Episode 53 — Assess Human Risk Drivers: Roles, Behaviors, and Likely Failure Points
In this episode, we take a practical look at human risk, not as a vague concept about people being careless, but as a set of predictable patterns you can study and improve. Most organizations already know that attackers target humans, yet many still treat human-driven incidents as random bad luck rather than a system with recurring causes. When you step back and observe how work actually happens, you start to see the same pressures and shortcuts repeating across teams. Deadlines, customer demands, tool friction, and unclear ownership all create conditions where mistakes become more likely. The goal is not to turn every employee into a security specialist, and it is not to lecture people into perfection. The goal is to understand which roles and workflows create the highest exposure, where errors are most likely, and how to redesign the environment so the safest path is also the easiest path. Once you adopt that mindset, human risk becomes manageable because you can intervene with targeted controls and realistic process design.
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Human risk, in a security context, is best defined as actions that enable compromise or loss, even when the person taking the action had good intent. That definition matters because it keeps you focused on outcomes, not motives. A user clicking a malicious link is not the root problem by itself; the root problem might be that the workflow encourages rapid responses, the email filtering allowed a convincing message through, and the environment lacked protections that limit the blast radius of a credential mistake. A manager approving a risky change is not automatically negligence; the risk might be that approval standards are unclear, evidence is hard to obtain, and the organization rewards speed while punishing delays. When you define human risk as actions that enable compromise, you can measure it, because actions leave traces in systems, tickets, and logs. You can also reduce it, because actions are influenced by friction, incentives, and guardrails. This framing moves you away from personality-based explanations and toward operational design. It turns human risk into a problem you can engineer around.
High-risk roles are those that combine privileged access, sensitive data exposure, and business-critical influence, because those roles create concentrated impact when something goes wrong. Privileged access includes administrative capabilities over systems, identity, and finance-related approvals, where a single mistaken step can open broad pathways. Sensitive data exposure includes roles that handle customer records, employee information, payment details, proprietary business data, or legal communications, where disclosure carries legal and reputational consequences. Business-critical influence includes roles that can approve transfers, change vendors, modify payment instructions, authorize emergency access, or override controls to keep operations moving. The most important point is that high-risk is not a moral label, it is a structural description of the role’s potential impact. A high-risk role can be held by an excellent, careful professional and still represent elevated risk because attackers target the role’s authority, not the person’s character. When you identify high-risk roles, you are identifying where the strongest defenses should be concentrated. This is the same principle used in engineering safety systems, where you reinforce the points of highest consequence.
Mapping common mistakes to specific job workflows is where your assessment stops being abstract and starts becoming useful. A mistake is rarely just a mistake; it is typically a decision made under pressure inside a workflow that encourages speed over verification. Consider common patterns like responding quickly to urgent requests, reusing credentials because tool friction is high, sharing files broadly because collaboration is faster that way, or approving changes without full context because the approval queue is overloaded. Each of these patterns has a workflow behind it that can be observed and improved. When you map mistakes to workflows, you learn where the decision points are, what signals the worker uses to decide, and what constraints they are operating under. You can then ask what would make the safe decision easier and the unsafe decision harder. This approach also helps you avoid generic training that does not match how people work. If your training talks about threats in a vacuum while the workflow punishes careful verification, the real driver wins. Workflow mapping aligns your interventions with reality.
The biggest pitfall in human risk work is blaming people instead of fixing systems and incentives, because blame feels satisfying in the moment but it does not reduce the next incident. Blame also drives underreporting, which is a quiet disaster in security because unreported near-misses are lost learning opportunities. When people fear consequences, they hide mistakes, delay reporting, or try to fix problems quietly, which increases damage and reduces your ability to respond. A better model is to treat incidents and near-misses as signals of where the system needs reinforcement. That does not mean accountability disappears, because accountability matters, especially for repeated negligence or intentional abuse. It means you differentiate between reckless behavior and normal behavior that becomes risky under current conditions. Most incidents are caused by normal people doing normal work in an environment that makes risky actions too easy. When you focus on environment design, you improve outcomes and build trust. Trust is essential because you need people to report issues quickly and participate in safer workflows without feeling punished for being human.
