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AI audit for companies: what to review before you keep using a system
An AI audit is an evidence-based review of an AI system you use now or plan to put into production. It helps you decide whether the system answers well, handles data acceptably, meets the rules that apply, and should continue, be fixed, be limited, or be shut down.
Key takeaways
- An AI audit reviews evidence: data flows, configuration, logs, and real answers.
- The useful outcome is 4 choices: continue, fix, limit, or shut down.
- If the system answers customers, employees, or suppliers, use test cases before and after each change.
- When a rule, form, or traditional automation solves the problem, you don’t need AI.
What is an AI audit, and what should it decide?
An AI audit is a structured review of a system and how the company operates it. It should review data, configuration, security, operations, and measured results.
The audit should use production evidence. What data enters the system? What permissions does it have? What does it answer? Who reviews it? What happens when it fails?
This applies to a customer chatbot, an internal document assistant, an agent that calls APIs, or an automation that classifies emails. If there is no measurement, the discussion stays at opinion level.
The buyer usually has three options. Current tool features may cover access, retention, and basic logs. An automation platform works when the flow follows clear rules. Custom development fits RAG, agents, sensitive data, detailed permissions, or several integrations. That is where AI consulting and architecture usually matter.
When do companies need an AI audit?
Not every company needs a large audit. It is worth auditing when AI touches decisions, sensitive data, or work that affects customers, employees, or suppliers.
- An assistant answers questions about policies, contracts, accounts receivable, complaints, quotes, certificates, or invoices. Nobody measures whether its answers are correct.
- The system uses RAG, semantic search, or internal documents. You do not know whether it retrieves the right context before answering. That review is close to auditing a production RAG system.
- Agents query databases, create tickets, send emails, change statuses, or call APIs. The audit should review permissions, limits, and stop mechanisms.
- A vendor delivered a chatbot or automation, but the team stopped trusting it. It answers poorly, invents facts, or fails on common cases. That often becomes an AI project rescue review.
- People use public AI tools with business data, personal data, or confidential documents. The company does not know what gets sent or stored.
- You sell to the European Union, or the system may face transparency, high-risk, or general-purpose model obligations.
- Teams have built AI workflows without technical review. Personal accounts may be handling company information.
What deliverables should you ask for in an AI audit for companies?
Ask for deliverables that help you decide. A long report is weak if it does not show evidence, owners, and the next action.
| Deliverable | What it should include | Decision it supports |
|---|---|---|
| Use inventory | Tools, teams, use cases, and unauthorized uses. | What to audit first. |
| Data map | Sources, fields, documents, retention, and vendor terms. | Which data should stay out. |
| Configuration review | Model, prompts, tools, rules, and recent changes. | Where the failure starts. |
| Evaluation set | Questions, expected answers, bad answers, and edge cases. | Whether quality improved. |
| Control review | Owners, approvals, incidents, logs, and permissions. | Who controls the system. |
| Decision memo | Continue, fix, limit, or stop for each use case. | What to do next. |
A code review can help, but it does not replace an AI audit. Good code can still answer poorly, use the wrong data, or take actions without enough control.
How do you measure whether the system answers well?
A demo with handpicked questions is weak evidence. You need test cases, metrics, and a before-and-after comparison.
- Define the system’s job. Write what the system should and should not do. For document Q&A, OpenAI recommends defining the objective, collecting a dataset, defining metrics, running evaluations, comparing results, and evaluating continuously in its evaluation best practices.
- Build an evaluation set. Use real questions, hard cases, repeated cases, and cases where the system should say “I don’t know.” Useful sources include complaints, accounts receivable emails, quotes, rejected invoices, and searches.
- Measure retrieval and answer quality. In RAG, measure whether the system retrieves the right document and grounds the answer in it. We operated a RAG assistant for 3 years in a loyalty platform with more than 2 million users across 10 countries and more than 99% availability. For metric detail, read how to measure a RAG system.
