Part 2 in the GRC Orchestrate Series
In the 2025 State of the GRC Market: Hitchhiker’s Guide to the GRC Galaxy, we’ll explore how these ideas are transforming both vendor landscapes and enterprise architectures.
In last week’s article, we introduced the concept of GRC 7.0 – GRC Orchestrate, a revolutionary-evolution of Governance, Risk Management, and Compliance. This next-generation approach envisions GRC as a dynamic and intelligent capability—one that continuously aligns business objectives, operational performance, obligations, and uncertainty across the enterprise. We explored how Agentic AI and digital twins transform traditional GRC into a living, learning system.
In this second installment, we dive deeper into one of the most transformative pillars of GRC Orchestrate: the Digital Twin.
GRC Orchestrate Is the Future: But the Journey Is Just Beginning
GRC 7.0 is a forward-looking framework that is beginning to materialize through early use cases and foundational technologies. While some organizations — particularly in Europe — are already piloting orchestration capabilities in strategy, compliance, and risk alignment, widespread adoption across the global market is will grow until 2030 when it becomes fully mature. Much of North America, for instance, is behind and still climbing toward GRC 6.0: Business Integrated GRC, focusing on embedding GRC into the business and linking it to strategic performance.
Before GRC can orchestrate, it must first integrate. That means aligning objectives with obligations, risk with decision-making, and policies with operations. But once these foundations are in place, organizations can evolve toward orchestration: where GRC capabilities dynamically interact, learn, adapt, and simulate. A core and critical piece of this next step lies the digital twin, which provides the structure, foresight, and simulation power to bring orchestration to life.
Digital Twins in GRC: Seeing Around Corners, Navigating Possibility
In the Marvel Cinematic Universe, Dr. Strange embodies the role of the ultimate Chief Risk Officer. In Avengers: Endgame, he explores over 14 million possible futures, seeking the one path to success. This fictional moment captures what digital twins offer in the real world of GRC: the ability to model complex futures, simulate countless outcomes, and make informed, strategic choices before events unfold.
A digital twin is a virtual, evolving software model that mirrors the enterprise: its structure, operations, risks, controls, policies, obligations, and external dependencies. But it’s not a static mirror; it is context-aware and predictive. It continuously ingests real-time data, refines its assumptions, and runs simulations to project what might happen next.
With GRC 7.0, digital twins become the engine of strategic foresight, allowing organizations not only to track their current GRC posture but to plan for disruptions, regulatory changes, market shifts, and strategic bets. Rather than treating risk and compliance as constraints, digital twins empower organizations to use GRC as a forward-looking capability that unlocks resilience, agility, and opportunity.
Building a GRC Digital Twin: The Eight Structural Pillars
To construct a functional GRC digital twin, organizations must think beyond traditional risk registers and compliance checklists. They must bring together data, logic, and governance layers to form a dynamic representation of the enterprise. Here are the eight structural components and GRC-related use cases with digital twins:
- Processes and Business Services. Every digital twin, in a GRC context, begins by mapping how the organization actually operates. Business processes — from procurement to HR, from order-to-cash to incident response — are digitally modeled. These aren’t just static diagrams but dynamic simulations tied to workflows, dependencies, and performance data. When a disruption occurs — a regulatory change, a cyberattack, a supply chain interruption — the twin can simulate cascading impacts across services and geographies, allowing for stress testing and rapid reconfiguration of business logic.
- Risks and Controls. Risk is modeled as a living variable tied to objectives. It is not just about capturing threats but about understanding how they evolve and interact. Each control is also represented as a live mechanism; complete with effectiveness ratings, failure scenarios, and response protocols. Together, they form the reasoning core of the twin: simulating what happens when risks escalate, controls degrade, or new threats emerge. Executives can model trade-offs and prioritize mitigation based on real-time risk-adjusted views of performance.
- Events, Issues, and Audits. A robust digital twin learns from the past. Historical issue logs, audit findings, and incident reports are not archived, they become behavioral patterns. These patterns inform the twin’s predictive capacity: highlighting weak signals before incidents recur, modeling root cause propagation, and identifying systemic control vulnerabilities. Over time, the digital twin becomes a risk historian and a resilience strategist.
