Do I need to be a CPA to take Level 1?

No. Bookkeepers, staff accountants, controllers, tax preparers, enrolled agents, auditors and finance leaders complete the same curriculum. Lessons say which work a licensed CPA, a signing tax return preparer, or the responsible accountant under management owns.

Will the course teach me to let AI prepare returns or financial statements on its own?

No. AI prepares, reconciles, summarises, drafts, flags and checks. Every workflow names the point where a qualified person verifies the output and takes responsibility for it. Signing a return, issuing a report and making a management representation remain human acts.

Which AI tools does the course require?

None in particular. The course is vendor-neutral and never ranks products. Every exercise can be completed with any general-purpose assistant your firm permits for fictional material, and the vendor module teaches you to judge whether a tool is suitable for client data.

Can I use real client or taxpayer data in the exercises?

No. Every exercise uses fictional companies, taxpayers and figures. Putting real client information into an unapproved tool could breach your confidentiality obligations and, for tax return information, the rules under Internal Revenue Code section 7216.

What do I receive when I finish?

An independent educational certificate issued by AI Coalition Network with a public verification page, plus the workbook, templates and your own Accounting AI Implementation Plan. The certificate is not a CPA licence, carries no professional education hours, and does not satisfy any state board of accountancy requirement.

Master Class

Master AI Accountant & CPA Certification · Level 1 · Foundations

AI Foundations for Accounting

Master Class · Level 1 of the Master AI Accountant & CPA Certification

A sixteen-module master class for accountants, tax preparers, auditors and finance leaders: how AI systems work and fail with financial information, what client data may reach them, the professional duties that do not move, and how to verify, supervise, govern and pilot AI in a firm or finance team.

Enroll in Level 1 Full curriculum

Level: 1 of 6
Difficulty: Beginner
Estimated time: 40 hours
Modules: 16
Curriculum: Version 2026.4

About this level

This is the foundation level of the Master AI Accountant & CPA Certification, rebuilt as a master class. It is written for people who do accounting work: staff and senior accountants, bookkeepers, controllers, tax preparers and reviewers, auditors, CPAs in public practice and industry, and the firm administrators and finance leaders who set AI policy. It assumes you know accounting and assumes nothing about artificial intelligence. A learner who completes it should be able to decide, for one real task, whether and how AI may be used, and to show the reasoning.

The first two modules explain what the systems sold as "AI" actually are: rules, machine learning and generative AI, how a language model produces an answer, and the retrieval, tools and agents inside accounting products. They then show where fluent output fails with financial information, including invented and stale citations, arithmetic, tie-out, rounding and sign errors on long ledgers, how to read accuracy studies for what they actually tested, and the twin traps of automation bias and algorithm aversion.

Modules three and four cover the duties that apply before and after a tool is used. Module three treats tax return information under Internal Revenue Code section 7216 and its regulations, the contractor exception and taxpayer consent, the FTC Safeguards Rule and the written security plan, and the AICPA confidentiality rule, ending in a repeatable data decision. Module four covers the AICPA Code duties that bind members, reliance on tools and due diligence in tax practice under Circular 230, competence, supervision and firm procedures, and how to check the rules of your own state board of accountancy.

Modules five to ten apply that grounding to the work itself: a safe research workflow that treats AI as an assistant rather than an authority and verifies every citation against the primary source; a structured prompt that supplies the figures and the authority instead of asking the tool to recall them; drafting client letters, tax memos and workpapers from a verified fact file; reconciliations, accrual support and flux analysis in the close, with management's responsibility kept where it belongs; audit and assurance, keeping PCAOB standards for issuer audits and AICPA standards for non-issuer audits apart; and firm operations and client accounting services.

Modules eleven to fourteen turn to control. They build staged workflows in which a wrong output never becomes a wrong action, rank AI agents from read-only to posting and paying with human approvals and kill switches, evaluate AI vendors on security claims, access, SOC reports, contracts and exit, and write a firm AI policy placed inside the system of quality management, using voluntary frameworks without overstating them.

Module fifteen is a practical lab of four exercises on fictional clients: find the planted errors in an AI-drafted reconciliation and memo, expose a fabricated Code section, review an extraction that obeyed hidden text, and grade a vendor before drafting a policy. Module sixteen is the capstone: an Accounting AI Implementation Plan for one task, with a measured baseline, named owners and review points, vendor evidence, data decisions and a bounded pilot. Statements of authority are labelled throughout as law or rule, professional standard, professional guidance, best practice, emerging practice, or an AI Coalition Network recommendation.

