How Should Accounting Firms in New Jersey Prepare Their Technology for AI Tools Without Increasing Risk?

 

Accounting firms in New Jersey should prepare their technology for AI tools with a 6-part framework: define approved use cases, protect sensitive data before it reaches any AI tool, review vendor and model risk, strengthen access and governance controls, require human review of outputs, and document and monitor AI use over time.

For CPA firms, AI readiness is not mainly a software decision. It is a leadership and risk-management decision that affects tax returns, financial statements, personally identifiable information, client trust, and the firm’s ability to use new tools without creating avoidable operational or compliance exposure. NIST’s AI Risk Management Framework and its Generative AI Profile both emphasize structured risk management for AI use, while the FTC has continued warning businesses about privacy, deception, and unfair practices tied to AI claims and deployment.

Why This Question Matters More for Accounting Firms

Many firms approach AI tools as a productivity question first. That is understandable, but it is incomplete.

For an accounting firm, the more important question is whether AI can be used in a way that protects client data, supports professional judgment, and fits real accounting workflows under deadline pressure. CPA firms routinely handle tax returns, financial statements, payroll information, banking data, and other sensitive records. That makes careless AI adoption a different kind of risk than it would be in a generic small business.

IRS guidance for tax professionals requires written safeguards for client data, and IRS AI privacy guidance warns against using sensitive but unclassified data, including personally identifiable information and tax information, to train public AI models. That same IRS guidance also emphasizes purpose limitation, data minimization, documentation, confidentiality, and risk assessment before AI use.

Before bringing AI into the firm, leadership should first review the underlying processes the tool is expected to support. If those processes are inefficient, inconsistent, poorly documented, or weakly controlled, they should be fixed first. AI can increase speed and scale, but it can also multiply errors, weak decisions, and operational problems much faster if the underlying processes are not sound.

The 6 Steps Accounting Firms Should Take Before Using AI Tools

The clearest way to approach AI readiness is through a 6-step operating framework.

1. Define the Specific Business Uses Before Choosing the Tools

The first step is not buying an AI platform. It is deciding exactly where AI fits and where it does not.

For an accounting firm, useful AI use cases may include:

• internal drafting of non-client-facing summaries
• research support for general topics
• workflow assistance
• meeting-note organization
• internal knowledge search
• administrative automation

That is different from allowing uncontrolled use of AI in tax analysis, client communications, financial interpretations, or document handling without boundaries. NIST’s Generative AI Profile emphasizes that organizations should identify intended uses, risks, and controls before deployment rather than treating generative AI like ordinary software.

For CPA firms, the key leadership question is simple: where would AI create real efficiency, and where would it introduce too much risk or too much uncertainty?

2. Protect Sensitive Client Data Before It Reaches Any AI Tool

This is the most important starting point.

Before any AI tool is used, the firm should decide what information must never be entered into an AI system. As a practical rule, accounting firms should not use free or public AI tools for firm or client-related work, because those tools typically do not provide the administrative security, governance, and data-handling controls needed to protect sensitive information. Typically, that should include:

• taxpayer information
• Social Security numbers
• non-public financial statements
• payroll information
• banking details
• client credentials
• confidential business records
• draft client workpapers
• sensitive audit or tax support files

Paid business versions may offer stronger controls, including settings or contractual terms that limit or block the use of customer data for model training. Before adopting any AI tool, an accounting firm should confirm whether those controls exist, whether they can be enforced centrally as a policy for all users, and whether they have been enabled before staff begin using the platform. A paid tool is not automatically safe, but it is generally the minimum starting point for serious evaluation.

IRS AI privacy guidance is explicit that sensitive but unclassified data, including personally identifiable information and tax information, should not be used to train public AI models, and it emphasizes minimizing the collection, use, retention, and disclosure of data in AI systems. It also states that users are responsible for the information they share when using AI.

For an accounting firm, that means AI use should begin with data boundaries, not productivity claims.

3. Review Vendor, Model, and Third-Party Risk Carefully

AI tools should be evaluated as third-party providers, not just as convenient apps.

That means the firm should be clear about:

• what data the tool collects
• whether prompts or files are retained
• whether customer data is used for model training
• where data is stored
• what security controls the vendor provides
• whether access logs and audit trails exist
• what contractual protections are available
• how data can be deleted or exported
• what happens if the firm stops using the tool

When evaluating an AI tool, accounting firms should not stop at whether the platform is free or paid. The more important question is whether the business version provides enforceable controls over data sharing, retention, model training, user access, logging, and administrative policy settings. A paid tool is not automatically safe, but it may provide the governance features a firm needs if those controls are available, properly configured, and applied consistently across all users.

For an accounting firm, AI vendors belong in the same third-party review category as other providers that may touch sensitive client data. Any tool under consideration should be reviewed for how it handles data, what protections it offers, what responsibilities remain with the firm, and whether its controls are strong enough to support the firm’s security and compliance expectations. The FTC Safeguards Rule requires covered firms to take steps to ensure service providers are capable of maintaining appropriate safeguards and to require those safeguards by contract. FTC materials on AI also continue to stress scrutiny around privacy, deception, and fairness.

4. Strengthen Access Controls, Governance, and Approved Usage Rules

A firm should not allow unrestricted AI use simply because a tool is easy to access.

