GRC Software for Security Leaders: Executable GRC Guide

GRC software (governance, risk and compliance software) gives security leaders a structured platform for managing compliance frameworks, risk registers, third-party assessments, internal audit and policy in one place. Executable GRC is the newest evolution of that category: AI Agents with defined Personas and Skills perform compliance activities autonomously rather than simply recording them, so a smaller team can run a programme that used to need significantly more people. This guide explains what GRC software does, what separates legacy platforms from modern ones, what makes a GRC programme executable, and how to evaluate a platform today. 

Security leaders carry more compliance obligations today than at any earlier point in GRC’s history as a distinct discipline. ISO 27001:2022, SOC 2, NIST CSF 2.0, DORA (Regulation (EU) 2022/2554), in force since January 2025, plus NIS2 and the forthcoming UK Cyber Security and Resilience Bill, have all expanded the framework landscape over the past three years.

Board-level accountability has followed the same curve. Provision 29 of the UK Corporate Governance Code (2024) requires the boards of UK premium-listed companies to declare the effectiveness of their material internal controls, a requirement that applies to accounting periods beginning on or after 1 January 2026. DORA introduces personal accountability for ICT risk at management-body level, and the Senior Managers and Certification Regime (SM&CR) already ties individual accountability to control failures across UK-regulated financial services.

The compliance obligation hasn’t changed in kind. It has expanded in scope, in enforceability and in board visibility.

Staffing has fallen behind the obligation. ISACA’s State of Cybersecurity 2025 survey found that 55% of cyber security teams describe themselves as understaffed and 65% carry unfilled positions, with mid-sized organisations reporting the worst shortages. GRC expertise specifically is one of the top skills security teams say they lack, according to ISC2’s 2025 Cybersecurity Workforce Study. The result is a structural execution gap: more frameworks to certify, more evidence to collect, more third parties to assess, and roughly the same number of people to do it.

Technology investment in GRC has grown alongside the obligation. The IDC MarketScape: Worldwide Governance, Risk, and Compliance Software Vendor Assessment, 2025 (IDC #US53615325) covers the competitive landscape in detail, yet legacy platform adoption on its own tends to widen the gap between storing compliance work and actually performing it. Most of that spend still buys a better place to store the work.

The average GRC programme now covers five or more compliance frameworks, runs on a team that hasn’t grown in three years, and still leans on spreadsheets, email chains and quarterly reporting cycles built for a world with fewer obligations and fewer people who need answers.

Most security leaders already know exactly what their programme should look like. The constraint is execution capacity: the assessments, the evidence, the questionnaires, the control testing, the board reports. All of it takes time and people that most teams don’t have spare.

The IDC Market Note frames this as more than an incremental change: itdescribes SureCloud’s Gracie AI as “the industry’s first cross-domain agentic platform”, and noting that SureCloud is "one of the few in its peer set" capable of credibly serving heavily regulated verticals such as financial services, healthcare and critical infrastructure at enterprise scale.¹ Gartner’s Hype Cycle for Cyber-Risk Management, 2025 covers this same ground from the analyst side, tracking how risk quantification and cyber GRC tooling are maturing across the market. 

GRC software (governance, risk and compliance software) consolidates the work of managing compliance frameworks, risk registers, third-party assessments, internal audits and policy management into one platform. It replaces the disconnected spreadsheets and email chains most teams start with, giving security and compliance functions a structured workflow, evidence trail and reporting layer across the whole programme.

The Core Domains: Compliance, Risk, Third-Party, Audit, Policy

A GRC platform generally manages five interconnected domains. Compliance management handles multi-framework certification and control mapping across ISO 27001:2022, SOC 2, NIST CSF 2.0, DORA, NIS2 and whatever else a team is obligated to maintain. Risk management provides a structured register, treatment workflows and risk quantification that translates operational exposure into terms the board can act on.

Third-party risk management extends that visibility to vendors and suppliers, running assessment programmes at scale and surfacing concentration risks that manual tracking misses. Internal audit handles finding tracking, remediation workflows and audit preparation, while policy management keeps the document library current and links policies to the controls they govern.

These domains don’t operate in isolation. A control mapped to ISO 27001:2022 is often also relevant to DORA, SOC 2 or NIST CSF 2.0.

A platform that holds all five domains in a shared data model means control mapping happens once, with evidence reused across frameworks. For teams maintaining three or more frameworks at once, that cross-domain integration is where the real time savings show up.