The level ships with a printable Master Class Workbook and templates: a risk matrix, a data classification worksheet, a prompt builder and prompt library, a research verification sheet, an error detection exercise, a workflow design canvas, a vendor due-diligence checklist, a policy builder and the capstone plan template. Each module ends with a knowledge check that explains every answer, and the final examination draws fifty scenario questions at random from a reviewed bank.

Everything here is professional education. It is not tax, accounting or legal advice, it does not replace the professional standards, IRS rules or state board requirements that govern accounting, tax and attest work, and learners must check the rules that apply to their own licence and practice. Completing the level earns an independent educational certificate issued by AI Coalition Network with a public verification page. It is not a CPA licence, carries no professional education hours, and does not satisfy any state board of accountancy requirement.

What you will be able to do

Explain how language models, retrieval systems and agents produce their output, and recognise the failure modes that matter most with financial information.
Detect invented or stale citations and arithmetic, tie-out, rounding and sign errors, and verify every AI-generated figure and authority against the ledger or the primary source before relying on it.
Classify client and company data before it reaches any AI tool, and apply section 7216, the FTC Safeguards Rule and the AICPA confidentiality rule to decide what may be used and where.
Apply the duties of competence, due care, diligence and supervision to AI-assisted work, and check the rules of your own state board of accountancy.
Write structured accounting prompts that supply the figures and authority, pin down the entity and period, and require a tie-out and a named reviewer.
Use AI to support research, client letters, tax memos, workpapers, the close, reconciliations and audit procedures while the responsible professional keeps every conclusion.
Design staged, supervised AI workflows and set approval points, logs and kill switches for agents that could post, pay or send.
Evaluate an AI vendor on security claims, access controls, SOC reports, contract terms and exit, and draft a firm AI policy within the system of quality management.
Plan and measure a bounded AI pilot for one accounting task, and produce an Accounting AI Implementation Plan a firm or finance team could adopt.

Modules and lessons

Module 01

Modern Artificial Intelligence for Accounting Professionals

What the systems sold as "AI" actually are, and why that changes how you check them. This module separates rules, machine learning and generative AI, explains how language models produce an answer, opens up the retrieval, tools and agents inside accounting products, and maps tool categories to COSO's capability types and to what the evidence says about use.

Rules, Machine Learning and Generative AI 35 min
How Large Language Models Produce an Answer 40 min
Retrieval, Tools and Agents Behind the Product 40 min
A Field Guide to Accounting AI Tools and How They Are Used 45 min

Module 02

What AI Can and Cannot Reliably Do With Financial Information

Fluent output can be wrong in ways an accountant must learn to predict. This module covers invented and stale authority, arithmetic, tie-out, rounding and sign errors on long ledgers, how to read accuracy studies for what they actually tested, and the twin traps of automation bias and algorithm aversion.

Confabulated Citations and Stale Authority 40 min
Arithmetic, Tie-Outs and Long Ledgers 45 min
Reading the Evidence on AI Accuracy 40 min
Automation Bias, Algorithm Aversion and the Discipline of Doubt 40 min

Module 03

Client Confidentiality, Tax Return Information and Data Privacy

Before client data goes into any AI tool, you need to know what the data is, which rules protect it and whom they bind. This module covers tax return information under section 7216 and its regulations, the narrow contractor exception and consent, the FTC Safeguards Rule and the written security plan, the AICPA confidentiality rule, and a repeatable data decision.

Tax Return Information and Confidential Client Information 40 min
Where AI Tools Send and Keep Client Data 35 min
Permitted Disclosures, the Contractor Exception and Taxpayer Consent 45 min
The Safeguards Rule, the Written Security Plan and the Data Decision 45 min

Module 04

Professional Responsibility and AI for Accountants

An AI tool changes how accounting work gets done, not who answers for it. This module covers the AICPA Code duties for members, the tax standard on relying on tools, Circular 230 due diligence, competence and firm procedures as the IRS Office of Professional Responsibility applies them to AI, supervision, and how to check your own state board rules.

