From Past to Prediction: Why Context Must Reshape Modern Risk Assessment

At first glance, two squares on a chessboard seem completely different in color. But place them in a different context, and you realize they’re exactly the same. Our eyes were tricked by the surroundings. Risk assessments work much the same way — without context, what we think we “see” is often misleading. And just like the illusion, the consequences of misreading reality can be serious.

In contemporary security practice, risk assessment is often portrayed as a rational, data-driven exercise: “We catalog what’s happened, quantify probabilities, and then predict what will occur next.” But in doing so, we commit a subtle but dangerous sleight of hand: by privileging historical events over real, shifting context, we risk missing emergent dangers entirely. The result: organizations believe they are protected — until a disruption shatters their assumptions.

This article argues three related propositions:

1. Modern algorithmic or data-based risk assessment (and even experience‐based security risk assessments) are in practice retrospective—they reflect the past and project it forward.

2. As the weighting of historical data increases, context — the immediate, dynamic environment — is undervalued or excluded. That omission systematically blinds risk models to emerging threats.

3. To address this, risk assessment culture must evolve: the context must be integrated into risk matrices with at least equal weight to historical incident data. This requires structural changes in how “contextual timelines” are defined, how risk is modeled, and how organizations institutionalize real-time context monitoring.

In what follows, I unpack these claims, illustrate them with high-stakes examples (such as Syria’s sudden collapse, or shifting road traffic during pandemic lockdowns), and then propose a practical path forward grounded in academic theory and operational principles of close protection.

1. The Retrospective Trap: What Risk Assessments Really Measure

At its core, risk assessment is the translation of uncertainty into decisionable quantities. Whether done via algorithmic models or via human expertise, the process relies on historical data: past kidnappings, prior attacks, incident reports. This is often viewed as rigorous, but it tends to treat the past as the best (or only) guide to the future.

In systems engineering literature, risk assessment is framed as “deriving security requirements from known threats and vulnerabilities” (e.g. “Security Risk Assessments: Modeling and Risk Level” 2023). But even that formal work concedes that models are only as good as the data input—and what the data fail to show, they cannot protect against.

In humanitarian security and aid operations, the problem is even more acute. The State of

Practice: The Evolution of Security Risk Management in the Humanitarian Space report emphasizes that most security risk assessments in the sector are “context-specific or thematic” and often do not generalize because they under-account shifting local dynamics (Humanitarian Outcomes 2024). If your risk assessment is locked into last year’s baseline, you cannot see next week’s upheaval.

A 2025 article on evolving terrorism risk underscores a similar point: risk assessment frameworks must adapt in step with changes in threats, tactics, and environmental conditions (Adapting Risk Assessments to a Changing Terrorism Landscape 2025). In short: static models in shifting terrain lose their value fast.

Thus, the real danger of “data-driven risk assessment” is not overfitting but under-surprising: that is, missing what the past did not include.

2. Context as the Missing Dimension

What do I mean by “context”? I mean the local forces, trends, flux points, governance dynamics, social shifts, and immediate triggers that shape what is possible in the next hours or days. When a risk assessment ignores the evolving context, several pathologies follow:

• Blind spots to novel threats — risks with no precedent won’t register on a historical baseline.

• Misweighted risk ranking — high frequency but low relevance events may dominate; emergent low-frequency but high-impact threats remain hidden.

• False security confidence — decision makers believe the risk landscape is stable, when it is not.

• Reactive bias — organizations plan only for what they know has already happened, never for what might be unfolding now.

To illustrate, consider Syria. In the lead-up to government collapse or civil war escalation, traditional risk assessments for international organizations would have flagged armed conflict zones, kidnapping, illegal detention, and scarcity. But accelerating context changes — governance collapse, vacuums of authority, trafficking, loss of law enforcement control — emerged so rapidly that a risk assessment done even a week earlier would likely miss them. The European Union’s country of origin guidance on Syria illustrates that security conditions shift rapidly and unpredictably in conflict zones (Country of Origin Information: Syria 2023).

Another example: road traffic risk during the COVID-19 pandemic. A conventional risk model counting pre-pandemic accident rates would have forecast the same level of traffic risk. But when many roads were shuttered, movement restricted, and populations locked down, the actual risk of road accidents plummeted in many areas. If risk models had rigidly followed historical data, they would have overestimated risk and misallocated mitigation resources.

These examples show that context matters not marginally but fundamentally.

3. Principles for Integrating Context into Risk Matrices

To avoid the trap of retrospective overreliance, risk assessment must consciously elevate context. Here are several design principles and operational techniques to do so:

a) Define a “contextual timeline” window

Rather than aggregating data over years, organizations should define a short-interval “context window”—days, weeks, or even hours—within which incident data and environmental indicators are weighted strongly. This ensures that what is happening in the present counts, not just what happened in the past.

b) Hybrid weighting of context and history

In the risk matrix or scoring model, context indicators (e.g. local protests, law enforcement withdrawals, spikes in social tension) should carry equal or greater weight than historical baselines for critical threat categories. That prevents the inertia of past data alone from dominating the model.

c) Real-time signal monitoring

Risk models should ingest “leading indicators” (open source signals, social media analytics, local intelligence, governance indices) continuously, not just periodically. The context becomes alive and responsive, not static.

d) Scenario extremes and “unknown unknowns” buffer

Include scenario-based extrapolations: “If governance collapses, what new threats emerge?” This forces assessors to imagine beyond historical precedent. Some literature on dynamic risk assessment in cyber domains supports scenario simulations (Czekster et al. 2025).

e) Institutional culture shift

Organizations must cultivate a mindset that sees risk assessment as anticipatory sensemaking, not backward looking. Instead of just reporting what has happened, risk units must challenge leadership with “what could unfold next given these contextual trends?”

f) Protocols for local coordination

In the domain of physical security and close protection, basic doctrine emphasizes coordination with local law enforcement. When a VIP travels into a region, if the protection detail does not coordinate with local police, road closures, or traffic control, then even a perfect historical model fails. Many such failures come from lack of alignment with local authorities—i.e., context ignorance.