A quick win that consistently reduces human-driven failures is redesigning processes so safe behavior is easier than unsafe behavior. This often looks like removing unnecessary steps that push people toward shortcuts, while adding friction only at the highest-risk decision points. For example, if a workflow requires people to copy sensitive data into tickets because the approved tool is too slow, the solution is not to scold them, it is to fix the tool path and remove the need for copying. If approvals are required but the approver lacks context, the solution is to embed context into the approval request so the approver can verify without a separate hunt. If workers frequently bypass a secure file transfer method because it is confusing, the solution is to make the secure method the default and to integrate it into the tools they already use. Process redesign is effective because it changes the daily experience of doing work. When safe behavior is easy, it becomes habitual, and habit is one of the strongest predictors of real-world outcomes. The organization reduces risk not by demanding willpower, but by shaping the work environment.
A scenario rehearsal that reveals real drivers is a finance team being targeted, because finance workflows often combine urgency, external communication, and high-impact actions. Attackers know that payment changes, invoice approvals, and account updates are time-sensitive and can be framed as routine. The pressure to keep money moving is real, and that pressure can reduce verification steps when the workflow is designed around throughput. In this scenario, the question is not whether finance staff are careful, it is whether the workflow makes verification practical under pressure. If payment instruction changes can be approved based on a single email thread, the workflow is fragile. If a secondary verification channel exists but is hard to use or frequently blocked by timing constraints, the workflow will drift toward convenience. The rehearsal should focus on what signals the staff rely on, what steps they skip when rushed, and what obstacles make safe steps inconvenient. Once you identify those pressure points, you can design controls that fit the rhythm of the work. Controls that fight the workflow tend to be bypassed, while controls that support the workflow tend to stick.
Incident patterns are one of the best inputs for prioritizing human risk focus areas because they show where the organization repeatedly fails in similar ways. Patterns might include repeated credential reset fraud attempts, recurring phishing success in specific departments, repeated misdelivery of sensitive files, recurring misconfigurations created during common deployment steps, or recurring approval mistakes in change management. The important detail is to treat patterns as evidence of systemic drivers, not as evidence that a group is incompetent. Patterns tell you where your controls are not aligned with behavior and where attackers are finding repeatable paths. When you analyze incidents, look at the sequence of actions, the time pressure, the tools involved, and what the person believed at each step. This kind of analysis can be done without personal judgment, focusing on the mechanics of the workflow. When you prioritize based on incident patterns, you avoid wasting time on low-impact, low-frequency problems while high-frequency, high-impact paths remain open. Over time, you can measure whether your interventions are working by watching those incident patterns change. When the patterns shift, the program is improving.
Controls should be aligned with risky actions rather than applied uniformly everywhere, because targeted controls reduce friction while increasing protection where it matters most. Multi Factor Authentication (M F A) is a classic example, because it reduces the damage of credential compromise, but it must be implemented in a way that matches how access is used. If the most sensitive actions involve privilege escalation, financial approval, or remote access, the strongest authentication requirements should be tied to those moments. Approvals should be required for actions that change risk posture, such as granting elevated access, changing payment instructions, exporting sensitive datasets, or modifying key security settings. The design goal is to create a narrow set of actions that have high consequence and then make those actions require stronger proof and stronger oversight. This reduces the number of times people experience heavy controls, which increases acceptance, while making the highest-risk actions measurably safer. When controls are aligned with actions, you also get better auditability because you can point to the gates that exist at risk inflection points. This approach respects operational flow while still protecting the business.
A complete human risk assessment must include contractors and partners, because modern operations often extend beyond direct employees, and attackers do not respect org charts. Contractors frequently receive access that is time-bound in theory but persistent in practice, especially when offboarding is inconsistent. Partners may have integration access, shared file transfers, support channels, or collaboration tools that create new paths for social engineering. Third-party personnel also experience different incentives and may be less immersed in your internal culture and procedures, which can increase the likelihood of misunderstandings. Including them in assessment means understanding what access they have, what workflows they participate in, and how their actions are verified and monitored. It also means ensuring that safe processes are available to them, not just written for employees. If contractors do not have access to the approved secure channel, they will use whatever channel works, and that channel may be risky. If partners do not have clear verification steps for sensitive requests, social engineering becomes easier. When you include contractors and partners, you reduce a common blind spot where the organization secures internal workflows while leaving external collaboration paths loosely governed. Those collaboration paths are often where breaches begin.
