Access Management Controls // IT Management Solutions

Access Management Controls

Probability of Identity Verification

To demonstrate how a probability calculation might work, lets use an example of three approaches to restricting access to a patient care database, as shown in Figure 11-1. Bella uses only password authentication, Olivia uses fingerprint recognition biometrics only, and Alex uses both a password and fingerprint recognition.

Because of the general environment and business culture restrictions at Bella’s workplace, security administrators do not require use of strong passwords. Consequently, we determine the probability that an unauthorized individual can access patient information as 30 percent (P = .30). You might rate this differently. However, the process for determining the relative effect of MFA is the same.

In Olivia’s workplace, the security director convinced management that biometrics by itself was strong enough to replace passwords and provide strong-enough identity verification. As we will see in Chapter 12, biometrics is not an identity panacea; it has its own set of challenges. In this case, management requires a low false rejection rate to reduce employee frustration. This results in a probability of 20 percent (P = .20) that someone could masquerade as Olivia and use her login account.

Alex’s security director decided to take a middle path. The director believes strong passwords cause more problems than they prevent: a view supported by business management. He also believes that lowering biometrics false rejection rates is necessary to maintain employee acceptance and maintain productivity levels. Instead of using only one less than optimum authentication factor, he decided to layer two: passwords (something Alex has) and biometrics (something Alex is).

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(415) xxx xxxx /

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