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AI Audit Data Collection: Structured, Unstructured, ETL, and Scraping
Data is the absolute foundation of any intelligent system, and understanding how that information is gathered, categorized, prepared, and protected is essential for any AI auditor. This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series surveys the techniques and risks involved in audit evidence collection across the AI data lifecycle, from the data formats engineers work with to the pipelines that move them and the threats that try to corrupt them.
What this episode covers
- Data is the fuel of every model — what auditors expect to see in the business case for sourcing and maintaining it.
- Training data and supervised vs. unsupervised learning — the two main approaches to teaching a model.
- Overfitting and underfitting — failure modes auditors must verify developers identified and corrected.
- Testing, validation, and synthetic data — including GANs and VAEs that generate privacy-safe test data.
- Production data and monitoring for drift — the focus once a model is deployed into live environments.
- Structured vs. unstructured data — schemas and data dictionaries on one side, loose files and media on the other.
- ETL pipelines and automated audit agents that extract, transform, and load data end to end.
- Data manipulation threats — bias, data poisoning, and adversarial attacks on live models.
- Scraping vs. APIs — why dynamic websites make scraping fragile compared to formal API access.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the four data sets used in the AI development lifecycle?
AI systems rely on training data to teach the model and discover patterns, validation and testing data to evaluate it against scenarios it has never seen, and production data, which is live information gathered from active environments after deployment. Training and testing data must be kept strictly separate because they serve entirely different purposes.
What is the difference between structured and unstructured data?
Structured data is highly organized in a predefined format, typically housed in a relational database with a schema and a data dictionary describing every element. Unstructured data lacks a predefined format or schema and includes loose text files, office documents, audio, video, images, social media posts, and metadata that do not fit neatly into a traditional database.
What is ETL in the context of an AI audit?
ETL stands for Extract, Transform, and Load. Data is first extracted from its original storage repositories, then transformed by cleaning, standardizing, and scrubbing it so it is usable, and finally loaded into a target system to be queried and tested. Auditors use tools like Audit Command Language, Tableau, and PowerBI, and the entire process can be fully automated with AI.
What are the main data manipulation threats AI auditors face?
The three main threats are bias, where prejudice is consciously or unconsciously introduced through training data selection, data poisoning, where a malicious actor intentionally introduces corrupted data to skew the output, and adversarial attacks, which manipulate the input prompt to deceive a live model into making incorrect decisions or producing harmful output.
Why is web scraping becoming less efficient than using APIs?
Scraping deploys automated scripts to crawl websites and aggregate public information, but modern websites are highly dynamic and constantly changing, so scripts break and need frequent tuning. Using a formal Application Programming Interface is like asking a kitchen for a recipe card and receiving a typed document, whereas scraping is like peering through the window with binoculars, often pulling in messy code that requires manual cleaning.
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Reference: This article is based on concepts discussed in AI Audit Data Collection: Structured, Unstructured, ETL & Scraping.