Before rollout, the firm should define:

• which AI tools are approved
• which users or roles may use them
• what tasks are allowed
• what data is prohibited
• whether outputs may be shared externally
• how prompts, uploads, and results are logged or supervised
• who approves new AI use cases
• how AI usage ties back to the firm’s WISP, risk assessment, and security policies

NIST’s AI Risk Management Framework and Generative AI Profile both support that approach by emphasizing governance, mapping of risks, and ongoing management rather than one-time deployment decisions.

A cloud or AI tool that is easy to access but poorly governed can increase risk rather than reduce it.

5. Require Human Review of AI Outputs Before They Affect Client Work

AI outputs should not be treated as final simply because they sound polished.

For accounting firms, human review is especially important because AI can produce incomplete, inaccurate, outdated, or overconfident outputs. That creates obvious risk when the subject matter involves tax interpretation, financial analysis, compliance-sensitive language, or client communications. IRS AI guidance says AI use should include verification and validation of data as much as possible and highlights the need for human review before taking consequential action. It also stresses accuracy, reliability, and regular monitoring.

In practice, that means:

• AI can assist, but not replace professional review
• no client-facing output should be used without review
• no tax or financial conclusion should rely on AI alone
• staff should be trained to treat AI as a draft or support tool, not an authority

For a CPA firm, that is not caution for the sake of caution. It is basic professional discipline.

6. Document, Monitor, and Update the Firm’s AI Use Over Time

AI readiness is not finished once the tool is turned on.

The firm should document:

• approved AI tools
• approved use cases
• prohibited data categories
• user responsibilities
• review requirements
• vendor review decisions
• policy changes
• training expectations
• incident or exception handling
• periodic reassessment of whether the tool is still appropriate

The IRS emphasizes written documentation, risk assessment, confidentiality, and purpose limitation in AI use. NIST treats AI risk management as an ongoing process, not a one-time selection exercise. CISA’s AI guidance also emphasizes data security and integrity throughout the AI lifecycle, including the protection of data used to train and operate AI systems.

For an accounting firm, the practical standard should be straightforward: if the firm cannot explain how an AI tool is being governed, it is not ready to rely on it.

What Accounting Firm Leadership Should Review Before Allowing AI Use

Before approving AI tools, leadership should want clear answers to questions such as:

• What specific business problem is this tool solving?
• What client or firm data would users be tempted to enter?
• Is that data allowed to be used in this tool?
• What are the vendor’s data-retention and training practices?
• Who reviews outputs before they affect client work?
• Does the tool fit the firm’s WISP, risk assessment, and incident response discipline?
• Could the firm explain its AI use clearly to a client, insurer, regulator, or internal leadership team?

This should not be treated as a software experiment managed only at the user level. It is a governance decision that belongs at the leadership level because the consequences are operational, reputational, and security-related.

Why “Ready-Fire-Aim” AI Adoption Usually Fails in CPA Firms

The biggest mistake is adopting AI tools before the firm has clear process boundaries.

That usually shows up in familiar ways:

• staff using public AI tools informally
• sensitive data being pasted into prompts
• no documented approved-use policy
• no vendor review
• no review requirement for outputs
• no link back to existing security documentation
• no plan for how AI use would be monitored or restricted

This kind of rushed adoption often creates hidden friction and risk rather than real strategic improvement. FTC guidance reinforces that caution from another angle. The agency continues to scrutinize AI-related claims and practices where privacy, deception, or unfairness may be involved.

Real-World Perspective from Inside a Regional Accounting Firm

Total Cover IT Founder David Quick spent 17 years as the internal IT Director for a mid-sized regional accounting firm in New Jersey, supporting the firm as it grew from approximately 50 employees to more than 80.

During that time, David was responsible for:

• Designing, implementing, and maintaining the firm’s entire IT infrastructure
• Supporting specialized practice management and time and billing systems, workflow management tools, and various accounting, audit, and tax-related applications
• Minimizing downtime, especially during peak tax seasons
• Leading a full headquarters office relocation, including the migration and reassembly of core IT infrastructure, with minimal disruption

That perspective matters because AI use in a CPA firm is not just about new software. It is about whether new tools fit real accounting workflows, support professional judgment, protect client data, and hold up under real deadline pressure.

FAQ

Should accounting firms use free or public AI tools for client-related work?

No. As a practical rule, accounting firms should not use free or public AI tools for firm or client-related work because those tools typically do not provide the administrative security, governance, and data-handling controls needed to protect sensitive information.

What should a CPA firm review before approving an AI platform?

Leadership should review approved use cases, prohibited data, vendor data-retention and training practices, access controls, governance rules, review requirements, and whether the tool fits the firm’s existing security and documentation framework.

Can AI be used for tax analysis or client-facing work without human review?

No. AI can assist with drafting and support work, but outputs should not be treated as final. Tax conclusions, financial interpretations, and client-facing content should be reviewed by a qualified human before use.

Why should accounting firms fix process problems before adopting AI?

Because AI can increase speed and scale, but it can also multiply weak processes, inconsistent decisions, and operational errors much faster if the underlying processes are not sound.

Related Resources for Accounting Firms

If you’re evaluating IT support for your accounting firm, these additional resources may help:

• What Is a Written Information Security Plan (WISP) for Accounting Firms — and Why Does It Matter in New Jersey?
• What Security Documentation Should Accounting Firms Maintain for Cyber Insurance?
• How Should Accounting Firms Evaluate Cloud Providers and Private Cloud Options?
• How Should Accounting Firms Prepare for Client Security Questionnaires?

View All Resources for Accounting Firms

This article is part of our Resources for Accounting Firms series covering IT costs, security requirements, compliance expectations, and operational risk. Go to Resources.