The AICPA Code Duties That Do Not Move 35 min
Reliance on Tools and Due Diligence in Tax Work 45 min
Competence, Supervision and Firm AI Procedures 40 min
State Boards, the Uniform Accountancy Act and Checking Your Own Rules 35 min

Module 05

AI-Assisted Tax and Accounting Research

AI can help an accountant find the way into a research question, but it is never the authority a conclusion rests on. This module builds a safe research workflow, explains what the GAAP hierarchy treats as authoritative, verifies every citation against the primary source, and records the research path so a reviewer can follow it.

A Safe Research Workflow From Question to Work Product 40 min
Assistant, Not Authority: What Counts as Authoritative 35 min
Verifying Every Citation Against the Primary Source 45 min
Documenting Research Provenance in the Workpapers 35 min

Module 06

Prompt Engineering for Accounting Professionals

A good accounting prompt supplies the figures and the authority instead of asking the tool to recall them, pins down the entity and period, requires a tie-out, and ends with a reviewer. This module builds that structure, checks what may be pasted before prompting, shows how to iterate and when to stop, and gives patterns for everyday drafts.

The Anatomy of a Structured Accounting Prompt 35 min
Supplying Figures and Authority Instead of Asking the Tool to Recall Them 40 min
Checking What You Paste, Then Iterating With Purpose 35 min
Prompt Patterns for Everyday Accounting Drafts 40 min

Module 07

Drafting Client Deliverables, Memos and Workpaper Documentation With AI

AI can produce a fast first draft, but every fact, figure and authority in it must trace to something a person checked. This module drafts letters and memos from a verified fact file, applies the written-advice standard to tax memos, builds workpapers that show who prepared and reviewed what, and keeps a proportionate record of AI use and approval.

Drafting Engagement Letters and Client Letters From a Verified Fact File 40 min
Tax Memos Under the Written-Advice Standard 45 min
Workpapers That Show Who Prepared, Who Reviewed and What Supports Each Conclusion 40 min
Recording AI Use, Validation and the Approval Record 35 min

Module 08

AI in the Close, Reconciliation and Financial Reporting

AI can prepare reconciliations, accrual support, flux analysis and disclosure checklists, but it must stop before anything is posted, approved or certified. This module works through the numbers, sets out management's responsibility and the certification and internal control rules that apply to issuers, and designs controls over AI in the close.

AI-Prepared Bank and Account Reconciliations 40 min
Accrual Support and Flux Analysis With AI 40 min
Management's Responsibility, Officer Certifications and ICFR for Issuers 40 min
Controls Over GenAI in the Close and Segregation of Duties 40 min

Module 09

AI in Audit and Assurance

What audit evidence standards ask of information a tool analyses, and why PCAOB standards for issuer audits and AICPA standards for non-issuer audits must never be mixed. The module covers AI support for risk assessment and document review, investigating items flagged in tests of details, and documenting what a tool did and who concluded.

Audit Evidence Under PCAOB and AICPA Standards When Technology Assists 40 min
AI Support for Audit Risk Assessment and Document Review 40 min
Investigating Items Flagged in Tests of Details 45 min
Documenting What the Audit Tool Did and Who Reached the Conclusion 35 min

Module 10

AI for Firm Operations and Client Accounting Services

Where AI fits in the running of a firm: client intake and scheduling assistants, document collection and extraction, bookkeeping and client accounting services, and internal knowledge search. The module covers what the field evidence does and does not show, informing clients about providers, using return information inside the firm, and ranking use cases by risk.

Client Intake, Scheduling and Client-Facing Assistants 35 min
Document Collection and Data Extraction 40 min
Bookkeeping, Client Accounting Services and What the Field Evidence Shows 40 min
Internal Knowledge Search, In-Firm Use of Return Information and Ranking Use Cases by Risk 45 min

Module 11

Building Repeatable Accounting AI Workflows

A chat that helps once is not a process. This module turns single AI tasks into staged workflows with classified inputs, a bounded AI step, independent checks, a named reviewer and a record. It shows how to stop a wrong output becoming a wrong action, how to design review against automation bias, and how to use the workflow canvas.

From Chat Session to Staged Accounting Workflow 40 min
Designing So a Wrong Output Never Becomes a Wrong Action 40 min
Designing the Review Step Against Automation Bias 35 min
Worked Workflows, the Workflow Canvas, Approvals and Records 45 min

Module 12

AI Agents and Autonomous Systems in Finance and Accounting

An AI agent does more than answer: it calls tools, opens browsers, reads files and sends email. This module explains what agents are, how to rank agent uses from read-only to posting and paying, where human approvals and kill switches belong, why indirect prompt injection stays a residual risk, and how to keep segregation of duties.

