Human Risk Management. Reimagined for Today's Workforce.

Many vendors focus on a single slice of the problem, such as phishing simulations or training delivery. Living Security takes a broader approach by connecting those signals into a unified view of workforce risk.

Rather than treating events like phishing clicks as isolated failures, Living Security correlates behavior, identity, and access data to reveal patterns and guide action. This allows teams to start where they are, whether with phishing or training, and evolve toward measurable risk reduction without rebuilding their program.

AI-native Human Risk Management means intelligence is built into the core of the platform rather than added on later. Instead of using static rules or surface-level analytics, AI-native HRM continuously analyzes behavioral, identity, and threat signals, learns from outcomes, and adapts over time.

This allows teams to anticipate risk earlier, understand why it matters, and take consistent action with human oversight. AI-native HRM focuses on helping teams make better decisions at scale, not replacing human judgment.

CISOs should look for platforms that move beyond awareness metrics and provide measurable risk reduction. Key considerations include the ability to correlate signals across systems, explain why users or behaviors are risky, guide consistent action, and prove outcomes over time.

Equally important are transparency and control. Recommendations should be explainable, actions auditable, and humans kept in the loop. The right HRM platform should reduce operational burden while increasing confidence in decisions and results.

Workforce risk refers to the security risk introduced by all identities that interact with systems, data, and applications. This now includes both humans and AI agents. Employees, contractors, and AI agents all make decisions, access resources, and take actions that can increase or reduce risk.

As AI agents become more autonomous and capable, they introduce new forms of risk that behave more like human risk than traditional machine risk. Managing workforce risk requires a unified approach that accounts for behavior, access, and impact across both human and non-human identities.

Measuring human risk requires looking beyond isolated events and static metrics. Modern HRM programs correlate multiple signals such as phishing behavior, identity posture, access level, policy violations, and repeated behaviors across systems.

By analyzing these signals together over time, organizations can identify patterns, track changes in risk posture, and measure whether interventions actually reduce exposure. The result is a defensible view of risk that reflects real behavior, not just activity in a single tool.

Human Risk Management (HRM) is an approach to security that focuses on reducing risk created by human behavior, not just improving awareness or compliance. Traditional security awareness and training programs are designed to educate users and measure participation through metrics like completion rates and phishing clicks. HRM goes further by continuously measuring risk, correlating behavior across systems, and guiding interventions that reduce actual exposure.

In practice, HRM shifts the goal from “Did people complete training?” to “Did risk go down?” It treats human behavior as a dynamic risk surface that can be understood, prioritized, and managed over time.

Traditional User and Entity Behavior Analytics (UEBA) focuses on detecting anomalies, often generating alerts when behavior deviates from a baseline. While useful for detection, UEBA typically operates in isolation and stops at alerting.

Behavior-based risk modeling, as used in modern HRM, looks at patterns over time and across systems to understand risk trajectories, not just anomalies. It connects behavior to identity posture, access level, and business impact, and ties insights directly to guided or automated interventions. The goal is not just to detect unusual behavior, but to reduce risk before incidents occur.