- Policies and Regulations. Policies are no longer just documents, they are structured data elements that include links to obligations, regulatory jurisdictions, control mappings, and enforcement logic. When new regulations are proposed or passed, the digital twin models the policy impact across the organization: which documents require revision, which functions must attest, which controls must be reoriented. This capability enables anticipatory compliance, getting ahead of regulatory shifts instead of reacting late.
- Real-Time Telemetry. The digital twin is fed continuously by telemetry from internal and external systems: cybersecurity alerts, ESG performance sensors, supply chain data, finance systems, and more. This stream of data provides the situational awareness needed to adjust simulations dynamically. When a vendor’s ESG score drops or a new threat pattern is detected, the twin instantly recalibrates exposure and updates its recommendations, closing the gap between sensing and decision-making.
- Strategic Planning & Scenario Analysis. Perhaps the most powerful use case for digital twins is strategic scenario simulation. Leaders can explore “what-if” questions in real time: What if we divest a business unit? Enter a new market? Reallocate compliance resources? The twin simulates outcomes across risk, cost, compliance, and performance. It acts as a virtual war room, a sandbox for executive decision-making that reduces uncertainty and enhances agility.
- Extended Enterprise. Third parties are modeled not just as data points but as interconnected nodes in the operational fabric. The digital twin captures performance metrics, compliance status, obligations, and exposure for each vendor, partner, or supplier. It enables the simulation of third-party failure or disruption, helping organizations prepare for—and prevent—cascading risk. GRC no longer ends at brick-and-mortar walls and traditional employees; it extends across the value chain.
- Regulatory Change Modeling. By combining horizon scanning with large language models and machine-readable regulatory updates, the twin can model the likely impact of future rules. This enables organizations to simulate different legal landscapes, estimate compliance costs, and adjust investment decisions accordingly. The twin transforms compliance from reaction to foresight—from an audit trail to a strategic compass.
From Digital Mirror to Digital Conductor
A mature GRC digital twin doesn’t just reflect reality: it guides it. It evolves from a digital mirror into a digital conductor, orchestrating the flow of data, decisions, and adjustments across governance, risk, and compliance domains.
Imagine asking a natural language interface:
- “How would ESG reporting requirements in Southeast Asia impact our current vendors?”
- “What’s the control confidence across our top 10 revenue-generating processes if we cut IT compliance spend by 15%?”
- “Which regulatory regimes are converging in our product roadmap jurisdictions, and what’s the associated risk delta?”
- “If China invades Taiwan, how does this impact our supply chain and ability to deliver products/services and maintain operations?”
- “What are the top resilience issues in our digital supply chain with dependencies on critical services?”
The digital twin answers not with reports, but with simulations, visualizations, and prescriptive actions — each grounded in data, logic, and context. This is the future of GRC: contextualized, autonomous, and orchestrated.
Why It Matters: Building Tomorrow on Today’s Foundation
The effectiveness of a digital twin tomorrow depends entirely on the integrity of the data and governance structures built today. Organizations cannot orchestrate what they cannot understand. Siloed risk functions, unstructured policies, and outdated control frameworks will hinder simulation and automation.
To prepare, organizations must:
- Define and maintain a shared GRC ontology.
- Integrate policy, process, risk, and control data.
- Tag obligations and controls with metadata.
- Normalize risk assessment and treatment workflows.
These are not just investments in compliance or audit readiness, they are prerequisites for future-readiness.
Conclusion: Orchestrating the Future
Digital twins are not dashboards. They are strategic instruments of foresight. They empower GRC to shift from accountability to adaptability, from control to intelligence. In a world where uncertainty is constant and integrity is non-negotiable, digital twins help organizations chart a path forward: one that is intentional, informed, and integrated.
GRC 7.0 isn’t about the tools we buy, it is about the architectures we build and the intelligence we embed. As we continue this journey, stay tuned for Part 3 in the GRC Orchestrate series: an in-depth look at Agentic AI — the autonomous force behind the orchestration.
GRC 7.0 isn’t a destination. It’s the command framework for the next generation of decision-making.
In the 2025 State of the GRC Market: Hitchhiker’s Guide to the GRC Galaxy, we’ll explore how these ideas are transforming both vendor landscapes and enterprise architectures.