What an AI Agent Does in Finance and Accounting Work 35 min
Agent Risk Tiers From Read-Only to Posting and Paying 40 min
Human Control Points, Event Logs and Kill Switches 40 min
Indirect Prompt Injection, Excessive Agency and Segregation of Duties 45 min

Module 13

Cybersecurity, Data Governance and Vendor Evaluation

An AI vendor is a service provider that holds client data, so the firm has to test its claims, control who gets in, read its SOC reports correctly and oversee it by contract. This module covers vendor claims and changing terms, MFA and access, what SOC 1, SOC 2 and SOC 3 reports do and do not show, breach notification and exit.

Testing a Vendor's Security and Privacy Claims 40 min
Identity, Multi-Factor Authentication, Access, Logging and API Keys 40 min
Reading SOC 1, SOC 2 and SOC 3 Reports for AI Vendors 45 min
Overseeing AI Providers: Contracts, Subprocessors, Breach Notification and Exit 45 min

Module 14

Firm AI Governance and Quality Management

A firm needs one written answer to which AI tools may be used, with what data, and who checks the result. This module builds that policy, places AI inside the system of quality management under SQMS 1 and QC 1000, uses voluntary frameworks without overstating them, and runs verification, disclosure, supervision, records, incidents and review.

The Firm AI Policy: Approved Tools, Prohibited Data and Acceptable Use 40 min
AI Inside the System of Quality Management: SQMS 1 and QC 1000 45 min
Voluntary Frameworks: The NIST AI RMF and ISO/IEC 42001 35 min
Running the Policy: Verification, Disclosure, Supervision, Records, Incidents and Review 45 min

Module 15

Practical Accounting AI Lab

Four hands-on labs on fictional clients: find the planted errors in an AI-drafted bank reconciliation and client memo, expose a fabricated Code section and rebuild the weak prompt behind it, review an AI extraction that obeyed text hidden in a document, and grade an AI vendor before writing a firm AI use policy.

Lab: Find the Errors in an AI-Drafted Reconciliation and Memo 45 min
Lab: Find the Fabricated Code Section and Fix the Weak Prompt 45 min
Lab: Review an AI Document Extraction and Design a Secure Workflow 45 min
Lab: Evaluate an AI Vendor and Build a Firm AI Use Policy 45 min

Module 16

Capstone: The Accounting AI Implementation Plan

Write a sixteen-section AI implementation plan for one real accounting task: choose a low-risk first use with a measured baseline, assign named owners and review points, file the vendor evidence and data decisions, then run a bounded pilot with metrics, return-on-investment tracking, stop signals and a review cycle.

Choosing a Low-Risk First Use and Scoping the Plan 45 min
Controls, Owners and Review Points 45 min
Vendor Evidence, Data Decisions and Records 40 min
Pilot, Metrics, Failure Indicators and Improvement 45 min

Assessment

Level 1 Final Comprehensive Examination

Questions: 50
Time limit: 90 minutes
Passing score: 80%
Attempts: 3

Questions are drawn at random from a reviewed bank. Every module also has a knowledge check.

Capstone project

Accounting AI Implementation Plan

An Accounting AI Implementation Plan with all sixteen numbered sections, for one named (fictional or de-identified) organisation and one low-risk first use.
A screening table rating at least three candidate uses on the six screening questions, with the reason the chosen use beats the highest-pain candidate.
A completed risk rating and data classification for the chosen use, with a reason for each dimension and each data class.
A completed workflow design canvas showing inputs, classification, the bounded AI step, verification checks, hold rules, review design and the run record.
A vendor evidence summary drawn from the due-diligence checklist, listing each document filed, who read it and every question still open.
A measurement plan with baselines, paired speed and accuracy metrics, a return-on-investment calculation method, failure indicators with thresholds and named stop authority, and the review cycle.
A disclosure note describing any AI assistance used in preparing the plan and how its output was verified.

Reviewed by a person against a published rubric. Passing score 80%.