- Review technical and security risks. Accuracy is not enough. NIST lists accuracy, explainability, privacy, reliability, safety, security, and harmful bias mitigation in its work on AI measurement and evaluation. OWASP includes prompt injection, sensitive information disclosure, supply chain, and data and model poisoning in the OWASP Top 10 for LLMs and Gen AI Apps 2025.
- Compare and decide. End with one decision for each use case: continue, fix and measure again, limit the scope, or shut it down. For generative systems, NIST recommends monitoring generated content for privacy risks and reevaluating adapted models when their domain changes, in its generative AI profile.
- Apply it to a real workflow. Illustrative example, not a client: a Colombian company receives complaints and claims by email. An assistant classifies the message, finds the policy, drafts a response, and leaves it for approval. The audit checks classification, retrieved documents, citations, personal data exposure, and send permissions. The result may be: keep approval, fix the document base, and block weak categories.
What regulation or framework should you review?
This is not legal advice. Use it to align legal, data, security, and technology teams before you decide scope.
| Framework or rule | Who should care | What to review |
|---|---|---|
| EU AI Act | Companies that offer, use, or integrate covered systems. | The European Commission says several obligations start August 2, 2026, with high-risk Annex III systems on December 2, 2027, in its enforcement FAQ. |
| AI Act transparency | Systems that interact with people or create synthetic content. | Article 50 requires notices for AI interaction and certain synthetic outputs, where applicable, in the Article 50 text. |
| AI literacy | Providers and companies that use covered AI systems. | The European Commission says Article 4 entered into application on February 2, 2025, on its AI literacy page. |
| Colombia: personal data | Companies using personal data in AI in Colombia. | SIC Circular 2 of 2024 says Law 1266 of 2008 and Law 1581 of 2012 apply, in the CRC legal compilation. |
| Colombia: accountability | Companies that process personal data with AI. | Circular 2 of 2024 requires suitability, necessity, reasonableness, proportionality, and risk classification, in this Bogotá Mayor’s Office publication. |
| NIST AI RMF | Companies managing AI risk without a specific legal mandate. | NIST AI RMF 1.0 is voluntary, according to NIST. Its core has 4 functions: govern, map, measure, and manage, in the AI RMF Core. |
| ISO/IEC 42001 | Organizations that provide or use AI products or services. | ISO/IEC 42001 defines requirements for an AI management system, according to ISO. |
Colombia’s CONPES 4144 of 2025 created the National Artificial Intelligence Policy and promotes ethical and sustainable AI adoption, according to the CONPES document approved on February 14, 2025.
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Frequently asked questions
What is an AI audit?
It is an evidence-based review of an artificial intelligence system and how the company operates it. It reviews data, configuration, vendors, security, results, human supervision, and compliance. The goal is to decide whether to continue, fix, limit, or stop the system.
Is an AI audit legally required?
It depends on the country, industry, system type, and data involved. Some European Union obligations apply to certain AI uses. In Colombia, personal data use brings data protection and accountability duties. Do not assume the company is exempt without reviewing the use case.
What’s the difference between an AI audit and an AI impact assessment?
The audit reviews system evidence: data, logs, prompts, metrics, controls, and results. The impact assessment focuses on risks, effects on people, rights, data, and mitigation measures. They often work together when AI affects customers, employees, or sensitive decisions.
Does an AI audit require source code access?
Not always. Many systems can be reviewed with documentation, configuration, prompts, logs, test data, vendor terms, permissions, and result samples. Code helps with custom development, complex integration, or agents that take actions. Code review alone does not prove answer quality.
Can an AI audit be fully automated?
Not responsibly. Tests, log analysis, answer comparison, and pattern detection can be automated. The audit still needs human judgment to define risk, interpret errors, talk to teams, review obligations, and decide what level of control each process needs.
Written by
Quarl engineering team
We are an AI engineering team in Medellín. We build and run systems that reach production — automations, assistants and agents — and for three years we ran an assistant for two million users across ten countries.
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