Understanding the Difference Between Inference and Prediction With AI

Donovan Blaylock II
April 08, 2019

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Often people will confuse prediction with inference. While the differences are actually very subtle, they have magnitudes of significance when it comes to making decisions on who, when and how one should be accessing your information infrastructure. Chief Information Security Officers implicitly understand that in only takes one bad actor to gain access to their information assets to cause $million is damage; so how identity authentication solutions make decisions on who is truly whom they say they are versus identifying a bad actor impersonating a valid credential can mean the difference between safety and remediating cyber damage.

What Is Inference?

Inference is simply a way of you asking yourself questions to figure something out in order to reach a conclusion, but you are not always able to confirm the results by the end of the situation. 

According to a video from the John Hopkins University course on “Managing Data Analysis” the goals of inferential questions include:

  • Association backed with outcome and key predictor while adjusting for confounders
  • Single or small number of key predictor(s)
  • Sensitivity analysis to check associations

It may be better to understand why we need to make inferences?

  1. Inferences help the source algorithm understand things the target wants them to know but does not directly relate to situation. As it relates to AIML authentication, this is the identification of cyber applications and associated hardware.
  2. Inferences help the source algorithm to understand behaviors, what they may have done in the past and what they may do next. As it relates to AIML authentication, this is the cataloging of behavior patterns with those cyber applications and associated hardware.
  3. Inferences help the source algorithm draw logical conclusions about what is happening. As it relates to AIML authentication, this is the determination of whether or not the credential being used is actually the one intended to use it based on previous behaviors.

Inferences alone aren’t an adequate method of determining immutable identity for cyber authentication. An effective solution will also understand how to make predictions.

What Is Prediction?

Predictions are simply a way of you asking yourself what will happen next and are able to confirm your think by the end of the situation.

Going back to the John Hopkins University video, the goals of predictive questions include:

  • Develop a model that best predicts the outcome
  • Use all available information
  • No predictions favored over the others
  • Little focus on mechanism

Ultimately, we make and confirm prediction to better understand the complexity and entirety of the situation. As it relates to identity authentication, it is a way of building up a knowledge base of accurate inferences and learn from inaccurate inferences to better predict immutable identity authentication with less drag (need for further authentication) in the future.

Biobehavioral Authentication

Acceptto’s eGuardian engine continuously creates, and monitors user behavior profiles based on the user interaction with the It’sMe authenticator. This provides both inference and prediction, so every time an activity occurs, actionable intelligence is gathered and used to optimize the user profile. 

eGuardian is capable of autonomously and continually learning new policies and adapting existing ones. While policies can still be manually defined and contribute to the computation, our Biobehavioral AIML approach automatically finds the optimal policy for each transaction. eGuardian leverages a mixture of AI & ML, expert systems and SMEs to classify, detect, and model behavior, and assign real-time risk scores to continuously validate your identity prior to, during and post-authentication.

Check out what Acceptto can do to ensure your employees, partners and customers can authenticate without passwords and still ensure security and privacy registering for a free demo today.


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