Completion and certificate

Complete every published lesson in all sixteen modules.
Pass each module knowledge check with a score of 80 percent or higher.
Pass the Level 1 Final Comprehensive Examination with a score of 80 percent or higher within the allowed attempts.
Submit the capstone Accounting AI Implementation Plan and have it approved by a reviewer against the published rubric.
Acknowledge the academic-integrity statement before each examination attempt and the capstone submission.

On completion you receive the certificate AI Foundations for Accounting (Level 1 of 6) with a unique number, a QR code, and a public verification page. Completing this level alone does not complete the master certification.

Frequently asked questions

Do I need to be a CPA to take Level 1?: No. Bookkeepers, staff accountants, controllers, tax preparers, enrolled agents, auditors and finance leaders complete the same curriculum. Lessons say which work a licensed CPA, a signing tax return preparer, or the responsible accountant under management owns.
Will the course teach me to let AI prepare returns or financial statements on its own?: No. AI prepares, reconciles, summarises, drafts, flags and checks. Every workflow names the point where a qualified person verifies the output and takes responsibility for it. Signing a return, issuing a report and making a management representation remain human acts.
Which AI tools does the course require?: None in particular. The course is vendor-neutral and never ranks products. Every exercise can be completed with any general-purpose assistant your firm permits for fictional material, and the vendor module teaches you to judge whether a tool is suitable for client data.
Can I use real client or taxpayer data in the exercises?: No. Every exercise uses fictional companies, taxpayers and figures. Putting real client information into an unapproved tool could breach your confidentiality obligations and, for tax return information, the rules under Internal Revenue Code section 7216.
What do I receive when I finish?: An independent educational certificate issued by AI Coalition Network with a public verification page, plus the workbook, templates and your own Accounting AI Implementation Plan. The certificate is not a CPA licence, carries no professional education hours, and does not satisfy any state board of accountancy requirement.

Sources consulted

Primary sources that shaped this level. Each is labelled by what kind of authority it is; consult the original for the current text.

