Traditional GRC programs rely on large static control libraries – essentially checklists of control statements (e.g. “SOX control: data backup procedure exists”). These libraries catalog existence of controls (often for audit purposes), but they rarely measure how well the control actually works. In other words, organizations typically mark a box “Control implemented” and move on. This checklist mentality is widespread because it meets basic compliance needs, but it has clear limits. Static controls quickly become obsolete as business processes, threats, and regulations change. Moreover, merely documenting a control’s existence does not prove it is effective or actually reduces risk. Modern GRC thought leaders and frameworks stress that controls must be continuously monitored and evaluated, not just listed. In short, static libraries don’t ensure control performance: they answer “Do we have a policy?” rather than “Did it work?”
Checklist-based controls (static): Defined by presence/existence. Often siloed by regulation (e.g. “SOX control X,” “PCI control Y”). Audit focuses on documentation or signature confirmations. Limitation: Doesn’t track actual control execution or outcomes.
Performance-based controls (dynamic): Defined by the outcome or risk mitigated. Controls are tied to metrics and continuously measured. Audit focuses on failure rates, test results, and trends over time. Advantage: Reveals effectiveness (or failure) in real time, enabling proactive remediation.
This table summarizes key differences:
| Aspect | Checklist (Static) Controls | Performance-Based (Dynamic) Controls |
|---|---|---|
| Definition | Static catalog of required controls (e.g. “policy exists”) | Controls defined by desired outcomes (e.g. “<5% exception rate”) |
| Approach | Focus on documentation and presence (point-in-time) | Focus on ongoing control effectiveness and monitoring |
| Monitoring | Periodic/annual testing or audits | Continuous monitoring, real-time data collection |
| Metrics | Existence checks (Yes/No) | Failure rates, control testing results, maturity scores |
| Effect | Passes compliance audits, but may miss hidden gaps | Enables proactive risk reduction; emerging issues catch sooner |
Real-World Control Failures
Recent incidents show the dangers of relying on checklists without performance monitoring. For example, the 2017 Equifax breach stemmed from an unpatched Apache Struts vulnerability. Equifax had a patch management policy (critical patches due in 48 hours) but failed to apply the patch on time. Investigators found that Equifax’s asset inventory was incomplete, so they didn’t even know a vulnerable system was in use. In practice, Equifax’s “control” (patching) was only on paper – static documentation existed but no one confirmed it worked, allowing hackers to breach 143 million records. Similarly, Wirecard’s colossal fraud in 2020 exposed complete breakdowns of control effectiveness. Reports note, “the first line of defense…failed was Wirecard’s own internal controls,” and that the controls “did not develop at the same speed as the growth of the company”. Executives and auditors trusted the static processes, but they were never tested or challenged, and billions in fake revenue went undetected.
In manufacturing, tragic safety incidents reflect the same issue. For instance, a Boeing 737 incident in early 2024 (a door plug flying off mid-flight) was traced to missing assembly steps with no documentation – “Boeing admitted there are no records” of reinstalling critical bolts. Culture even discouraged defect reporting (“staff were pressured not to document defects”. Here again, written procedures existed but were not verified or enforced. These failures across finance, healthcare, and manufacturing show that a checkbox (“we have a patch policy” or “we have a safety SOP”) provides false comfort if ongoing testing or monitoring is absent.
Modern Frameworks Emphasize Performance and Monitoring
Leading control frameworks now mandate metrics and continuous oversight. COSO’s 2013 Internal Control Framework – and its 2017 update to Enterprise Risk Management – move beyond mere control existence. COSO explicitly includes Monitoring Activities as a core component: organizations must perform “ongoing evaluations” of controls and use performance metrics to gauge control effectiveness. In practice, COSO requires not just that a control is defined, but that its operation is measured and reported. For example, COSO’s principle 16 requires “regular or ongoing evaluations” including supervisory reviews, transaction reviews, and performance metrics. Similarly, ISO/IEC 27001:2022 stresses continual improvement. Clause 10.1 of ISO 27001:2022 mandates that an ISMS “must remain dynamic” – continually monitoring performance, identifying opportunities, and implementing enhancements. In effect, ISO 27001 explicitly requires tracking KPIs and incident trends to ensure controls remain effective.
