How Other Experts Refer to “Somewhat You Why”

Unlike most other posts that appear under my name—Bredebot posts are another matter—this post breaks my 2023 rule because most of it was written by Google Gemini, not me.

But I figured that if I wrote a book specifically about SIX factors of authentication…

Four pages from "Proving Humanity: The Six Factors of Identity Verification and Authentication" by John E. Bredehoft, Bredemarket. Click on the image to purchase.
The book.

…it would be nice to know if other experts held similar thoughts.

My prompt to Gemini

Bredemarket has promoted “somewhat you why” as a sixth factor of identity verification / authentication. Has anyone else shared similar observations?

Gemini’s response

Here is the short answer: Yes, industry experts and security frameworks strongly agree with the core observation, though they call it “Authentication Intent,” “Intent Verification,” or “Context-Aware/Risk-Based Authentication” rather than using Bredemarket’s play-on-words.

Shifting Truths. Google Lyria. Public Domain.

John Bredehoft (Bredemarket) coined “somewhat you why” to fit the traditional naming convention (something you know, have, are, do, somewhere you are). His argument is that checking why an action or request is taking place—evaluating intent, transaction risk, or reasonableness—functions as a crucial safeguard, especially in an era of deepfakes and automated AI agents.

While the tongue-in-cheek name is unique to Bredemarket, the concept itself is widely discussed and implemented across the cybersecurity industry under three main paradigms:

1. “Authentication Intent” (NIST Standards)

The National Institute of Standards and Technology (NIST) explicitly addresses intent in NIST SP 800-63B.

  • The Problem: A user might have a valid session token, biometrics, or physical key plugged in, but malware or a session hijack could trigger actions without the user’s conscious decision.
  • The Solution: Systems require an explicit act of intent—like tapping a physical FIDO key, typing a PIN, or responding to a contextual prompt—proving that the user intended to execute that specific action at that moment.

2. Intent Verification for AI & Delegated Actions

With the rise of autonomous AI agents and automated workflows, identity security groups (like the Non-Human Identity/NHI movement) frequently discuss Intent Verification.

  • Conventional authentication proves who logged in, but not whether the user meant to perform this specific action.
  • Intent Verification bridges authentication and authorization by evaluating whether a transaction (e.g., a $35M wire transfer or an API call) aligns with the user’s actual current decision and business policy.

3. Context-Aware and Adaptive Authentication

Identity providers (like Palo Alto Networks, Okta, and Silverfort) build their Adaptive Multi-Factor Authentication (AMFA) around the “why” and “context”:

  • Context Signals: What resource is being accessed? Is this normal business behavior? Does this request make sense given the time, value, and sensitivity?
  • Dynamic Step-Up: If the “why” seems low-risk (e.g., checking internal docs during work hours), access is smooth. If the “why” is high-risk or suspicious (e.g., a wire transfer requested via video call), the system demands step-up verification.

How the Views Compare

While most identity security professionals treat “intent and context” as a governance or risk engine layer wrapping traditional factors rather than a 6th factor itself, everyone agrees with Bredemarket’s baseline premise: verifying who someone is without evaluating what and why they are trying to do it is no longer enough to stop modern fraud.

CITeR and Combating Facial Recognition Demographic Bias

The National Institute of Standards and Technology (NIST) isn’t the only entity that is seeking to combat facial recognition demographic bias. The Center for Identification Technology Research (CITeR) is doing its part.

The Problem

NIST and other entities have documented facial recognition accuracy differences related to skin tone. This is separate from the topic of facial analysis: this relates to facial recognition, or the identification of an individual. (As a note, “Gender Shades” had NOTHING to do with facial recognition.)

It’s fair to summarize that the accuracy of an algorithm depends upon the data used to train the algorithm. For example, if an algorithm is trained entirely on Japanese people, you would expect that it would be very accurate in identifying Japanese, but less accurate in identifying Native Americans or Kenyans.

Many of the most-used facial recognition algorithms are authored by North American/European or Asian companies, and while the good ones seek to employ a broad data set for algorithm training, NIST and other results document clear demographic differences in accuracy.

The Research

The Center for Identification Technology Research (CITeR) is a consortium of universities, government agencies, and private entities. The lead entity in CITeR, Clarkson University, has initiated research on “improving equity in face recognition systems.” Clarkson is using the following methods:

  • Establish a continuous skin color metric that retains accuracy across different image acquisition environments.
  • Develop a statistical approach to measure equity, ensuring FR results fall within a precise margin of error.
  • Employ new FR systems in combination with or instead of existing measures to minimize bias of results.

In this work, Clarkson is cooperating with other entities, such as the International Organization for Standardization (ISO) and the FIDO Alliance.

The final goal is to make facial recognition usable for everyone.

Your problem

Is your identity company and its product marketers also working to reduce demographic bias? How are you telling your story? Bredemarket (the biometric product marketing expert) can help with strategic and tactical solutions for your marketing and writing needs.

Bredemarket services, process, and pricing.

If I can help your firm with analysis, content, or even proposals in this area, talk to me.

PoisonSeed and FIDO Update

Update to my July 21 post “PoisonSeed: Cross-Device Authentication Shouldn’t Allow Authentication on a Fraudster’s Device.” FIDO’s cross-device authentication is NOT inherently insecure.

From Chris Burt at Biometric Update:

“A reported passkey vulnerability has been walked back, and FIDO is recommended as the fix to the vulnerability of “phishable” MFA wreaking havoc on corporate networks around the world.

“The PoisonSeed attack reported by security company Expel earlier this month does not give access to protected assets, if the FIDO Cross-Device Authentication flow is properly implemented.”

Proper implementation and configuration is essential.

PoisonSeed: Cross-Device Authentication Shouldn’t Allow Authentication on a Fraudster’s Device

(Important July 30 update here.)

(Imagen 4)

The FIDO Alliance is one of the chief proponents of the “death of passwords” movement, and is working on delivering secure authentication. But even the most secure authentication method is not 100% secure. Nothing is.

Authentication is a complex undertaking, and the ability to authenticate on a new device is a special challenge. But the FIDO Alliance has addressed this:

“Cross device authentication allows a user to sign in with their device using a QR code. 

“FIDO Cross-Device Authentication (CDA) allows a passkey from one device to be used to sign in on another device. For example, your phone can be linked to your laptop, allowing you to use a passkey from your phone to sign into a service on your laptop.

“CDA is powered by the FIDO Client-to-Authenticator Protocol (CTAP) using “hybrid” transport. CTAP is implemented by authenticators and client platforms, not Relying Parties.”

What could go wrong? Well, according to Expel, plenty:

“After entering their username and password on the phishing site, the user was presented with a QR code…. 

“What happened behind the scenes is the phishing site automatically sent the stolen username and password to the legitimate login portal of the organization, along with a request to utilize the cross-device sign-in feature of FIDO keys. The login portal then displayed a QR code….

“In the case of this attack, the bad actors have entered the correct username and password and requested cross-device sign-in. The login portal displays a QR code, which the phishing site immediately captures and relays back to the user on the fake site. The user scans it with their MFA authenticator, the login portal and the MFA authenticator communicate, and the attackers are in.

“This process—while seemingly complicated—effectively neutralizes any protections that a FIDO key grants, and gives the attackers access to the compromised user’s account, including access to any applications, sensitive documents, and tools such access provides.”

Presumably the FIDO Alliance will address this soon.