Deep Fake Faces

Can Blockchain Solve Data’s Integrity Problem?

Goodness is hard to measure. More so in the field of Cybersecurity. In the physical world, if you possess something, say a $1 bill, you have it. If you spend it, you don’t have it. If someone steals it, you don’t have it, either. The digital world is quite different. Digital copies are the same as the original – exactly the same. Each replicated copy is at least as original as the original original. “Can you send me a copy?” can only be answered, “No, but I can send you an original.”

You know all that.

A non time-sensitive digital asset that could be infinitely replicated was itself of little value. It could be replicated many times and in theory “spent” many times. But of course, there were no buyers. Enter cryptocurrency, Bitcoin for an obvious example. A Bitcoin aspires to be a digital $1 bill that can neither be double-spent nor infinitely replicated. How do those two miracles occur? Blockchain. 

Data’s Deep Fake Problem

What else can we do with this marvelous technology that allows us to prove in the digital world that if I have something, I really have it, and if I do not have it, I really don’t have it?

First Digital Photo

The first digital image ever created was of Russel Kirsch’s son, Walden, scanned from a photograph in 1957.
(Source: Wikipedia.)

More than 60 years ago, the first digital photograph was created. Businesses missed the implication. Film-based photographs were hard to manipulate; not so digital photographs which can be easily manipulated. The implication is that the integrity of the photographic data on which a business decision was being made had very substantially degraded. And, no one seemed to notice… for a while.

When businesses did notice, they just started to drop photographs from their business processes. Rightly so. The integrity of the data was highly suspect and nowhere near the quality for a serious business decision. Enter blockchain once again. Blockchain enables the data to be “frozen” at the “moment of creation.” The integrity of the data is preserved and actionable business decisions can be made by responsible people.

How do we think about this? What is the right way to analogize what we know? For illustration and conversation, the present authors offer the table below, the Data Integrity Scale, in the hope of making levels of “goodness” contributory to decision support. Availability has metrics – downtime can easily be measured – but, until now, Integrity has not had a firm scale to measure with.

A Scale for Data Integrity

Most current systems are not designed to protect the Integrity of the data from the moment of creation until the point of use. Protect its Confidentiality? Yes. Protect its Availability? Yes, again. The more we depend on data to drive processes of increasing complexity, the more Integrity supplants Confidentiality and Availability as the paramount goal of cybersecurity.

The authors propose a scale to measure data integrity.

The Cyber Integrity Question of 2021

The table attempts to correlate the measures of trustworthiness across the domains of Law, Accounting, and Business. The sort of question that jumps out from the table might be:

Since I require the proof of a person’s identity (credentialing) be above the red bar before I would let him or her act on the company’s data, why should I not also require that data be above the red bar before I allow it to act on other company data?

“Data integrity is the maintenance of, and the assurance of the accuracy and consistency of data over its entire life-cycle, and is a critical aspect to the design, implementation, and usage of any system which stores, processes, or retrieves data.” … It is at times used as a proxy term for data quality.”5

But “quality” without a way to define and measure it, is an ephemeral term. One common definition of quality is “conformance to requirements.” Here, we might require that the Integrity of data be “above the bar” on the Data Integrity Scale. 

A report from Deloitte (PDF) indicates that Data Integrity violations account for over 40 percent of pharmaceutical warning letters issued globally. 

The historical methods of chasing Visibility and Context through Data Governance down a long chain-of-custody/audit trail are now outdated techniques (and not very reliable in any event – too many steps along the way). A registered “record copy” via blockchain technology is a far better solution. Businesses that are assiduously checking for viruses (aka automated tampering), should also ensure the data they actually use for major decisions has Integrity and is not the result of automated or physical tampering. Blockchain technology allows photos, videos, and other data to jump “above the bar.”

Back to the Future

Roll back those 50 years – actually to 1957 – when the world encountered the first digital photograph. A person needed the skills of a professional photographer to fake a photograph. There was a general feeling of “trust” in what was depicted in a photograph. That was then and this is now, but with adroit use of blockchain technology it is once again possible to have “trust” in photographs and videos, and restore Integrity

What can you do with that “trust?” Business decision makers no longer have to deal with information along a previously believed continuum of certitude; “through a glass darkly,”  but rather can see clearly the demarcations where information is useful and not useful.

The rapid digitalization of business processes has caused a greater need for accurate data as there are no longer humans further upstream in the process to keep the low-quality data from infecting the automated business decision process.

Now is the time to align the ordinal scales of jurisprudence and accounting with each other and with like-minded ordinal scales for business processes. We offer a first cut at that necessary advance; we hope that it is sufficient to purpose and self-explanatory, and will allow this advancement in technology to open new markets with innovative products.


  1. Legal

“Beyond a Reasonable Doubt.”  Whitman J. (2005) The Origins of Reasonable Doubt, Yale University Press. 

“Clear and Convincing Proof.” Colorado v. New Mexico, 467 U.S. 310, 467 (1984)

“Preponderance of the evidence.” Leubsdorf J., (2015), The Surprising History of The Preponderance of the Standard of Civil Proof, 67 Fla. L. Rev. 1569 

“Substantial Evidence” Richardson v. Perales, 402 U.S. 389, 401 (1971)

“Probable Cause” United States v. Clark, 638 F.3d 89, 100–05 (2d Cir. 2011)

“Reasonable Suspicion” Terry v. Ohio 392 U.S. 1 (1968)

“Mere Scintilla” Hayes v. Lucky, 33 F. Supp. 2d 987 (N.D. Ala. 1997)

  1. CPA

“In all material respects” Materiality considerations for attestation engagements, AICPA, 2020 

“Reasonable Assurance” Guide to Financial Statement Services: Compilation, Review, and Audit. AICPA. 2015 AU-C 200: Overall Objectives of the Independent Auditor. AICPA. 2015. AU-C 240: Consideration of Fraud in a Financial Statement Audit. AICPA. 2015 

“Substantial Authority” “Realistic possibility “Reasonable basis” “Frivolous or Patently Improper”

Interpretations of Statement on Standards for Tax Services No. 1, Tax Return Positions, AICPA (Effective Jan. 1, 2012, updated April 30, 2018,)

  1. Identities

NIST Special Publication 800-63 Revision 3 June 2017

  1. Photos and Videos

“SOC2” AICPA -Reporting on an Examination of Controls at a Service Organization Relevant to Security, Availability, Processing Integrity, Confidentiality, or Privacy. Updated January 1, 2018

“ISO 270001” is an international standard on how to manage information security. Revised 2013. The International Organization for Standardization is an international standard-setting body composed of representatives from various national standards organizations.

“GDPR” The General Data Protection Regulation is a regulation in EU law on data protection and privacy in the European Union and the European Economic Area. Implementation date: 25 May 2018

  1. Boritz, J. “IS Practitioners’ Views on Core Concepts of Information Integrity”. International Journal of Accounting Information Systems. Elsevier. Archived from the original on 5 October 2011.
  1. Under the spotlight: Data Integrity in life sciences [Internet]. Deloitte LLP. 2017. [Cited: 4 March 2020].