4. How Contextual Integration Could Alter Outcomes

To ground these ideas, let’s revisit hypothetical and real examples:

• Syria (revisited): If a UN mission had built into its risk matrix real-time indicators (e.g. intelligence of police disintegration, shifts in local party control, mass dissidence), the assessment would have flagged emergent threats like lawlessness, trafficking, or militia fragmentation even before they overtook cities. A purely historical lens would miss this pivot.

• Close protection scenario: Suppose a dignitary travels to a city where recent unrest has flared suddenly. Historical data show no violent incidents in that neighborhood. But real-time context shows heightened protests, security forces pulled to other areas, and local governance under strain. A context-aware risk matrix would adjust threat scores upward—even without prior incidents—forcing higher security posture.

• Pandemic mobility and transport risk: A city reopens and traffic surges unpredictably. A rigid historical traffic-accident model fails to capture new patterns (e.g. new micro-mobility, e-scooters, altered commuter flows). But a context-adjusted model would include mobility data, road usage metrics, and recent local trends in accidents, making predictions more accurate.

When organizations adopt these techniques, their risk planning becomes forward-leaning. They can allocate resources not only to known threats but also to emergent ones.

5. Challenges, Risks, and Practical Limitations

Of course, integrating context is not costless or risk-free. Several challenges arise:

• Data quality and noise: Real-time signals are noisy, uncertain, and sometimes manipulated. Overweighting them can lead to false positives.

• Cognitive load and complexity: More variables means model complexity, which raises the risk of overfitting or misinterpretation.

• Resistance in institutions: Long-trained analysts and managers often trust historical crime data more than speculative signals, making cultural buy-in hard.

• Quantifying context: Turning qualitative context (e.g. political shifts, sentiment, governance fracturing) into numeric scores is methodologically delicate.

• Balancing model stability vs agility: Too much reactivity can make risk scores volatile and undermine credibility.

Academic critiques highlight such tensions. For example, Heyerdahl’s “Risk Assessment without the Risk?” questions how probability and context are conceptualized in security assessments (Heyerdahl 2022). In many systems, probability is over-emphasized, and context (uncertainty) underweighted.

In cybersecurity, scholars propose context-based adaptive cybersecurity risk management frameworks to dynamically adjust risk based on current state (Frank et al. 2025). Such work provides precedent for analogous application in physical security.

6. Path Forward: Institutional and Procedural Reform

To move from theory to practice, security organizations and risk units should adopt the following roadmap:

1. Pilot contextual matrices in critical zones: Start with geographic areas where volatility is high (e.g. conflict zones, unstable governance).

2. Develop context indicator catalogs: List signals (governance metrics, protest trends, local police strength, open source sentiment) and assign scoring rules.

3. Hybrid scoring systems: Create risk matrices that fuse historical and context scores, with safeguards so context doesn’t destabilize the entire model.

4. Continuous feedback and revision: After incidents, backtest context weighting to see if signals would have predicted the event. Adjust model weights accordingly.

5. Train analysts in sensemaking: Equip risk assessors with both statistical literacy and narrative judgment, so they interpret context signals, not just numbers.

6. Institutionalize coordination protocols: In security operations, protocols must require local authority coordination—so the context of policing, traffic, local capacity is embedded, not optional.

Over time, this cultural shift helps organizations avoid being deceived by the past, and instead become more attuned to the present.

Conclusion

Risk assessment is most dangerous when it masquerades as foresight while anchored solely to hindsight. By relying disproportionately on historical data, modern security models risk becoming retrospective mapmakers, not forward scouts. The real world is dynamic; threats emerge in the cracks of change, not always in the shadows of past incidents.

To defend against this, context must be elevated—not as an afterthought, but as a coequal input. Defining short contextual timelines, hybrid weighting, real-time signal ingestion, scenario planning, and cultural reform are the tools to achieve this. Organizations that remain anchored to history alone will find themselves surprised, unprepared, and exposed.

The future of security risk assessment lies not in perfect predictions, but in sensitive anticipation.

Works Cited

“Adapting Risk Assessments to a Changing Terrorism Landscape.” Journal of Forensic Sciences, 2025.

Chandra, N. A. “Information Security Risk Assessment Using Situational Awareness Models.” Risks, vol. 10, no. 8, 2022.

Frank, et al. “Context-Based and Adaptive Cybersecurity Risk Management Framework.” Risks, 2025.

Heyerdahl, A. “Risk Assessment without the Risk? A Controversy about Probability in Security.” Journal of Security Studies, 2022.

“Risk Assessment Methods for Process Safety.” Safety Science, 2024.

“Security Risk Assessments: Modeling and Risk Level.” Proceedings of ACM Workshop, 2023.

“State of Practice: The Evolution of Security Risk Management in the Humanitarian Space.” Humanitarian Outcomes, 2024.