Technology & Innovation

Agentic AI in finance: is your SaaS audit-ready and set up for AI governance?

Agentic AI can automate finance workflows, but greater autonomy brings new governance challenges. Learn how to establish the controls, permissions, and audit trails needed to scale AI responsibly while maintaining visibility and accountability.

Published November 2, 202613 min read Share

Agentic AI goes beyond generating answers or recommending next steps. It can plan work, interact with financial systems, and act across finance workflows, opening new opportunities to automate repetitive tasks while raising questions around control and accountability.

For CFOs, controllers, and finance leaders, the questions now go beyond what AI can do to whether its actions can be explained, approved, and traced. This is where establishing governance, permissions, approvals, monitoring, and evidence in financial decision-making becomes key.

Here’s what to consider before scaling autonomous AI across your finance function.

What does audit-ready agentic AI look like in a SaaS finance team?

Audit-ready AI means teams can reconstruct an AI-supported financial action from beginning to end. You should be able to trace what the agent was authorized to do, which data and rules it used, what it recommended or changed, who approved it, and how any exceptions were resolved.

Think of the evidence chain as:

Authorized use case → approved data → agent activity → recommendation or action → human approval → resulting transaction → reconciliation → exception or review

For example, an agent might identify overdue invoices, recommend collection actions, and prepare customer communications. An audit-ready process should show why those invoices were flagged, what criteria drove the recommendation, and whether the resulting action stayed within the agent’s approved authority.

How does agentic AI change finance controls?

Traditional automation generally follows predefined rules, while generative AI typically produces content, analysis, or recommendations for a person to review.

Agentic AI introduces another layer: it can plan, call tools, and trigger actions. That changes the control environment because the system is no longer only producing an output for someone to consider. Depending on its permissions, it may also change data or initiate a workflow.

That means controls need to cover three types:

• Preventive controls: access permissions, action limits, approval thresholds, and segregation of duties.
• Detective controls: monitoring, reconciliations, anomaly detection, and sampled reviews.
• Corrective controls: rollback, incident response, remediation, and changes to the agent or workflow.

The more consequential the action, the stronger the controls should be. And this is becoming increasingly relevant as organizations move from exploring agentic AI to putting it into practice. A 2025 KPMG Canada survey found that 27% of Canadian organizations had already deployed agentic AI, while another 64% were exploring, experimenting with, or piloting the technology.

As adoption moves forward, finance teams need to establish the right control environment before expanding AI agents’ capabilities.

What foundations should be in place before deploying AI agents?

Before deploying an AI agent, finance should establish who owns it, what it can do, what data it can access, and how its activity will be monitored and reviewed. The goal is to define these boundaries before an agent can take action within a financial workflow.

A practical AI governance framework starts with an inventory of AI agents and embedded AI features that can affect finance. That visibility matters as AI use expands beyond formally approved tools. Deloitte Canada found that nearly two-thirds (64%) of Canadian organizations estimate that at least 11% of AI activity occurs outside approved or governed tools.

For each use case, document:

Control field	What to capture
Owner	Named business and technical owner
Purpose	Specific finance problem being addressed
Systems and data	Approved systems, data sources, and data classifications
Permitted actions	What the agent can read, recommend, draft, or change
Risk tier	Level of risk based on potential financial, privacy, security, or operational impact
Approvals	Actions that require human authorization
Testing	Known scenarios, edge cases, and failure modes
Monitoring	Logs, alerts, reconciliations, and review frequency
Evidence	Activity records, approvals, test results, and reconciliations
Response	What happens if the agent produces an unexpected result or fails

Risk-tiering is particularly important because not every AI use case requires the same level of control. For example, an agent that summarizes variance reports may require relatively limited permissions, while one that can prepare journal entries or initiate payments warrants stricter access controls, approval thresholds, and monitoring.

How do data quality and system boundaries affect AI auditability?

An agent cannot produce reliable, auditable results if the underlying data is incomplete, inconsistent, or impossible to trace.

Define approved sources and establish clear boundaries around what the agent can access. Finance should know where inputs came from, how they were transformed, and which version of a system, rule, or workflow was used.

Pay particular attention to:

• Data lineage and source systems
• Reconciliation between source and accounting systems
• Retention requirements
• Read versus write access
• Master-data changes
• Duplicate or conflicting records
• Data shared with third-party AI providers

For personal information, privacy requirements also need to be considered before data is provided to an AI system.

How should finance control access to AI agents?

AI agents should receive only the permissions they need to perform their approved tasks. Role-based access, least privilege, and segregation of duties should apply to agents just as they do to people.

Use dedicated service identities where appropriate rather than shared employee credentials. Review and recertify permissions regularly, and trigger an additional review when an agent’s purpose, connected system, or permitted actions change.

Which actions should require human approval?

Human oversight is an important part of building trust in AI. KPMG research found that 83% of Canadians would be more willing to trust AI when safeguards such as human oversight, accountability, and monitoring are in place.

In finance, that means keeping people accountable for AI-supported actions that could affect financial statements, cash, customers, employees, compliance, or the company’s financial position.

Using the framework below, consider what the agent is doing and the level of oversight it requires.