AI Coalition Network — Certification Educational Standard: Human Review of AI-Assisted Professional Work AI Coalition Network educational standard · accessed Sep 15, 2026
AICPA & CIMA — SOC 3® - SOC for Service Organizations: Trust Services Criteria for General Use Report Industry guidance · accessed Sep 15, 2026
AICPA & CIMA (Association of International Certified Professional Accountants) — Future-Ready Finance: Technology, Productivity, and Skills Survey (press release: AI Transformation Opens Door for Finance Professionals to Build Future-Ready Skills) Recommended practice · published Dec 17, 2025 · accessed Sep 15, 2026
AICPA Assurance Services Executive Committee — 2017 Trust Services Criteria (With Revised Points of Focus — 2022), TSP Section 100 Standard or framework · published Oct 1, 2022 · accessed Sep 15, 2026
AICPA Auditing Standards Board — Statement on Auditing Standards No. 142, Audit Evidence (AU-C Section 500) Standard or framework · published Jul 1, 2020 · accessed Sep 15, 2026
AICPA Auditing Standards Board — Statement on Quality Management Standards No. 1, A Firm's System of Quality Management Standard or framework · published Jun 1, 2022 · accessed Sep 15, 2026
AICPA Auditing Standards Board — Statement on Auditing Standards No. 145, Understanding the Entity and Its Environment and Assessing the Risks of Material Misstatement Standard or framework · published Oct 1, 2021 · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA) — AICPA Code of Professional Conduct Professional rule · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA) — AICPA Code of Professional Conduct — Confidential Client Information Rule (1.700.001) and Third-Party Service Provider interpretations (1.150.040, 1.300.040, 1.700.040) Professional rule · published Jul 1, 2026 · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA) — SOC 1 - SOC for Service Organizations: ICFR Industry guidance · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA) — TQA Section 9561 SOC Examinations: Effect of the Service Organization's Use of AI on SOC 1 and SOC 2 Examinations Industry guidance · published Sep 11, 2026 · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA) and National Association of State Boards of Accountancy (NASBA) — Uniform Accountancy Act: Standards for Regulation, Ninth Edition Standard or framework · published Jul 1, 2025 · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA), Professional Ethics Executive Committee — AICPA Code of Professional Conduct: Competence (1.300.010) and Supervision of a Specialist on Consulting Engagements (1.300.020) Professional rule · published Mar 1, 2025 · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA), Professional Ethics Executive Committee — AICPA Code of Professional Conduct: Use of a Third-Party Service Provider (1.150.040, 1.300.040) and Disclosing Information to a Third-Party Service Provider (1.700.040) Professional rule · published Mar 1, 2025 · accessed Sep 15, 2026
American Institute of Certified Public Accountants (AICPA), Tax Executive Committee — Statement on Standards for Tax Services No. 1, section 1.4, Reliance on Tools Professional rule · published Nov 13, 2023 · accessed Sep 15, 2026
Benjamin P. Commerford, Sean A. Dennis, Jennifer R. Joe and Jenny W. Ulla; Journal of Accounting Research — Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence Recommended practice · published Dec 1, 2021 · accessed Sep 15, 2026
California Legislature (California Legislative Information) — California Civil Code § 1798.82 — Data breach notification Legal requirement · accessed Sep 15, 2026
Code of Federal Regulations (via Legal Information Institute, Cornell Law School) — 26 CFR § 301.7216-2 — Permissible disclosures or uses without consent of the taxpayer Regulation · accessed Sep 15, 2026
Committee of Sponsoring Organizations of the Treadway Commission (COSO) — Internal Control — Integrated Framework (2013) Standard or framework · accessed Sep 15, 2026
Committee of Sponsoring Organizations of the Treadway Commission (COSO) — Achieving Effective Internal Control Over Generative AI (GenAI) Industry guidance · published Feb 23, 2026 · accessed Sep 15, 2026
Cybersecurity and Infrastructure Security Agency — Require Multifactor Authentication (Secure Our World) Industry guidance · accessed Sep 15, 2026
Cybersecurity and Infrastructure Security Agency (CISA), Australian Signals Directorate's Australian Cyber Security Centre, and U.S. and international partners — Careful Adoption of Agentic Artificial Intelligence (AI) Services Industry guidance · published May 1, 2026 · accessed Sep 15, 2026
David A. Wood and several hundred co-authors, Issues in Accounting Education (American Accounting Association) — The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions? Recommended practice · published Oct 26, 2023 · accessed Sep 15, 2026
Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani; Organization Science — Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality Recommended practice · published Mar 1, 2026 · accessed Sep 15, 2026
Federal Trade Commission — FTC Safeguards Rule: What Your Business Needs to Know (16 CFR Part 314) Industry guidance · accessed Sep 15, 2026
Federal Trade Commission (Electronic Code of Federal Regulations) — 16 CFR Part 314 — Standards for Safeguarding Customer Information Regulation · published Dec 9, 2021 · accessed Sep 15, 2026
