As a security leader, you face an inevitable daily reality: a flood of alerts pouring in from dozens of different tools. Risky sign-ins are flagged in Microsoft 365, weak passwords are pinged from a vault audit, and a separate report identifies which employees failed the latest phishing simulation. While all this information is valuable, most leaders are unable to connect these separate data points to paint a clear, cohesive picture of an individual user’s overall risk.
This is the silo problem in action. Crucial user risk data is “trapped” within the very tools meant to provide security insight. When these systems don't communicate, security teams are left with a disjointed and incomplete assessment. Critical patterns are missed, and security teams are unable to accurately measure an individual’s true risk profile.
Breaking down these data silos is essential for shifting from a reactive to a proactive security posture. This blog provides a step-by-step playbook for unifying your user risk data into a single, actionable dashboard. We will walk through how to inventory your data sources, consolidate these insights, and use that unified view to drive real-time visibility and more effective security decisions.
The high cost of a siloed security view
A siloed view of user risk isn't just an inconvenience—it represents a fundamental flaw in an organization's defense. When critical data points are isolated, security teams are forced to operate with blinders on, making it impossible to see emerging threats or accurately assess the true risk landscape.
Why disjointed data fails
In most organizations, crucial pieces of user risk data reside in separate, non-communicating systems. For example, performance in security awareness training might live in one platform, while password hygiene reports exist in another. Endpoint alerts from EDR solutions have their own dashboard, while logs detailing SaaS application usage are isolated elsewhere. Each system provides valuable data, but only a very narrow snapshot—incapable of telling the full story on its own.
Think of it like a doctor trying to assess a patient’s health by looking at only one lab result at a time. A cholesterol reading on its own is just a number; when viewed alongside blood pressure, family history, and lifestyle factors, it becomes part of a comprehensive risk assessment. Likewise, a security team that sees only that an employee failed a phishing test—without context from their password hygiene or recent SaaS activity—is missing the critical insight needed to understand that user’s actual risk.
How incomplete assessments lead to breaches
These data silos create incomplete assessments of a user’s overall risk posture and obscure compound risks, where the combination of several low-level events can create a high-priority threat. Without a unified view to connect these alerts, security teams end up manually chasing individual signals, often missing the bigger picture until after a breach has occurred.
In a siloed environment, making these critical connections in real time is nearly impossible. This lack of a unified context is where preventable security incidents evolve into full-blown data breaches. Consider these examples:
Example 1: The Susceptible and Vulnerable User
An employee fails a phishing simulation, generating a low-level alert in a training platform. Separately, a password audit reveals this same user has a weak, reused password that has previously appeared in a data breach. In isolation, these are two minor findings. When correlated, however, they reveal a high-risk individual who is both susceptible to phishing and easily compromised—a prime target for an attacker.
Example 2: The Risky SaaS Adoption
A finance employee is granted high-privilege access to a new SaaS application that can sync with their company’s financial data. A day earlier, EDR logs showed that this same user downloaded an unapproved application from a questionable website. Viewed separately, these events might not raise alarms. When viewed together, they create a clear security concern that requires immediate investigation: Is the user preparing to exfiltrate financial data, or has their account been compromised for that very purpose?
How to consolidate your user risk data
Consolidation is the solution to the security gaps created by data silos. Breaking down these walls and bringing disjointed data streams together builds the unified view of user risk needed for proactive, intelligent security decisions. Follow these three steps to connect the data silos in your organization.
Step 1: Inventory all sources of user behavior data
You can't consolidate what you don't know you have. The foundational first step is to conduct a thorough inventory of every system that generates data about user actions and security posture. Identify and map out these systems, noting where data is collected and stored. The goal is to create a comprehensive list of data sources that, together, can paint a complete picture of an individual’s digital behavior.
This process will likely require collaboration across departments (like IT, HR, and business unit managers) to uncover the full scope of tools in use, both sanctioned and unsanctioned. While every tech stack is unique, most user risk data resides in a few key categories. Use the following checklist to begin your inventory:
- Identity and access management (IAM) and SSO logs: Data on user logins, access requests, authentication successes and failures, and multi-factor authentication (MFA) status
- Microsoft 365 / Google Workspace audit logs: Insights into file sharing, email activity, and user-authorized third-party app integrations
- Security awareness training and phishing simulation platforms: Records of training completion, quiz scores, and which users are susceptible to simulated phishing attacks
- Password manager and credential vault alerts: Data on weak, reused, or potentially compromised passwords associated with employees
- SaaS management platforms (SMPs) or direct SaaS usage reports: Cloud applications employees are using and the permissions they have granted
- Endpoint detection and response (EDR) solutions: Data on device security posture, software installations, and interactions with potentially malicious files or websites
- Data loss prevention (DLP) systems: Alerts on unusual data movement or potential exfiltration attempts by users
Step 2: Establish a centralized view with an integration-friendly tool
After mapping your data sources, the next step is to aggregate them into a single, unified dashboard. The goal is to create a “single pane of glass” for user risk, allowing your team to analyze and correlate events without manually connecting data from dozens of different tools. This centralized view is what transforms isolated data points into a cohesive risk narrative for each user.
