How control assessments influence the likelihood of risk events in the FAIR model

Let’s talk about risk in plain terms. Think of a risk like a weather forecast for your information systems. The forecast isn’t just about big storms; it’s about how likely the storm is, how hard it hits, and what you’re doing to prepare. In the FAIR model—short for Factor Analysis of Information Risk—the calculation boils down to two things: how often a loss event could happen (the likelihood) and how bad it would be if it did (the impact). And that’s where a control assessment steps in. Its main job is to illuminate the probability side of the equation.

What is a control assessment in FAIR, really?

In simple terms, a control assessment checks the line-up of safeguards you already have and asks, “Are these safeguards actually doing the job?” It’s not just about whether a control exists on paper; it’s about whether it works in practice. You examine:

• What controls are in place for a given risk scenario

• Whether the controls are well designed to block or slow down the threat

• How those controls perform in the real world (operating effectiveness)

• How comprehensively those controls cover the scenario (coverage)

The outcome isn’t a yes-or-no verdict. It’s a nuanced read on how effective each control is at lowering the chance that a risk event occurs. And that nuance matters a lot. Because if a control looks good on paper but fails in operation, the likelihood of a loss event sticks around, maybe even with surprising stubbornness.

Why likelihood is the star of the show

Here’s the thing: risk is not a single number that sits quietly in a file. In FAIR, risk is seen as a product. You multiply the frequency with the magnitude of loss. If you want to cut risk, you can go after either side, but most of the meaningful gains come from reducing the likelihood—the probability that a threat event actually results in a loss, given the controls you have.

A quick mental model helps. Imagine you’re trying to keep a leaky faucet from flooding a room. You have a few valves (controls) in place. A control assessment asks: Are those valves properly installed? Do they seal when water pressure rises? Do they cover all the relevant pipes, or are there gaps you’ve overlooked? If the valves work reliably, the chance of a flood goes down. If some valves aren’t up to the task or aren’t used correctly, the risk of a flood remains higher than you thought. In short, the assessment translates the technical strength of controls into the practical likelihood of a bad event.

How to actually assess controls (a practical route)

If you’re mapping this out, you’ll want a clear, repeatable approach. Here’s a straightforward way to proceed without getting tangled in jargon or endless spreadsheets:

• Map controls to risk scenarios. Start with the loss events you care about, and link each scenario to the controls meant to mitigate it. This creates a clean picture of what’s supposed to reduce what.

• Check design vs. reality. Ask: Is the control well designed for the threat? Is it the right control for the vulnerability? Do we understand the threat landscape clearly enough to justify the control?

• Gather evidence of operating effectiveness. Look for logs, test results, incident records, and interviews. The goal is to confirm that the control is not just theoretical but actually functioning when it’s needed.

• Assess coverage. Are there gaps where a threat could exploit a vulnerability despite the control? If yes, you’ll see where the likelihood doesn’t shrink as much as you’d hoped.

• Recalculate the likelihood. Based on the evidence, adjust the probability of the loss event for the scenario. If a control is strong and consistently operated, the likelihood drops; if it’s weak or inconsistently applied, the likelihood stays higher.

• Prioritize actions. The changes that move the needle the most—those that reduce the highest-probability, highest-impact scenarios—should rise to the top of the to-do list.

A concrete, bite-sized example

Let’s say a company worries about unauthorized access to its sensitive data. The risk scenario involves an attacker guessing credentials and breaching a server.

• Controls in play: a strong password policy, enrollment of multi-factor authentication (MFA), and continuous monitoring for anomaly login attempts.

• Design check: MFA is well-suited to block credential theft; logs exist to support monitoring.

• Operating effectiveness: MFA has been adopted across the main apps, and monitoring alerts are generated when anomalies appear.

• Coverage: The controls cover most entry points, but a few legacy systems aren’t enrolled in MFA yet.

• Likelihood readout: With MFA and monitoring, the probability of a successful unauthorized access drops, but because a subset of systems is MFA-free, the overall likelihood remains non-negligible.

