Human Risk Management

A Human Risk Management (HRM) platform is a technology solution that helps organizations identify, measure, manage, and reduce security risks arising from employee behaviour. By combining behavioural and security signals, it identifies where human risk exists and enables targeted interventions. Rather than simply delivering training, an HRM platform provides a continuous, data-driven approach to managing human cyber risk.

Adaptive human risk management continuously adjusts security interventions based on employees’ actual behaviours and changing risk levels, rather than applying the same training to everyone.

Below are few steps it typically follows:

Collect behavioural signals - Gather data from phishing simulations, reported incidents, risky clicks, policy violations, assessments, and other relevant activities.

Assess individual risk - Use these signals to build a dynamic risk profile for each employee or user group.

Personalise interventions - Provide targeted training, coaching, or additional controls based on identified risks.

Re-measure and adapt - Track behavioural changes and continuously adjust the risk assessment and interventions.

In short, adaptive HRM creates a continuous measure → intervene → learn → adapt cycle, making human risk management more targeted, responsive, and personalised.

Benchmarking human risk means measuring your organisation’s current level of human-related cyber risk and comparing it against a defined baseline, internal groups, or relevant industry benchmarks.

A practical approach is to:

1. Define key risk behaviours - Identify behaviours that could expose the organisation, such as phishing susceptibility, poor data handling, or failure to report incidents.

2. Collect behavioural data - Use phishing simulations, assessments, incident reports, policy violations, and other behavioural signals.

3. Create a baseline - Establish your current risk level across individuals, teams, or the organisation.

4. Compare results - Benchmark performance across departments, roles, locations, time periods, or industry peers where reliable data is available.

5. Track improvement - Re-measure regularly to determine whether interventions are reducing human risk over time.

Improving human risk scores is not about simply increasing training completion rate. It is about changing the behaviours that create security risk.

A practical approach is to:

• Identify high-risk behaviours - Focus on the specific actions driving elevated risk scores.
• Target interventions - Provide personalised training, coaching, or simulations based on individual risk patterns.
• Reinforce secure behaviours - Use timely reminders, practical guidance, and real-world scenarios.
• Address root causes - Improve processes, access controls, or workflows when risky behaviour is driven by friction or poor design.
• Measure behavioural change - Re-test and track whether risky behaviours decrease over time.
• Continuously adapt - Adjust interventions as employee behaviour and risk levels change.

Human risk management works best when employees see security as part of their everyday responsibilities; when security is relevant, personalised, practical, positive, and continuous.

Make it relevant: Connect security guidance to employees’ actual roles, risks, and daily tasks.

Personalise interventions: Use behavioural insights to provide targeted guidance instead of generic training for everyone.

Keep it practical: Focus on simple actions employees can apply immediately.

Use varied formats: Combine short learning modules, simulations, scenarios, reminders, and real-world examples.

Create a positive experience: Avoid fear or blame. Encourage employees to report mistakes and suspicious activity without hesitation.

Show progress: Share meaningful improvements and demonstrate how employee actions contribute to reducing risk.

Make it continuous: Reinforce secure behaviours throughout the year rather than relying on annual training.

Measure and adapt: Track engagement and behavioural outcomes, then adjust interventions based on what the data shows.