Why Legacy GRC Stops at Reporting

The problem with most GRC software is that it records what has already happened rather than helping teams act on what needs to happen next. Legacy platforms, often built on case management or document management foundations, are good at storing policies, holding risk registers and generating reports. They aren’t built to distribute work, chase evidence, monitor controls continuously or surface the next priority for a stretched compliance team.

The result is a familiar pattern: evidence collection chased manually over email, control testing that happens periodically instead of continuously, board reports that take days to compile from scattered spreadsheets, and a team that manually extends its own processes every time a new framework is added instead of the platform absorbing the obligation.

IDC puts it plainly: the challenge for most GRC teams is “not a shortage of expertise, but a chronic shortage of execution capacity” (IDC Market Note, June 2026). The expertise is there. The platform is the bottleneck.

Executable GRC is an approach to governance, risk and compliance where the platform carries out the activity itself: AI Agents with defined Personas and Skills collect evidence, map controls, run third-party assessments and prepare board reports, letting a small team run a programme that would otherwise need significantly more resource.

Executable GRC Defined

Executable GRC marks a shift in what a GRC platform is for. In the legacy model, the platform is a system of record. It holds policies, stores evidence, generates reports, and the team does the actual work while the platform stores the output.

In an executable GRC model, the platform is a system of execution. It performs the activity: collecting evidence, completing control assessments, dispatching third-party questionnaires, flagging exceptions, escalating overdue items, compiling the board report. The team sets priorities and reviews outputs; the platform carries out the operational work in between.

The IDC Market Note defines what this looks like in practice:

This is a different architecture, built from the outset to perform GRC work rather than store it.

A Virtual GRC Team: Personas, Agents, Skills

Gracie AI operates as a virtual GRC team. Three elements define how it works.

AI Agents are autonomous workers that perform specific GRC activities: collecting evidence for a control test, sending a third-party questionnaire, preparing an audit-ready summary. They don’t wait to be prompted for each step.

AI Personas give each Agent a defined function and domain expertise. A Compliance Manager Persona operates across framework obligations, a Risk Analyst Persona handles risk register work and quantification, and a Third-Party Risk Persona manages vendor assessment at scale. Each Persona brings the context of its function to the tasks it performs.

AI Skills are the modular capabilities Agents deploy: control mapping, evidence collection, gap analysis, report generation, regulatory change monitoring. Skills are coordinated across Personas through a Senior Agent Collaboration model that directs activity across domains.

The result is a programme that runs between human decisions instead of waiting for them. Compliance activity happens continuously, exceptions reach the right person, and the board report reflects the current state of the programme rather than last quarter’s.

A modern GRC programme operates on two levels. The strategic layer gives the board and senior leadership risk quantification, investment justification and programme governance. The operational layer runs compliance activity day to day: evidence collection, control testing, third-party assessment, framework management and audit preparation. A programme only functions well when both layers are served.

The Strategic Layer: Board, Budget, Risk Quantification

Board-level GRC has one purpose: turning operational compliance work into decisions. The board needs to know where the material risks sit, which controls are working and what investment closes the gaps. Most compliance programmes struggle at this layer not because the information is missing, but because it takes too long to compile and arrives in a form that needs translating.

A GRC platform that automates quantification, pulling from the risk register, control test results and incident data to produce a board-ready view, removes the quarterly rebuild from the team’s workload. Risk quantification connects directly to investment decisions: when a programme can show the board what residual risk costs against what a control investment costs, the budget conversation changes.

The Operational Layer: Compliance, Frameworks, Third-Party, Evidence

The operational layer is where most GRC team time goes: maintaining framework compliance, running third-party assessments, collecting control evidence, managing policy reviews, preparing for audits. For a team maintaining multiple frameworks, the challenge is finding the capacity to act on all of it, on a recurring basis.

Executable GRC addresses the capacity problem directly. When AI Agents perform evidence collection, send third-party questionnaires, monitor controls continuously and flag exceptions without manual intervention, the team’s time shifts from admin to judgement. The work gets done; people focus on the decisions that actually need human context.

The shift from periodic to continuous matters. In a legacy model, a compliance programme runs in cycles: assess, remediate, certify, repeat. In an executable model, monitoring runs continuously, exceptions surface in real time, and the programme adapts to new obligations without a manual restart. 

