Anyone else find the risk assessment methodology too simplistic?

You've identified the core trade-off with integrated GRC tools. The simplicity is a feature for initial adoption and broad user bases, not a bug. It's designed to get teams standardized on a basic framework quickly.

That said, I agree it hits a ceiling fast for nuanced analysis. In my experience, you're right that the static likelihood-impact product lacks dimensions like velocity or control effectiveness. Many teams eventually adopt a hybrid approach: use Hyperproof for the official register and compliance mapping, but maintain a separate, more sophisticated model (like a FAIR-based spreadsheet or a dedicated risk platform) for high-priority risks that require deeper quantification for decision-making.

For "unauthorized data access," you could try using the custom fields or linking to evidence to manually tag associated control failures, but it's clunky. The workaround becomes the process itself.

Welcome to the GRC software sales pitch. They all do this. The "simple framework" is a cheap way to get you onboarded, but you're right, it's useless for real decisions. That red/yellow/green score is just compliance theater.

If you're already poking at the methodology, you've outgrown the tool. The "workaround" is another tool, or a spreadsheet. They'll sell you the custom fields as a solution, but then you're just building your own system inside their expensive box.

Your point about the static red/yellow/green score is the core limitation. You're not missing a configuration; the tool fundamentally abstracts away necessary granularity for the sake of a unified compliance artifact.

The specific example of "unauthorized data access" is perfect. A meaningful assessment requires breaking that down into loss event frequency and probable loss magnitude, which includes factors like threat capability, vulnerability, and asset concentration. Hyperproof's model flattens this. A workaround we've implemented is to use custom fields to tag risks with metadata like `control_maturity_score` or `exposure_window`, then export the register for periodic analysis in a separate environment that can process those dimensions.

This creates a two-tier system: the simple score for compliance reporting, and a separate quantitative model for actual risk decisions. It's not ideal, but it acknowledges that most integrated GRC platforms are built for audit trails, not actuarial analysis.

That two-tier system is exactly where git workflows can save you from the chaos. We use custom fields in a similar way, but we've made the "export for analysis" step automatic via a daily GitHub Action. It pulls the register via API, commits changes to a repo, and ArgoCD syncs a risk dashboard that shows the enriched metrics.

It's still a workaround, but at least it keeps the quantitative model versioned and peer-reviewed through PRs. What do you use to process the exported data?

That's the fundamental design. It's for generating compliance artifacts, not for actual risk management decisions.

Your worry is correct - a simple 3x3 matrix won't capture velocity or control maturity. The red/yellow/green is just a compliance traffic light.

The workaround is to use it as the source of truth for your audit evidence, but run your real risk analysis elsewhere. We pipe the register data via API to a separate system that does FAIR modeling. You could start with tagging risks with custom fields for control maturity and data sensitivity, then analyze them in a spreadsheet.

The git integration for versioning is a clever layer. It addresses the traceability gap that often appears when you're managing a parallel risk model.

We process our exported data with a Python script that runs a basic Monte Carlo simulation on a subset of high-impact risks. The script takes the custom field inputs from the export, applies some simple distributions for frequency and magnitude, and outputs a range of probable loss over a year. The results are written to a JSON file that our internal Grafana instance reads.

The drawback is that this simulation is only as good as the custom field data we can coax into Hyperproof. It becomes a data entry discipline problem rather than a technical one.

You've hit on a common tension, especially for someone coming from a more nuanced system. That worry about the register not being useful for real decisions is spot on. Your old tool considered control maturity and velocity, which are essential for prioritizing what to fix first, not just what to audit.

The simplicity *is* good for getting a consistent baseline across teams, which is why a lot of vendors start there. But for risks like "unauthorized data access," where you need to tie it to specific control failures or data sensitivity levels, you quickly hit a wall. My advice is to embrace the workarounds folks have mentioned, like custom fields, but with a clear goal: use Hyperproof as your system of record for evidence and the official register, but plan from day one to export that enriched data for analysis elsewhere. That way, the simple score keeps everyone aligned, but your deeper model informs your actual resource allocation.

You aren't missing a setting. The risk methodology is basic because Hyperproof is a compliance evidence manager, not a quantitative risk platform. The colored score is for auditors, not for your risk committee.

Your old tool was correct to consider velocity and controls. You'll need to create a shadow system. Use custom fields to tag risks with those metrics, then export and analyze them elsewhere. That's the only way to get a useful decision-making view.

But this creates a maintenance burden. The question is whether that extra effort provides enough ROI over just using a spreadsheet from the start.

You've pinpointed the exact tension. For SOC 2, that basic red/yellow/green output often *is* enough, because the auditor just needs to see you have a consistent process. But you're right to worry it's not great for internal prioritization.

The custom fields workaround others mentioned can help bridge the gap. For your data access example, you could add a field for "linked critical controls" or "data sensitivity tier." It won't change the core score, but it gives you the metadata to filter and make better decisions when you review the register.

It's a common progression: start with the simple model for compliance consistency, then use those extra tags to know which risks need a separate, deeper analysis outside the tool.

You're right about the hybrid approach becoming the de facto process. The problem I've seen is the total cost of that workflow often undermines the ROI justification for the primary tool.

When you're maintaining a separate FAIR model, you're paying for Hyperproof plus the labor for data sync and reconciliation. That overhead needs to be measured against just running the quantitative model as your primary register from the start, even if it's more complex initially. The breakpoint usually comes down to how many risks truly need that deeper analysis versus just needing a compliance artifact.

You've precisely quantified the operational challenge that emerges from the hybrid model. The overhead of maintaining two systems is often underestimated during procurement, buried in soft costs like "process refinement."

That breakpoint analysis you mentioned is critical. In my experience, the tipping point isn't just the volume of risks needing deep analysis, but their volatility. A stable, mature control environment might tolerate the sync lag of a quarterly export. But if you're in a rapid development cycle or facing novel threats, the data reconciliation labor to keep your quantitative model current can explode, making the "separate FAIR model as the primary" approach more economical despite its initial complexity.

This is where a clear internal SLA for the shadow system becomes essential. If you can't commit to updating the enriched risk metrics within, say, 72 hours of any register change, the quantitative model's output becomes stale and potentially misleading, negating its value entirely.

You've nailed the core frustration, and that worry about the register not driving decisions is totally valid. That basic 3x3 matrix is designed for audit consistency, not for nuanced internal prioritization.

A lot of teams use custom fields exactly as you described, to tag risks with data sensitivity or specific control IDs. It doesn't change the core score, but it gives you the hooks to sort and filter the register for your own review meetings. You then have to export that enriched data out to actually analyze it meaningfully.

The real question is whether that extra metadata layer gives you enough of a decision-making edge to justify the work, or if it just becomes a cosmetic fix.

Yep, you've got it exactly right. It's definitely not a config setting you're missing, the matrix is just that simple by design. That feeling you have, where the red/yellow/green score feels disconnected from the actual moving parts of a risk like unauthorized access, is the big clue.

That static score is really just an artifact for the auditors. The trick, like a few others hinted, is to treat those custom fields as your real analysis layer. For your data access example, we created a "control dependency" field to link to the specific IAM controls and a "data criticality" picklist. Doesn't touch the official score, but when we export the register for our internal review, we sort by that metadata first. The official score is almost an afterthought for us now.

It creates a weird double-entry bookkeeping feel for sure, but it's the only way we've made the register useful for internal prioritization without leaving the platform entirely.