Ensuring Data Integrity in AI Systems: Significance and Technologies to Achieve Results

As the adoption of artificial intelligence (AI) and machine learning (ML) grows exponentially across industries, the data these technologies feed on is also under scrutiny. AI provides sharp insights and helps to make reliable decisions only if the data it uses is realistic and free from biases. This is why data integrity needs attention—it ensures the completeness, accuracy, validity, and consistency of the data used by an enterprise.

Why Work on Data Integrity NOW?

In the age of AI-driven operations, unreliable data poses significant risks, distorting product and service design decisions. AI models need high-quality data to understand customer needs and market trends. If the data they learn from is erroneous, organizations will build products that do not meet real-world expectations. The consequences are more severe in a sector like healthcare, where unverified data used by pharma companies can trigger the production of ineffective or life-threatening drugs.

When data integrity does not get attention, marketing teams fail to run effective campaigns. Flawed data misguides AI-driven tools in analyzing customer behavior and preferences, causing a waste of resources on irrelevant advertisements and failure to engage the right audience. Inaccurate data poses a risk while making financial decisions as well. Companies have started using AI systems to assess market conditions and forecast growth. If their data is misleading, they may make investments that result in financial losses or cause them to overestimate the potential of certain ventures, eventually hurting profitability and long-term sustainability.

Along with all other implications, using data that has not passed its integrity test will expose organizations to reputational risks, legal actions, and regulatory penalties, especially as governments introduce stricter laws regarding AI ethics, data privacy, and transparency.

Real-World Examples of Harm Caused by Poor Data Integrity

Let us now assess a few examples of the harm that such information has already caused:

• Equifax's Inaccurate Credit Score Fiasco

Between March 17 and April 6, 2022, Equifax issued inaccurate credit scores for millions of consumers who had applied for loans, mortgages, and credit cards at organizations such as Wells Fargo and JPMorgan Chase. For more than 300,000 individuals, the credit scores were off by at least 20 points — enough to inflate their interest rates or reject applications.

Equifax leaders blamed a coding issue within its legacy on-premises server for the fiasco. Sources further revealed that specific attribute values, such as the "number of inquiries within one month" or the "age of the oldest tradeline," were inaccurate.

The result? Equifax's stock fell by 5%, and the company faced a class-action lawsuit by an applicant who was denied a loan because the company told the lender her credit score was 130 points lower than the benchmark.

• Uber's miscalculation of driver payments

It was found in 2017 that Uber had been miscalculating its commission in New York for 2.5 years, depriving drivers of their rightful earnings. Per the company's terms and conditions, the commission had to be calculated based on net fare, minus sales tax and other fees. However, Uber computed its cut based on gross fare. It implied that for 2.5 years, Uber took 2.6% more from drivers than its actual commission.

Here, while Uber's data might not be "inaccurate", it was undoubtedly invalid to calculate driver payments and its earnings. The team was supposed to maintain data transparency for fair business practices.

The result? Business leaders had to pay back each impacted driver's earnings with 9% interest, and these payments cost the company around $45 million.

• Samsung's Data Entry Error

In April 2018, Samsung Securities (the stock trading arm of Samsung Corporation), inadvertently distributed shares worth around $105 billion to employees — 30X more than the actual number of outstanding shares.

An employee's data entry error caused the blunder. Instead of paying out a dividend of 2.8 billion Won (South Korean currency worth around $2.1 million then), the person used the wrong key, entering "Shares" instead of "Won" into the computer. Consequently, a stock ownership plan issued 2.8 billion shares (valued at $105 billion then) to employees.

It took over 30 minutes for the organization to realize the mistake and stop the employees from selling off the "ghost stock. However, 16 employees had sold shares worth around $187 million in this time frame.

The result? A drop of about 12% in stock prices wiped out nearly $300 million of market value. This was followed by losing major customer relationships and regulatory restrictions on getting new customers for six months.

• Tesla's "Full Self-Driving" System

Tesla has faced controversies due to the use of incomplete data for its full self-driving feature. The data that trained Tesla's AI models was flawed because it considered only the possibility of serious crashes with airbag deployment and did not show how often human drivers have to get in control to avoid a collision. The full self-driving system has led to multiple incidents involving misjudgment of obstacles and road conditions. In April 2024, Tesla's AI-based system also killed a motorcyclist in the Seattle area.

The result? Such issues have raised serious concerns about data completeness in safety-critical applications, resulting in legal scrutiny and safety recalls that cost the company financially and in terms of brand trust.

