC platforms are built for compliance breadth across the enterprise. Drel is built for AI governance depth on a single system. The AI Committee needs both — the GRC platform for the programme, Drel for the per-system clearance record.
What GRC platforms do well
- Manage control libraries across multiple frameworks simultaneously — ISO 27001, SOC 2, GDPR, NIST CSF.
- Track control ownership, evidence collection, and audit workflows at enterprise scale.
- Manage risk registers across the full organisation, not just AI systems.
- Automate evidence collection from integrated tools (cloud providers, HR systems, ticketing).
- Produce audit-ready reports for external auditors and certification bodies.
- Manage policy lifecycle — creation, approval, distribution, attestation.
The buyer is typically the GRC team, compliance function, or CISO office managing the enterprise compliance programme. The artifact is a compliance posture dashboard and audit package.
Where GRC platforms fall short for AI governance
- The unit of analysis is the control, not the AI system. GRC platforms reason about controls and their evidence. AI governance requires reasoning about a specific system — its architecture, its threat model, its agentic graph, its tool surface. The system is the unit, not the control.
- No five-state clearance model. AI governance requires a disposition: proceed, conditional, restricted pilot only, hold, or decline. GRC platforms track control status (implemented / not implemented / partial). Those are different questions.
- No AI-specific threat taxonomy. OWASP Agentic Top 10, OWASP LLM Top 10, MITRE ATLAS, MAESTRO — the frameworks that matter for AI system review are not first-class in most GRC platforms. They are added as custom frameworks, if at all.
- No re-assessment trigger model. AI systems change in ways that require re-review: model version change, tool added, autonomy increased, vendor changed. GRC platforms track control status; they do not encode the events that should fire a re-review of a specific system.
- The AI Committee artifact is missing. The AI Committee needs a Risk Disposition memo — decision, rationale, required controls by lifecycle gate, residual risk acceptance, evidence gaps, re-assessment triggers, sign-off log. GRC platforms produce compliance reports; they do not produce this artifact.
Side by side
| Capability | Drel | GRC platform |
|---|---|---|
Primary unit of analysis | A specific AI system — its architecture, threat model, and clearance decision. | Controls and their evidence, across the enterprise. |
Clearance model | Five states: proceed, conditional, restricted pilot only, hold, decline. | Control status: implemented / not implemented / partial. |
AI-specific threat taxonomy | OWASP Agentic Top 10, OWASP LLM Top 10, MITRE ATLAS, MAESTRO — first-class. | Custom framework import, if supported. Not first-class. |
Re-assessment triggers | Typed events (model change, tool added, autonomy increase, vendor change) encoded per system. | Periodic review cadences. Not system-specific trigger events. |
AI Committee artifact | Risk Disposition memo with decision, rationale, controls by lifecycle gate, residual risk acceptance, sign-off log. | Compliance report. Not a per-system clearance decision record. |
Framework mapping | ISO/IEC 42001, EU AI Act Art. 9, NIST AI RMF, OWASP Agentic Top 10. | ISO 27001, SOC 2, GDPR, NIST CSF — broad enterprise frameworks. |
Scope | AI systems specifically. Not a general-purpose compliance tool. | Enterprise-wide. All systems, all frameworks. |
Coexists with GRC platforms | Yes — Drel produces the per-system AI clearance record; the GRC platform manages the enterprise programme. | — |
How the two fit together
The pattern that works: the GRC platform manages the enterprise compliance programme — ISO 27001 controls, SOC 2 evidence, GDPR records. Drel sits next to it, owned by AI Governance or Security Architecture, and produces the per-system clearance record for each AI system the organisation deploys.
The Drel disposition feeds the GRC platform: the clearance record for an AI system is the evidence artefact that closes the AI-related controls in the enterprise GRC programme. The GRC platform does not need to understand the five-state clearance model; it just needs the signed disposition as an evidence attachment.
See the AI Committee artifact Drel produces
The Risk Disposition Drel generates is the per-system clearance record your AI Committee signs. Read a sample, then decide whether your current GRC programme produces the same artifact for each AI system you deploy.
A note on scope: Drel reviews assessed systems against documented architecture, configuration and intent. It does not ingest live telemetry from production environments. Dispositions reflect the assessed system at the time of review and the re-assessment triggers that govern when the disposition must be revisited.