Risk Analysis and Predictive Analytics: Why are they important?

Design brainwave

It is essential to use analytics and controls to highlight risky situations linked to access data.

Digital transformation is driving an exponential increase in the number of applications and data being managed digitally. The best way to regain control is to map the access to know who works for the company and to what they have access. But mapping alone cannot reduce the risk.

Access can relate to accounts, groups and identities as well as access rights and permissions, so where should you start? Identity Analytics can be used to analyze and apply controls adapted to highlight the situations at risk. The types of risk we are referring to are:

  • puce Risks related to poor data quality.
  • puce Deviations from established management rules.
  • puce Unexpected events.

With Identity Analytics, navigate risky situations linked to access by combining these three indicators:

  • puce Control-based risk scoring.
  • puce Maximum risk level.
  • puce Transformation from risk scoring to risk ranking.

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