U.S. standards reinforce this shift. NIST SP 800-53 Rev. 5 (2020) redefines controls to be outcome-based, focusing on the result rather than a prescriptive procedure. Likewise, NIST SP 800-137 (Information Security Continuous Monitoring) provides guidance on implementing an automated, ongoing control monitoring program. It emphasizes “ongoing assurance of the effectiveness of deployed security controls,” enabling risk-based decisions. In short, the current best practices (COSO, NIST, ISO, etc.) all align: controls must be measured and monitored, not just listed. Continuous monitoring tools can provide real-time insights into control performance and monitoring activities include performance metrics to provide insight into effectiveness.
Control Performance Metrics and Risk Trends
In a performance-based model, key metrics quantify how well controls work. Common metrics include the control failure rate (e.g. percentage of tests where a control did not operate as intended), control maturity scores (often using a CMMI or similar model to rate process maturity), and testing frequency (how often each control is tested or reviewed). For instance, NIST’s performance measurement guidance (SP 800-55) advises that measures should be quantifiable (percentages, counts, averages) and should show performance trends over time. Trend analysis is critical: a rising failure rate or longer testing interval signals control degradation and elevates risk. As one analyst notes, “security controls inevitably degrade over time,” so organizations need strategies to detect when controls weaken.
How do trends impact risk posture? Imagine a bank where quarterly tests show an increasing number of access-control failures each quarter. This trend – “more blue dots” – means more pathways for fraud. In practical terms, if control failures increase by 5% per quarter, residual risk is growing in lockstep. Conversely, a high-maturity control (e.g. automation replacing a manual check) will have near-zero failures and a high score. In short, performance-based metrics translate into a view of enterprise risk posture: rising failure rates and low maturity signal higher residual risk, while stable or improving metrics mean risk is better controlled. As NIST 800-55 advises, organizations should track these metrics so they “demonstrate performance trends” and support decisions on resource allocation.
Empirical studies agree: tracking trends drives improvements. For example, continuous control monitoring (CCM) platforms have enabled organizations to cut audit preparation by ~60% by detecting issues earlier. In practice, risk teams use dashboards to trend controls: e.g. number of failed controls per month, average time to remediate issues, or changes in control maturity scores. If these metrics worsen (control failures spike, or a less-trained staff replaces a skilled one), risk managers see their risk heatmaps shift into the red. In summary, key control metrics and their trends are leading indicators of the company’s risk posture.
Modern GRC Platforms and Dynamic Controls
Next-generation GRC platforms are built to surface control performance data and automate dynamic monitoring. Unlike legacy tools, they integrate data across systems, apply analytics, and alert on anomalies in real time. For example, modern platforms allow linking key performance indicators (KPIs) and key risk indicators (KRIs) directly to controls and objectives. In practical terms, a GRC dashboard might graph control pass rates over time, or heatmaps of process deficiencies.
Crucial capabilities include automated alerting (e.g. a critical control failing triggers an email or issue ticket), historical trend analysis (visualizing control performance over months/years), and audit integration (linking control evidence to audit findings). One industry commentary emphasizes that effective platforms deliver “real-time alerts for any security anomalies” and pre-built integrations to automate evidence collection. Another notes that the future of GRC is “agile” and “integrated” – meaning continuous feedback loops and closed-loop remediation. In sum, the modern GRC stack is shifting toward live control monitoring: it aggregates logs, user reports, test results, and then analyzes them. When a control drifts or fails, the system updates its risk score or generates tasks automatically. This is a far cry from the old model of annually emailing compliance checklists.
Connected Risk®: Empowered’s Dynamic GRC Platform
Empowered’s Connected Risk® platform exemplifies the performance-driven approach. It is a unified, low-code GRC platform that consolidates all risk and control data into a single system of record. The platform is highly configurable: organizations can build custom workflows, dashboards, and data linkages without coding. Importantly, Connected Risk embeds real-time monitoring capabilities. For instance, it can track control objectives, KPIs and KRIs across the enterprise (even linking them to strategic goals), then display their performance visually. A recent analyst report highlights:
“Increased visibility” – With Connected Risk, organizations can “monitor the performance of objectives, key performance indicators, key risk indicators, and impact tolerances” across the enterprise.
The platform also automates audit and compliance evidence. It maintains full audit trails (“who did what and when”) and integrates with external data feeds, so controls are continuously validated and documented. For example, when a control failure is detected (say, a policy violation from log data), Connected Risk can automatically assign a remediation task, update the control’s status, and trigger alerts to stakeholders – all within its workflows. Clients report that this leads to dramatically faster assurance. One GRC20/20 report notes that Connected Risk customers cite strong ROI: they quickly consolidated GRC data (no more silos) and appreciated the platform’s flexibility to adapt without coding. In practice, a callout summary of Connected Risk’s key capabilities might read:
Connected Risk Capabilities:
- Real-Time Monitoring: Monitors controls, KPIs, and KRIs continuously, surfacing failures and trends instantly.