When evaluating a GRC platform, security leaders should weigh four criteria: whether compliance, risk, third-party and audit data sit in one shared model so controls can be mapped once and applied across frameworks; whether the platform is fully event-sourced and auditable; whether its AI features are governed, explainable and accountable; and whether it scales operational GRC activity without requiring more headcount.

Four Criteria: Cross-Domain, Auditable, Governed AI, Scalable

Four criteria separate mature GRC platforms from legacy alternatives.

1. Event-sourced and fully auditable. Every action the platform takes, every control test result, every evidence record, every Agent activity, needs to trace back to a specific event with a timestamp and a responsible party. An auditor reviewing a certification, or a board reviewing a Provision 29 declaration, should be able to follow every finding back to its source. Platforms that summarise or overwrite data don’t meet this standard.
2. Governed, explainable AI. For AI features to work in regulated environments, the platform must explain what each Agent did and why. A compliance team can’t sign off on AI-generated output that arrives without an audit trail. Explainability is a regulatory expectation for FCA-regulated firms, DORA in-scope entities and organisations subject to the EU AI Act, so an AI-first GRC platform needs to treat auditability as a design decision made from day one.
3. Cross-domain integration. A platform that holds compliance, risk, third-party risk, audit and policy in a shared data model lets controls be mapped once and applied across frameworks. One that keeps these domains in separate modules, or needs manual re-entry between them, recreates the duplication executable GRC is meant to eliminate.
4. Scalable execution. The real test of an executable GRC platform is whether it reduces the headcount needed to run the programme, or whether its AI features are bolt-on additions that still need manual operation. A genuine execution model performs recurring operational tasks (evidence collection, questionnaire dispatch, control monitoring, report compilation) without a human triggering each step.

For a walkthrough of applying these criteria at enterprise scale, alongside a weighted scoring model for RFPs, see SureCloud’s enterprise GRC platform evaluation guide.

Build vs Spreadsheets vs Platform

Three routes exist for managing GRC: build a custom system, run the programme in spreadsheets, or use a purpose-built GRC platform.

Spreadsheets can handle the compliance work of a single framework for a small team. They don’t scale to multiple frameworks, don’t provide an auditable evidence trail, don’t distribute evidence collection to control owners systematically, and don’t give the board the reporting layer modern governance needs. They’re common because they’re cheap to start and expensive to leave behind.

Building a custom GRC system is something large enterprises have tried. The typical outcome is a project that takes years, costs several times more than a platform alternative, and produces something that can’t keep pace with regulatory change without further bespoke development.

A purpose-built GRC platform absorbs regulatory change, scales across frameworks and delivers the execution model that the other two routes can’t match.

The business case for executable GRC rests on two things: the productivity gain from AI-performed operational tasks, and the risk reduction from continuous rather than periodic compliance monitoring.

On productivity, IDC’s analysis of Gracie AI attributes a 70 to 80 per cent productivity uplift across record management, evidence chasing and assessment analysis (IDC Market Note, June 2026), which shifts senior professionals’ time away from administrative throughput and toward higher-value judgement calls. SureCloud’s own published product data points to a similar pattern elsewhere in the platform:

1. Management report preparation cut from two weeks to two days, according to SureCloud’s published CISO product data.
2. 75% faster time to insight, across the full GRC estate, per the same source.
3. 40% faster decision-making at leadership level, with unified, real-time risk data replacing the wait for a quarterly rebuild.
4. A 70–80% reduction in manual GRC workload, which is the SureCloud-published figure closest to IDC’s independently derived 70–80% uplift.

These aren’t incremental improvements. They’re the difference between a compliance programme that runs reactively, always behind, always compressing timelines before a certification audit, and one that runs continuously and stays audit-ready.

On risk reduction, continuous control monitoring means exceptions surface when they happen rather than at the next quarterly review. Third-party risk that deteriorates between annual assessments gets caught in real time. Board reporting reflects the programme’s current, live state.

The IDC Market Note evaluated SureCloud's platform this year and we believe the conclusion is clear: the architecture represents a category-level shift in how GRC platforms are built. The IDC Market Note calls Gracie AI “the industry's first cross-domain agentic GRC platform”, a verdict that we believe puts a name to what security leaders are already seeing in their own programmes.

SureCloud has worked with organisations across regulated financial services, legal, critical infrastructure and enterprise sectors since 2006. The Gracie AI execution model is built on two decades of GRC programme expertise, encoded into the platform rather than layered on top of a generic AI toolset.