EmergingLegal, accounting, audit and tax AI

AI Controls and Audit Specialist

Also called AI Controls & Audit Specialist · SOX for AI · AI Internal Auditor

Designs and tests the financial controls that apply when AI agents prepare or approve entries, and audits what those agents decided. It matters because auditors and regulators will ask the company to show its AI-touched numbers can be trusted.

What they do

Maps finance processes where an AI agent drafts, codes, reconciles or approves entries, then works out where things can go wrong. Writes control objectives, sets approval limits and defines what evidence must be kept. Tests whether the controls work by sampling agent decisions, replaying them and checking the sources behind them. Reviews retrieval set-ups to see whether the agent pulled the right policy or contract. Logs exceptions, agrees fixes with finance and engineering, and explains findings in plain language to audit committees and external auditors.

What a strong hire delivers

Within the first months, a strong hire gives you a clear map of where agents touch financial reporting and a risk-ranked list of gaps. They put in place documented controls for the highest-risk workflows, with test procedures and evidence trails. They run a first round of agent decision testing, close the main findings with engineering, and leave finance leaders able to brief external auditors with confidence.

How you can tell they are good

Ask them to walk through a control they designed or tested and what evidence proved it worked. Good candidates separate model quality from control design. They talk about sampling, reperformance, segregation of duties and traceability. They can explain retrieval and evaluation without jargon. Be wary of anyone who treats the model as a black box they simply trust, or who only offers policy documents with no testing.

Why it is hard to fill

Almost nobody holds this title yet. The work sits inside internal audit, SOX teams, risk or finance transformation, so the right people look like ordinary auditors on paper. Few have also learned how agents and retrieval systems fail. Those who have are scarce and often stay put, because their employers value them. Expect to assess for potential and adjacent experience, not a matching job title.

Where to find them

Look first in internal audit and SOX or ICFR teams at public companies, and in the technology risk and assurance practices of large audit firms. Finance systems and controllership teams that have rolled out automation are another source. Some come from legal or tax technology. The strongest have worked with data science or engineering colleagues and can read evaluation results.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Controls and Audit Specialist

  1. Describe a financial control you designed or tested. How did you prove it operated as intended?

    Strong answers name the risk, the control, the sample and the evidence kept. They show reperformance and clear conclusions. Worry if they describe only policy writing, or cannot say how failure would have been detected.

  2. An AI agent posts journal entries and a human approves them. Where would you worry the control is weak?

    Good answers cover rubber-stamp approvals, volume overwhelming reviewers, missing source evidence and unclear accountability. They suggest thresholds and sampling. Worry if they either ban the agent or accept human sign-off as sufficient.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are using AI agents in parts of our finance process and need someone to make sure we can trust and evidence the results. You will design and test controls around agent-prepared and agent-approved entries, and audit the decisions agents make. You will work with finance, engineering and our external auditors.

What you will do

  • Map finance processes where AI agents prepare, review or approve entries
  • Design controls, approval limits and evidence requirements for those processes
  • Test control operation through sampling, reperformance and decision replay
  • Review how agents retrieve and use policies, contracts and source data
  • Record findings and agree remediation with finance and engineering
  • Support external auditors with documentation and walkthroughs
  • Report risks and progress clearly to senior finance leaders

What you will bring

  • Demonstrated work designing or testing financial controls, such as SOX or ICFR
  • Experience in internal audit, external audit or controllership
  • Working understanding of how language models and retrieval systems fail
  • Ability to design samples and evaluate error rates sensibly
  • Clear written and spoken communication with technical and non-technical audiences
  • Sound judgement on materiality and risk

Nice to have

  • Experience working alongside data science or engineering teams
  • Familiarity with licensing and intellectual property questions in data use
  • Experience supporting an external audit of automated processes

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Retrieval-augmented generationWorkingGround generated responses in retrieved evidence
  • Model evaluationWorkingMeasure capability and failure modes credibly
  • Intellectual property and licensingWorkingAssess rights in training inputs and generated outputs
  • Technical communicationWorkingExplain evidence and limitations to relevant readers

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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