A helpful memory anchor for predicting human risk is role, pressure, habit, and access, because these four factors explain most real-world failures. Role determines what actions the person can take and what decisions they are expected to make. Pressure determines how much time and cognitive capacity the person has to verify and reflect. Habit determines what they will do when pressured, because under stress people revert to routine rather than policy. Access determines the blast radius of any mistake, because broad access turns small errors into large incidents. When you assess a workflow, you can walk through these factors and quickly see where the risk concentrates. A high-impact role operating under constant pressure with well-established shortcuts and broad access is the classic risk cluster. The solution is rarely to demand better behavior; the solution is to reduce pressure at critical points, improve the default habit by redesigning the workflow, and narrow access so mistakes are contained. This anchor helps you stay practical because it directs you to levers you can actually change. It also helps you communicate with leadership in a way that avoids blaming individuals while still describing real risk drivers.
Improvement should be measured using behavior signals rather than attendance alone, because training attendance is an input and behavior change is the outcome. If you want to know whether risk is reducing, you need indicators that reflect what people do in real workflows. Signals might include reduced rates of risky approvals without verification, reduced frequency of sensitive data exports, increased use of approved secure channels, faster reporting of suspicious requests, reduced credential misuse patterns, or reduced recurrence of the same incident type. The key is to choose signals that reflect the specific workflows you targeted, not generic security metrics that do not map to behavior. Measurements should also be interpreted carefully, because sometimes an increase in reporting reflects improved culture rather than increased risk. A mature program treats measurements as feedback, not as punishment, because punitive measurement encourages hiding rather than improvement. When people know metrics exist to improve systems, they participate more honestly. Behavior signals also allow you to test whether a process redesign is working, because you can see whether the risky workaround disappears when the safe path becomes easier. This creates a continuous improvement loop that is far more effective than periodic awareness campaigns.
When you need to quickly name high-risk roles and why, focus on roles that combine authority and consequence rather than roles that simply have busy schedules. Privileged system administrators are high-risk because they can change controls, create access, and bypass safeguards, and those actions can affect the entire environment. Finance and procurement roles are high-risk because they can move money, modify payment instructions, and approve vendors, and these actions are frequently targeted by social engineering. Customer support and operations roles can be high-risk because they often handle identity recovery, account changes, and sensitive customer interactions under time pressure, which creates fertile ground for manipulation. Engineering and deployment roles can also be high-risk because they control release pipelines and production changes, and mistakes can create exposure at scale. The point is not that these roles are careless; the point is that the workflows contain high-value actions that attackers want to influence. Once you identify the roles, you can map the specific risky actions and design controls around them. This keeps the assessment targeted and respectful while still being honest about where risk concentrates.
To conclude, choose one workflow to harden against mistakes, because concentrated improvements beat scattered efforts when you are trying to reduce human-driven incidents. Pick a workflow where the impact of an error is meaningful, where the workflow is used frequently, and where pressure is real, because those conditions create the strongest return on effort. Then map the workflow step by step, identify the decision points that enable compromise, and observe what people do when they are rushed. Redesign the workflow so the safe decision requires less effort than the risky shortcut, and align controls like M F A and approvals to the high-consequence actions rather than spreading friction everywhere. Ensure that roles and access are narrowed so a single mistake does not become a widespread incident, and make sure the workflow produces auditability so you can detect misuse and learn from near-misses. Finally, measure improvement by watching behavior signals and incident patterns, because those are the real outcomes. When you harden one workflow thoughtfully, you create a pattern that can be applied to the next workflow, and the program becomes a series of real risk reductions rather than a collection of generic advice. That is how human risk becomes predictable, manageable, and steadily lower over time.