- Integrated Audit & Reporting: Full audit trails and analytics dashboards link controls to objectives, facilitating strategic assurance.
- Dynamic Workflows: Configurable, no-code workflows automate remediation tasks and alerts when control gaps emerge.
- Single Source of Truth: All risk, control, policy, and incident data live in one system, improving data quality and governance visibility.
By delivering these features, Connected Risk aligns with the very trends identified by COSO/NIST/ISO: continuous monitoring, metrics-driven assurance, and agility. It transforms each control from a static entry to a live metric in the enterprise’s risk dashboard.
Use Cases: Transitioning to Dynamic Controls
Financial Services: A large bank moved from spreadsheet-based SOX control checklists to a continuous monitoring system. By instrumenting controls (e.g. automated access reviews) and tracking their test results over time, the bank saw a ~60% reduction in quarterly audit prep effort. Exception alerts were delivered instantly to control owners (instead of discovered after audit), so issues were remediated before they became audit findings. The bank also implemented maturity scoring (using COBIT’s maturity model) for its key IT controls; by tracking maturity trends year-over-year, it could justify budget increases for underperforming areas.
Healthcare: A large health system implemented ISO 27001 and HIPAA compliance. Initially, policies and controls were documented in PDFs. Transitioning to a dynamic GRC, the organization embedded control testing into daily operations (for example, automated scans checked that encryption controls were on for patient systems). Management dashboards now show the percentage of compliant systems in real time. When an encryption control failure occurred, the system auto-assigned a ticket to IT, reducing response time from weeks to hours. Overall, the health system reported a 50% improvement in compliance efficiency and faster incident response, thanks to moving beyond static checklists.
Manufacturing: A global manufacturer integrated ERP and production data into a risk platform. Instead of just listing safety inspections, the system ingested sensor and audit log data to verify safety controls in action (e.g. an assembly station’s shutdown mechanism). If a control deviation (like a missed safety stop) was detected, Connected Risk alerted the plant manager immediately. As a result, the company could spot a pattern of degraded controls before accidents occurred. This predictive insight (control failure trends) became a key part of its risk management reviews with the board, showing tangible ROI in preventing costly downtime.
Across these cases, the shift from static to dynamic controls yields clear benefits: faster remediation, lower compliance costs, and stronger risk reduction. In financial and regulatory contexts (FedRAMP, SOX, HIPAA, NIST CSF, etc.), auditors now expect evidence of continuous monitoring. For example, one agency reported completing a FedRAMP High authorization in just 3 months using a CCM approach, whereas the traditional timeline was 18–36 months. Organizations that adopt performance-based controls often find they can reallocate staff from manual checklists to strategic risk analysis – a classic efficiency gain.
Final Thoughts
Karen Domingos, Product Manager for Compliance at Empowered helps us close out with: “Organizations can’t afford a one-size-fits-all approach to compliance. The magic happens when you create a strategic blend of static and performance-based controls. Static controls provide a foundational framework of consistency and predictability, while performance-based controls inject agility and real-time responsiveness. It’s like having a robust safety net that simultaneously adapts to the unique rhythms of your business—ensuring not just compliance, but intelligent risk management that evolves with your organizational ecosystem.”
Governance and compliance are moving beyond static libraries and checklists. As frameworks like COSO, NIST, and ISO stress, effective GRC requires metrics-driven, continuous control assurance. The examples of Equifax, Wirecard, and Boeing remind us that undiscovered control failures can have devastating impacts. Modern platforms (e.g. Connected Risk) make it practical to implement continuous monitoring: they provide real-time control dashboards, automated alerts, and analytics that turn raw data into insights. For financial services, healthcare, and manufacturing, embracing performance-based controls means not just checking the box, but actually managing risk dynamically. The ROI is clear: fewer surprises, more reliable compliance, and a resilient risk posture that evolves with the business.
Ready to move beyond checklists?
Empowered’s Connected Risk platform gives you real-time visibility into how your controls actually perform—not just whether they exist. Track failure trends, automate remediation, and link every control to enterprise impact.
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