Federal Trade Commission, Office of Technology (staff) — AI Companies: Uphold Your Privacy and Confidentiality Commitments Industry guidance · published Jan 9, 2024 · accessed Sep 15, 2026
Financial Accounting Standards Board (FASB) — Statement of Financial Accounting Standards No. 168, The FASB Accounting Standards Codification and the Hierarchy of Generally Accepted Accounting Principles Standard or framework · published Jun 1, 2009 · accessed Sep 15, 2026
Internal Revenue Service — Publication 4557 (Rev. 6-2024), Safeguarding Taxpayer Data: A Guide for Your Business Industry guidance · published Jun 1, 2024 · accessed Sep 15, 2026
Internal Revenue Service — Publication 5708 (Rev. 8-2024), Creating a Written Information Security Plan for your Tax & Accounting Practice Industry guidance · published Aug 1, 2024 · accessed Sep 15, 2026
Internal Revenue Service — Dirty Dozen tax scams for 2026: IRS reminds taxpayers to watch out for dangerous threats (IR-2026-30) Industry guidance · published Mar 5, 2026 · accessed Sep 15, 2026
Internal Revenue Service — Rev. Proc. 2013-14 (Internal Revenue Bulletin 2013-3, January 14, 2013) — format and content of taxpayer consents to disclose and use tax return information Industry guidance · published Jan 14, 2013 · accessed Sep 15, 2026
Internal Revenue Service, Office of Professional Responsibility (OPR) — Introductory Guidelines for Responsible AI Use in Federal Tax Practice (Alerts from the Office of Professional Responsibility, Issue Number 2026-19) Industry guidance · published Jun 24, 2026 · accessed Sep 15, 2026
International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC), ISO/IEC JTC 1/SC 42 — ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system Standard or framework · published Dec 18, 2023 · accessed Sep 15, 2026
Journal of Accountancy (AICPA & CIMA) — Accounting ethics in the age of AI Recommended practice · published Aug 5, 2026 · accessed Sep 15, 2026
Jung Ho Choi (Stanford Graduate School of Business) and Chloe L. Xie (MIT Sloan School of Management), Journal of Accounting Research — Human + AI in Accounting: Early Evidence from the Field Recommended practice · published Apr 16, 2026 · accessed Sep 15, 2026
Kate Goddard, Abdul Roudsari and Jeremy C. Wyatt; Journal of the American Medical Informatics Association — Automation bias: a systematic review of frequency, effect mediators, and mitigators Recommended practice · published Jan 1, 2012 · accessed Sep 15, 2026
Marc Eulerich, Aida Sanatizadeh, Hamid Vakilzadeh and David A. Wood, Review of Accounting Studies (Springer) — Is it all hype? ChatGPT's performance and disruptive potential in the accounting and auditing industries Recommended practice · published Jun 27, 2024 · accessed Sep 15, 2026
Michael R. Bock, Kara Molisee, Zachary Ozer and Sumit Shah (Column Tax) — TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task Recommended practice · published Jul 22, 2025 · accessed Sep 15, 2026
National Cyber Security Centre (UK) — Prompt injection is not SQL injection (it may be worse) Industry guidance · published Dec 8, 2025 · accessed Sep 15, 2026
National Institute of Standards and Technology — Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 Standard or framework · published Jan 26, 2023 · accessed Sep 15, 2026
National Institute of Standards and Technology — Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1 Standard or framework · published Jul 26, 2024 · accessed Sep 15, 2026
National Institute of Standards and Technology — Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2 E2025) Standard or framework · accessed Sep 15, 2026
OWASP Foundation, OWASP GenAI Security Project — OWASP Top 10 for Agentic Applications for 2026 Standard or framework · published Dec 9, 2025 · accessed Sep 15, 2026
OWASP Foundation, OWASP GenAI Security Project — OWASP Top 10 for LLM Applications 2026 Standard or framework · published Aug 4, 2026 · accessed Sep 15, 2026
Pranab Islam, Anand Kannappan, Douwe Kiela, Rebecca Qian, Nino Scherrer and Bertie Vidgen (Patronus AI; Contextual AI; Stanford University) — FinanceBench: A New Benchmark for Financial Question Answering Recommended practice · published Nov 20, 2023 · accessed Sep 15, 2026
Public Company Accounting Oversight Board — AS 1105: Audit Evidence Standard or framework · accessed Sep 15, 2026
Public Company Accounting Oversight Board — Amendments Related to Aspects of Designing and Performing Audit Procedures that Involve Technology-Assisted Analysis of Information in Electronic Form (PCAOB Release No. 2024-007) Standard or framework · published Jun 12, 2024 · accessed Sep 15, 2026
Public Company Accounting Oversight Board — QC 1000: A Firm's System of Quality Control Standard or framework · published May 13, 2024 · accessed Sep 15, 2026
Public Company Accounting Oversight Board — PCAOB Staff Shares Observations from Outreach on Use of Generative Artificial Intelligence in Audits and Financial Reporting Industry guidance · accessed Sep 15, 2026
Public Company Accounting Oversight Board — AS 1215: Audit Documentation Professional rule · accessed Sep 15, 2026
Public Company Accounting Oversight Board (PCAOB) — AS 2301: The Auditor's Responses to the Risks of Material Misstatement Standard or framework · accessed Sep 15, 2026