There are several ways to achieve this, each with its own level of complexity:
- Custom data lake integration: Building a custom solution offers maximum flexibility but requires significant in-house development resources, expertise, and ongoing maintenance to manage data pipelines and integrations.
- Leveraging a modern SIEM: A Security information and event management (SIEM) tool is powerful for log aggregation. However, most SIEMs are event-centric, not user-centric, and typically require extensive custom rule-writing and data parsing to correlate disparate events back to a specific individual's risk profile.
- Using a specialized user risk platform: The most efficient approach is often to use a specialized tool designed to ingest and correlate these varied data streams. These platforms are built with user-centric correlation in mind, providing out-of-the-box integrations and risk models that reduce the need for custom development (and deliver actionable insights more quickly).
Step 3: Implement automated and continuous data aggregation
To be truly effective, your unified view of user risk must be as close to real-time as possible. A user’s risk profile can change in a single day, meaning manual data pulls—even weekly ones—will always leave your security team looking at an outdated picture of risk.
Implement automated, continuous data aggregation, primarily through API integrations. Automation ensures that your centralized dashboard is constantly updated with the latest user activities and security alerts from across your environment. This continuous flow of information enables your team to spot emerging risks as they happen, not weeks or months after the fact. Automated data aggregation transforms your user risk program from a static, backward-looking review into a dynamic, forward-looking security function.
From data to decisions: Activating your unified risk insights
The true value of consolidated user risk data is unlocked when you activate these holistic insights to make faster, more informed security decisions. This is where your organization shifts from a reactive posture of chasing disconnected alerts to proactively mitigating risks before they escalate.
Correlating events and integrating threat intelligence
Once your user behavior data is centralized, you can begin to correlate it. A unified platform allows you to connect seemingly unrelated events from different systems to uncover hidden risks that are invisible in siloed views. For example, a single alert about a user failing a phishing simulation might be ignored. But what if it occurs just days after that same user granted excessive permissions to a new SaaS application? A unified context immediately elevates the seriousness of both events.
This capability becomes even more powerful when you integrate internal user risk indicators with external threat intelligence. A password audit might reveal that an employee is reusing passwords, a low-level risk on its own. However, if threat intelligence shows that one of their known passwords was just leaked in a new third-party breach and a phishing campaign is targeting their department, that low-level risk instantly becomes a critical threat. This fusion of internal behavior with external context allows your team to understand not just what could happen, but what is likely to happen next.
Implementing user risk scoring and prioritization
The sheer volume of centralized data can be overwhelming. It’s critical to translate this raw, correlated data into a single, actionable risk score for each user. A dynamic risk score synthesizes various data points—such as password hygiene, SaaS usage, and training performance—into an easily understood metric. This score should automatically adjust in near real-time as new behaviors and events are detected.
This approach directly solves one of the biggest challenges for security teams: alert fatigue. Instead of treating every low-level alert with equal urgency, teams can use risk scoring to prioritize their efforts effectively. By focusing time and resources on the top 5% or 10% of users who pose the most significant risk at any given moment, your team can maximize its impact by addressing the most critical vulnerabilities first.
Enabling proactive intervention before a breach occurs
Real-time, holistic views of user risk allow security teams to move from post-incident investigation to proactive intervention. This pre-breach intervention model means your team can take targeted, defensive actions based on risk indicators before an attacker has the chance to succeed. A unified and scored view of user risk enables a variety of automated or semi-automated interventions:
- Targeted micro-trainings: Automatically assign a short, specific training module on phishing after an employee clicks on a simulation link, reinforcing the lesson at the exact moment it's most relevant.
- Adaptive access controls: Temporarily restrict a user's access to sensitive data or critical systems if their risk score suddenly spikes above a predefined threshold, pending a review.
- Proactive credential resets: For a high-risk user whose credentials have been spotted on the dark web, you can immediately initiate a forced password reset and MFA re-enrollment to neutralize the threat.
- Automated security nudges: Deliver real-time reminders based on user context, such as a pop-up about company data policy when a user attempts to log into an unsanctioned AI tool.
The unified view: A new era for user risk management
Operating with a siloed view of user risk is no longer a sustainable strategy; it leaves organizations dangerously exposed to threats that thrive in the gaps between disconnected security tools. Breaking down these silos and unifying user-risk data streams allows security leaders to finally get the complete picture needed for an effective defense. This transformative approach moves security from a reactive, alert-driven program into a proactive, intelligence-led model focused on mitigating human risk before it can escalate into a breach.
Ultimately, the ability to consolidate and correlate user-centric data into a single, actionable view is not just an efficiency gain—it is the cornerstone of modern human risk management. This deep insight into user behavior is what empowers organizations to make smarter security decisions, foster a more resilient culture, and confidently navigate the complexities of today's threat landscape.
If you’re interested in learning more about how UpGuard is helping organizations automate human risk management, visit https://www.upguard.com/contact-sales.
