Uncertainty sits at the heart of risk, and the FAIR framework helps us understand what we can't know.

Risk is a futures puzzle, not a crystal ball. We’re forecasting what might happen to our systems, data, and operations, but the future stubbornly refuses to be predictably neat. So, when a multiple-choice question asks, “Since risk is invariably a matter of future events, there is always some amount of _______________,” the right fill-in is Uncertainty. Let me explain why that’s the heart of risk thinking—and why it matters from the data center to the boardroom.

What does uncertainty really mean in risk?

Think of risk as the mix of what could happen and how bad it would be if it did. In the information-risk world, that means potential losses tied to threats and vulnerabilities. The future, however, isn’t a script that’s been fully written. We don’t know every variable, every attacker move, every regulatory twist, or every cost of remediation. That gap—the unknowns—is uncertainty. It isn’t a flaw in our thinking; it’s the nature of looking ahead.

Contrast that with probability. Probability is a number we assign to a known event—the chance that something happens. Uncertainty is the space around that number—the parts we don’t know, the questions we can’t answer yet, the outcomes we can’t even imagine with confidence. In risk work, you’ll often hear: probability tries to quantify likelihood; uncertainty tries to acknowledge what we don’t yet know or can’t measure precisely.

Where uncertainty shows up in FAIR

FAIR—the Factor Analysis of Information Risk—doesn’t pretend to remove uncertainty. Instead, it structures uncertainty so we can reason about risk in a disciplined way. In FAIR, risk is built from a few core pieces, two of which are especially sensitive to the unknowns:

• Loss Event Frequency (LEF): How often a threat event might occur in a given time period. Uncertainty here comes from our ignorance about attacker prevalence, how effectively controls work, how often people make mistakes, or how a system’s configuration might evolve.

• Loss Magnitude (LFM): How costly the resulting loss could be if an event happens. Uncertainty here includes the potential scope of data exposure, the speed and cost of containment, legal or regulatory penalties, brand damage, and the cascade of indirect effects.

Both pieces are laced with uncertainty because the future depends on many moving parts. You can have a scenario where a vulnerability exists, a threat actor is active, and a control exists—but you still don’t know exactly how often an incident occurs or how severe the impact will be in a real-world event. That’s the essence of uncertainty in practice.

Why uncertainty isn’t “just” a theoretical concern

A lot of risk thinking sounds precise because we love numbers. But the numbers we love are only as good as the assumptions behind them. If you pretend you know exact probabilities and precise losses, you’re setting yourself up for overconfidence when surprises show up. The world of information risk is noisy: new threats emerge, systems change, people alter their behavior, and economic conditions shift. Uncertainty is the honest counselor that keeps us from pretending we have a crystal ball.

A quick contrast that helps: weather forecasts vs. a football score

• Weather forecasts often give probabilities: a 70% chance of rain. That probability is useful precisely because it comes with a transparent sense of uncertainty—the forecast team knows where its model is strong and where it’s rough.

• A football score is a single outcome: rain or no rain, win or lose. If you asked a forecaster to state the exact score of the game next Sunday, you’d get a lot of stubborn uncertainty. In risk terms, that’s the cocktail we’re always dealing with: probabilities for events, and the uncertainty that those probabilities and outcomes may be off.

How uncertainty threads through the risk assessment process

Let’s map the idea onto a practical risk exercise without getting lost in jargon. Suppose you’re assessing a cyber risk for a midsize company. You’ll look at two levers:

• LEF: How often might a cyber incident occur? That depends on threat activity in your sector, the exposure of your systems, the effectiveness of your defenses, and even how often employees fall for phishing attempts. Each of those factors carries uncertainty. Some are data-driven, some are intuitive judgments, and some sit in the uncertain middle.

• LFM: If an incident happens, what would it cost? You’ll consider data breach costs, system downtime, regulatory fines, customer churn, remediation expenses, and reputational impact. Each cost element has a range of potential outcomes, not a single fixed number. Uncertainty here means you’re dealing with best-case, typical, and worst-case scenarios rather than a single, clean price tag.

And here’s the kicker: uncertainty isn’t something to erase. It’s something to illuminate. When you lay out the uncertainties clearly, you can compare different risk scenarios side by side, see which assumptions drive the biggest swings, and focus on the controls that actually move the needle.

How to handle uncertainty without losing your footing

The goal isn’t to pretend you know everything. It’s to be explicit about what you don’t know and to build decision-relevant insight around that honesty. In practical terms, that looks like a few well-trodden approaches:

• Use ranges and tiers: Instead of a single number for LEF or LFM, provide low/typical/high ranges. This helps stakeholders see the spread of possible outcomes and the sensitivity of decisions to different assumptions.