All the examples mentioned here show how important it is for AI-reliant businesses to focus on data quality and integrity before they deploy cognitive and automation technologies for their operations.

How to Optimize Data Integrity?

Here are some best practices and tools that organizations can implement to improve data integrity:

• Data provenance, secure data pipelines, and lineage tracking

Data provenance verifies authenticity by documenting the origin, transformations, and usage of data, ensuring transparency and accountability. It enables organizations to trace errors to their source and assess data reliability.

Secure pipelines safeguard data throughout its lifecycle, preventing unauthorized access, tampering, or loss during collection, processing, and storage. These measures ensure the integrity and confidentiality of data used in AI models.

Lineage tracking maps data movement and transformations, offering insights into changes over time. This minimizes errors and enhances AI system performance.

• Checksums

A checksum, derived from a dataset's contents, serves as a unique fingerprint for the data. It detects even minor changes, ensuring data integrity during transfer or storage. By recalculating the checksum at a new location, any discrepancies reveal potential tampering or corruption. Matching checksums confirm the data remains intact.

In AI systems, checksums safeguard training data against errors or manipulation, ensuring models are built on reliable, accurate inputs. This enhances the integrity of predictions and boosts overall system trustworthiness. With their computational efficiency and speed, checksums are ideal for instantly verifying large datasets.

• Digital signatures

A digital signature is a cryptographic technique to strengthen data integrity. It generates a unique code from the data (to be authenticated) with a private key that can be decrypted with a corresponding public key. The recipients then know that the private key's owner verified and signed the data. They can also detect if anyone else tampered with the data during transmission.

Users of digital signatures can keep the input data for their AI systems, and subsequent model updates authentic, eliminating the risk of errors, unauthorized changes, and malicious interference.

• Differential privacy

Differential privacy is a rigorous privacy framework that enables data analysis while ensuring that the inclusion or exclusion of a specific individual's data does not significantly affect the outcome. It adds carefully calibrated "noise" to the data or query results, making it impossible to identify individuals even if unauthorized individuals access the dataset. Differential privacy provides privacy protection backed by mathematical guarantees against re-identification.

A step ahead of data anonymization, differential privacy warrants that data analysis or queries do not reveal information about individuals, even in datasets that are not fully anonymized. It helps organizations use data with stronger adherence to regulatory compliance and makes data integrity for AI systems more robust.

The Role of Blockchain

Blockchain's decentralized, transparent, and immutable structure is pivotal in ensuring data integrity and securing transactions. It operates as a distributed ledger, recording each transaction as a 'block' linked to its predecessor, creating a secure, cryptographic 'chain.' Once data is recorded, altering it requires changing all subsequent blocks, making tampering nearly impossible.

By ensuring all participants access the exact version of the ledger, blockchain enhances transparency and accountability while reducing the risk of fraud, data manipulation, or unauthorized access. Consensus mechanisms, such as proof of work or stake, validate transactions, ensuring only legitimate entries are added to the chain.

Blockchain also eliminates intermediaries, reducing transaction risks and potential data breaches. Its robust encryption techniques secure data movement, making it an ideal solution for maintaining data integrity in modern digital ecosystems.

Anomaly Detection to Mitigate Data Integrity Issues

Organizations leveraging digital technologies to uphold data integrity must proactively address anomalies in their datasets. Automated error detection is crucial in identifying and resolving inconsistencies, often caused by human error. These systems detect outliers or unusual patterns using machine learning algorithms that analyze historical data to establish a baseline of normal behavior.

By clustering, classifying, and performing statistical tests, these algorithms flag deviations for further investigation. Identified anomalies are assessed to determine if they stem from errors, data corruption, or malicious activity. This process enables organizations to promptly rectify issues, ensuring their data remains accurate, reliable, and trustworthy.

Strengthening Data Integrity: A Path Forward

Maintaining data integrity is increasingly challenging due to human errors, cyber threats, fragmented data ecosystems, and inconsistent data management practices across departments. As reliance on data grows, organizations must adopt proactive measures, including regular data cleaning routines and implementing zero-trust cybersecurity architectures for data storage.

When combined with encryption and validation techniques discussed in this article, these practices significantly enhance data security, accuracy, and authenticity. By fortifying their data integrity, companies can unlock greater value for all stakeholders while building trust and reliability in their digital operations.

Authored by Anji Maram, Founder and CEO – CriticalRiver