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Annualized loss expectancy

As explained in Data-Driven Cybersecurity

Annualized loss expectancy is the expected financial loss from a particular risk or threat over one year.

Annualized loss expectancy (ALE) estimates the financial loss an organization expects from a specific risk or threat over one year. It turns an asset’s potential loss and the expected frequency of incidents into a yearly figure. section 6.3.2

ALE = Single loss expectancy (SLE) x Annual rate of occurrence (ARO)

ALE equals single loss expectancy (SLE) multiplied by annual rate of occurrence (ARO). SLE is asset value multiplied by exposure factor (EF); ARO is the number of incidents per year. section 6.3.2

Estimate the exposure factor

For a stolen encrypted laptop, an EF of 10% may be more appropriate than 100% because encryption prevents data exposure. Check that assumption before using it in the yearly estimate: an inaccurate EF can understate or overstate the loss. section 6.3.2

How Data-Driven Cybersecurity covers it

How deep it goes
Defines ALE, gives equations for its components, and explains exposure-factor selection through damage, recoverability, and controls.
Examples
Illustrates exposure factors with unrecoverable data loss, short ransomware disruption, and a stolen encrypted laptop.
What you can do after
Evaluate/understand expected yearly loss by combining loss per incident with estimated incident frequency.
How it connects
Links asset value, exposure factor, single loss expectancy, and annual rate of occurrence.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Data-Driven Cybersecurity. The section links point to the parts of the book it draws on.