• Scenario thinking: Craft a few plausible futures—optimistic, baseline, and pessimistic—and explore how risk shifts in each. Scenarios force you to test how robust controls are across different conditions.

• Sensitivity analysis: Ask, “Which inputs, if changed, move the risk the most?” Then prioritize data collection or control improvements that tighten those high-impact uncertainties.

• Document assumptions: Put a light, readable note on every assumption. If the assumption proves false, what changes? This keeps the analysis resilient and navigable.

• Leverage multiple data sources: Combine internal data, industry reports, and expert judgment. When sources disagree, it’s a signal to probe further, not to pretend certainty exists.

• Communicate with clarity: Present the uncertainty in plain terms. Stakeholders don’t need every statistical nuance; they need to understand what could happen, how likely it is, and what you can do about it.

A few concrete examples to make it real

• In a data-privacy program, LEF might be uncertain because attacker patterns shift with new software vulnerabilities. LFM could be uncertain because regulatory penalties depend on jurisdiction and the severity of the breach. Your analysis would show a range of potential losses and highlight where tightening access controls or improving incident response timing makes the biggest difference.

• In a supply-chain risk review, uncertainty can stem from vendor reliability and geographic disruption scenarios. You may report that if a key supplier experiences a regional outage, the loss magnitude could spike sharply, but the exact duration of the outage is uncertain. That insight helps you decide where to build redundancy or diversify suppliers.

• In a security-operations context, uncertain human behavior—the likelihood of a phishing attempt succeeding—plays a big role. The best you can do is quantify the effectiveness of training and controls, and then model how those changes shrink your risk across the LEF and LFM spectrum.

Digressions that help, not distract

You might wonder why we’re spending so much time on uncertainty when we’re supposed to be protecting information. Here’s a simple take: fear of the unknown often leads to paralysis or poor choices. Naming uncertainty, then bounding it with structured analysis, gives you a map for navigating risk without pretending the map is a perfect plan. It’s the same reason teams run tabletop exercises or stress tests—by rehearsing what could happen, you harden your posture against surprises.

As you think about the bigger picture, you’ll notice uncertainty also nudges you toward better governance. It invites clearer roles, more transparent decision rights, and more thoughtful risk reporting. It’s not a nuisance; it’s a compass that points toward smarter controls, better data, and more informed conversations with leadership.

A practical mindset shift you can carry forward

If you take one takeaway away from the discussion, let it be this: uncertainty is not the enemy of good risk management. It’s the honest partner that reminds you to check your assumptions, gather the right signals, and test how changes ripple through the risk landscape. In FAIR terms, you’re not chasing a single number; you’re building a story about what could happen, how bad it could be, and what you’re willing to do to reduce the odds or soften the impact.

Quick-start ideas for teams embracing uncertainty

• Start with a simple LEF/LFM ladder: low, typical, high. Attach a brief rationale to each rung.

• Pick one critical assumption per risk scenario and draft a one-sentence exposure note clarifying why it matters.

• Run a two-scenario test: one where controls are assumed perfect, another where they’re only partially effective. Compare outcomes.

• Create a one-page risk thumbnail for leadership that shows potential losses, main drivers of uncertainty, and the top actions with the greatest uncertainty reduction.

• Schedule a quarterly sanity check: re-evaluate key uncertainties as projects evolve and new data comes in.

So, what’s the bottom line?

Risk can never be perfectly predicted. The future is, by its nature, uncertain. That’s not a flaw; it’s the fundamental truth of decision-making under complexity. The strength of a solid information-risk approach lies in how well we acknowledge and map that uncertainty. By framing risk with clear LEF and LFM considerations, by using ranges, by testing assumptions in scenarios, and by communicating openly about what’s uncertain, you gain a practical grip on what to do next.

If you’ve ever felt that risk talks glow with precision but leave you with a nagging doubt, you’re not alone. The real power lies in embracing uncertainty as a guide rather than a shadow. It nudges you to collect better data, refine your controls, and keep conversations with stakeholders grounded in reality. And when you do that, you’re not just managing risk—you’re building resilience into the very fabric of your information environment.

In the end, risk is future-facing. Uncertainty is the honest companion that keeps us plant-footed, adaptable, and ready to respond to whatever comes next. That mindset—paired with a structured framework like FAIR—helps teams turn ambiguity into action, not paralysis. And that, in turn, is how organizations stay secure, informed, and capable of moving forward with confidence.