That little math shift matters. The few systems left out become the weak link. The assessment makes the problem concrete and helps you decide whether to invest in MFA rollout across all systems, introduce compensating controls for the legacy platforms, or adjust monitoring to catch attempts at those gaps.

Gaps, decisions, and where you focus your energy

A control assessment often reveals more than “how well do we perform.” It highlights where coverage is thin and where the risk model needs updating. When you know which controls actually cut the probability of a loss event, you can allocate your resources with confidence. It’s about smart disruption—fix the gaps that matter most, and you’ll see a real drop in LEF (loss event frequency) figures.

This is where management questions come in naturally. If a control is effective but expensive, is the cost justified by the risk reduction? If a control is cheap and easy to implement, what’s holding you back from deploying it everywhere? The answers aren’t just technical; they shape budgets, staffing, and timelines. The control assessment becomes a negotiation tool with the business side, translating technical security into tangible risk exposure.

Common missteps worth avoiding

Like any practical activity, control assessment has its potholes. A few to watch out for:

• Treating controls as binary. If a control is “on” or “off,” you’ll miss how well it actually works. Real life is messy; effectiveness slides with usage, configuration, and context.

• Ignoring real-world evidence. The best-designed control can fail if people don’t use it or if processes don’t support it. Evidence matters.

• Forgetting coverage gaps. A single blind spot can undermine a solid control set. Look for those under-the-radar points where attackers might slip through.

• Relying on optimistic assumptions. If you assume a control will magically perform at peak all the time, you’ll overestimate risk reduction. Ground estimates in data.

• Letting updates stall. The threat landscape changes. Your controls and the likelihood they influence should be revisited as new information appears.

Tools and resources you can explore

If you want to dive deeper without getting lost in the weeds, several reputable avenues exist:

• OpenFAIR. A community-driven approach to the FAIR taxonomy, useful for structuring risk discussions and aligning teams on probability and impact.

• FAIR Institute resources. A great starting point for practical frameworks, terminology, and case examples that translate theory into action.

• RiskLens (and similar risk-analysis tools). These platforms help quantify risk using FAIR concepts and can streamline control assessments with built-in data models.

• Real-world case studies. Look for reports or articles from organizations that walk through how they mapped controls to risk scenarios and what the results looked like in practice.

A gentle reminder about nuance

One of the coolest parts of the FAIR approach is its honesty about uncertainty. While the aim is to sharpen understanding of likelihood, you’ll rarely land on a single exact probability. Instead, you’ll build ranges and test sensitivities. The goal isn’t perfect precision; it’s better visibility—so you can see which levers truly move the dial and which ones merely nudge the numbers.

Bringing it all together: what this means for you

If you’re studying or building a risk program, remember this: a control assessment is the bridge between “we have safeguards” and “these safeguards actually reduce risk.” It’s not enough to have controls on a diagram or in a policy. You want to know how well those controls hold up under pressure, what gaps they leave, and where you should invest to reduce the chance of a loss event.

Think of it as a practical mapping exercise—a way to translate security measures into real-world probability shifts. When you can say, with some confidence, that a particular control lowers the likelihood of a damaging event, you’re not just arguing about security for security’s sake. You’re informing decisions that save time, money, and trust.

A closing thought

The beauty of the FAIR approach is that it invites a candid conversation about risk. It asks you to look at controls not as decorative stamps but as active players in the safety net. By focusing on how effectively those controls reduce the probability of loss, you equip yourself to make smarter, more defensible choices. And that, in turn, keeps the conversation with stakeholders honest, practical, and finally, productive.

If you’re curious to explore further, start small: pick a risk scenario you care about, map the controls tied to it, gather a few pieces of evidence about how those controls perform, and project how the probability of a loss event shifts. You’ll likely see that the most meaningful work happens where control effectiveness meets real-world usage—and that’s where risk management earns its keep.