Public Company Accounting Oversight Board (PCAOB) — Standard-Setting, Research, and Rulemaking Projects - Data and Technology (research project) Industry guidance · accessed Sep 15, 2026
Public Company Accounting Oversight Board (PCAOB) — Amendments to certain provisions of QC 1000 and related amendments to the QC reporting rule and PCAOB forms (proposed June 9, 2026; adopted September 9, 2026) Standard or framework · published Sep 9, 2026 · accessed Sep 15, 2026
Raja Parasuraman and Dietrich H. Manzey; Human Factors (Human Factors and Ergonomics Society) — Complacency and Bias in Human Use of Automation: An Attentional Integration Recommended practice · published Jun 1, 2010 · accessed Sep 15, 2026
Taxpayer Advocate Service (an independent organization within the IRS) — Is AI Generated Tax Advice Making the Grade? Industry guidance · published Jun 11, 2024 · accessed Sep 15, 2026
Texas Legislature (89th Legislature, Regular Session) — H.B. No. 149, Texas Responsible Artificial Intelligence Governance Act Legal requirement · published Jun 22, 2025 · accessed Sep 15, 2026
Texas State Board of Public Accountancy — AI and the Texas CPA: Professional Obligations in a Changing Environment (Texas State Board Report, Vol. 168, August 2026) Industry guidance · published Aug 1, 2026 · accessed Sep 15, 2026
Texas State Board of Public Accountancy (22 Tex. Admin. Code § 501.74, via Legal Information Institute) — 22 Tex. Admin. Code § 501.74 - Competence Professional rule · published Dec 7, 2011 · accessed Sep 15, 2026
Thomson Reuters Institute — 2026 AI in Professional Services Report Recommended practice · published Feb 9, 2026 · accessed Sep 15, 2026
U.S. Department of the Treasury (Federal Register, via GovInfo) — Regulations Governing Practice Before the Internal Revenue Service — Notice of Proposed Rulemaking, REG-116610-20 Regulation · published Dec 26, 2024 · accessed Sep 15, 2026
U.S. Department of the Treasury / Internal Revenue Service — Treasury Department Circular No. 230 (Rev. 6-2014): Regulations Governing Practice before the Internal Revenue Service, 31 CFR Part 10 Regulation · published Jun 12, 2014 · accessed Sep 15, 2026
U.S. Department of the Treasury / Internal Revenue Service (Electronic Code of Federal Regulations) — 26 CFR § 301.7216-1 — Penalty for disclosure or use of tax return information Regulation · published Jan 7, 2008 · accessed Sep 15, 2026
U.S. Department of the Treasury / Internal Revenue Service (Electronic Code of Federal Regulations) — 26 CFR § 301.7216-3 — Disclosure or use permitted only with the taxpayer's consent Regulation · published Jan 7, 2008 · accessed Sep 15, 2026
U.S. Securities and Exchange Commission — Commission Guidance Regarding Management's Report on Internal Control Over Financial Reporting Under Section 13(a) or 15(d) of the Securities Exchange Act of 1934 (Release Nos. 33-8810; 34-55929) Industry guidance · published Jun 20, 2007 · accessed Sep 15, 2026
U.S. Securities and Exchange Commission (via Legal Information Institute, Cornell Law School) — 17 CFR 240.13a-15 - Issuer's disclosure controls and procedures and internal control over financial reporting Regulation · accessed Sep 15, 2026
United States Code (via Legal Information Institute, Cornell Law School) — 26 U.S.C. § 7216 — Disclosure or use of information by preparers of returns Legal requirement · accessed Sep 15, 2026
United States Code (via Legal Information Institute, Cornell Law School) — 15 U.S.C. § 7262 — Management assessment of internal controls (Sarbanes-Oxley Act section 404) Legal requirement · accessed Sep 15, 2026
United States Congress (via Legal Information Institute, Cornell Law School) — 15 U.S.C. 7241 - Corporate responsibility for financial reports (Sarbanes-Oxley Act of 2002, section 302) Legal requirement · accessed Sep 15, 2026

Who it is for

Staff and senior accountants, bookkeepers and client accounting services staff, controllers and accounting managers in industry, tax preparers and reviewers, enrolled agents, audit associates and internal auditors, CPAs in public practice or industry, and firm administrators and finance leaders who set AI policy. No prior AI experience is required.

Prerequisites

No prior AI experience is required. A working knowledge of bookkeeping, financial statements, tax preparation or audit is assumed. Learners need access to at least one general-purpose AI assistant their firm or employer permits for fictional material; every exercise uses invented companies, taxpayers and figures.

Program roadmap

L1 · AI Foundations for Accounting Available
L2 · Certified AI Accounting Professional Available
L3 · Advanced AI Accounting Operations Available
L4 · Master AI Accountant & CPA Certification Available
L5 · AI Accounting Firm Automation Specialist Available
L6 · Certified AI Accounting Instructor Available

Review cycle

Curriculum version 2026.4. Last reviewed Sep 15, 2026. Next review scheduled Mar 15, 2027.

Independent professional education

MAAC is an independent professional education program offered by AI Coalition Network. It is not a professional license, does not replace licensing or regulatory requirements, and is not endorsed by any regulator, licensing authority, or professional body. No professional education hours or approvals are claimed.