What the agent does	Example	Human approval?	Recommended control
Makes a recommendation	Flags an unusual transaction for review	Not necessarily	Monitor the agent’s recommendations and review exceptions
Prepares a change	Drafts a journal entry but does not post it	Yes, before the change is made	Finance professional reviews and approves the entry
Makes a financial change	Posts a journal entry that affects the accounts	Yes	Require approval from an authorized finance user before posting
Takes a high-impact action	Initiates a payment or changes banking details	Yes, with additional safeguards	Use segregated approval, transaction limits, and enhanced monitoring

The higher the potential impact, the more safeguards required. Your approval thresholds should reflect your existing financial controls, the size and nature of the transaction, and how difficult it would be to reverse the action.

How can finance preserve segregation of duties with AI agents?

The approval controls above only work if the people and systems involved are kept separate. Segregation of duties means no single person or AI agent should have enough access to complete and approve a consequential financial action autonomously.

For AI agents, finance should separate four responsibilities:

• Configuration: Who can change the agent, rules, or permissions?
• Operation: What actions can the agent perform?
• Approval: Who authorizes consequential actions?
• Review: Who independently evaluates the agent’s activity and exceptions?

For example, an agent may be permitted to prepare a journal entry, but it should not be able to approve or post that entry without the required human authorization. Likewise, the person who configures the agent should not automatically be able to approve the transactions it generates.

This separation should also extend to the agent’s permissions. An agent should not be able to change its own access, modify the approval rules governing its activity, or grant itself additional permissions. Regular access reviews can help confirm that these boundaries remain in place as finance workflows and AI use cases evolve.

What should an AI audit trail capture?

A complete AI audit trail gives finance a way to understand an agent’s action, verify that it was authorized, identify who was accountable, and investigate or reconstruct the outcome if something goes wrong.

At a minimum, the audit trail should capture:

• Who: Agent and user/service identity
• When: Date and time of the activity
• What data: Data sources and relevant inputs
• Which AI: Model, workflow, or prompt version
• Which systems: Tools and systems the agent accessed
• What it produced: Recommendation or output
• What it did: Action taken
• Who approved it: Approval and approver
• What went wrong: Exceptions or overrides
• What changed: Resulting transaction or system change
• What happened afterward: Reconciliation or review outcome

These records create an evidence trail from the agent’s input through to the financial outcome. Logs should also be protected from unauthorized alteration and retained according to applicable policies and requirements.

How should finance test and monitor AI agents?

Testing should continue after an agent goes live. Finance needs ongoing checks to confirm that the agent is producing expected results, staying within its approved boundaries, and responding appropriately when conditions change.

A few controls are particularly useful:

• Known-answer tests: Give the agent a set of scenarios where the expected outcome is already known. This helps confirm that it produces the right result before deployment and after material changes.
• Exception monitoring: Define what counts as an unexpected or out-of-bounds result, such as an unusual transaction or an action outside the agent’s normal pattern. Route these exceptions for review and determine whether the agent needs to be adjusted.
• Drift monitoring: Watch for changes in the agent’s performance over time. Changes in data, systems, workflows, or underlying AI models can affect how an agent behaves, even when its configuration has not changed.
• Sampled human review: Periodically review a sample of the agent’s recommendations or actions, including some that did not trigger an exception. This can help identify issues that automated monitoring may miss.
• Control attestations: Have the appropriate owner periodically confirm that the agent is still operating within its approved purpose, permissions, and controls. Keep the attestation as evidence that the controls were reviewed.
• Reconciliation: Compare AI-supported activity against underlying financial records or source systems to identify discrepancies.
• Change review: Document and test changes to the agent, workflow, model, or permissions before they affect production activity.

These controls give finance several ways to identify errors, investigate unexpected behaviour, and confirm that the agent continues to operate within its approved boundaries.

If an agent begins producing unexpected results, the response should be defined before an incident occurs.

How can finance roll out AI agents safely?

A phased rollout gives finance a way to increase automation gradually, with each stage adding more autonomy only after the necessary controls are working. Bounded autonomy can keep lower-risk tasks within defined limits, while higher-risk actions remain subject to human review or approval.

A staged approach can help finance build confidence before expanding an agent’s permissions:

1. Read-only: The agent accesses approved information but cannot change anything.

2. Recommendation: The agent analyses data and recommends an action for a person to review.

3. Drafted action: The agent prepares a journal entry, reconciliation, communication, or workflow for approval.

4. Bounded execution: The agent can execute defined low-risk actions within approved limits, with monitoring and exception handling.

Approval-by-exception can also be used within these stages, allowing routine work to proceed automatically while directing unusual or higher-risk activity to a person for review.

Only move to the next stage when testing, evidence, monitoring, and controls demonstrate that the current stage is working as intended.

How can SaaS companies build an AI governance framework that scales?

AI governance becomes easier to scale when ownership and evidence requirements are standardized rather than created from scratch for every agent. A simple control matrix can help make those responsibilities clear and tie them into the broader finance control environment.

The example below shows how a finance team could structure its matrix. Owners, review frequencies, and escalation paths should be adjusted based on the agent’s risk level, the workflow involved, and the organization’s existing controls.

Control	Owner	Frequency	Evidence	Escalation
Review agent permissions	Finance systems	Quarterly	Access review	Security/ Finance
Test financial outputs	Controller	Monthly	Test results	Finance leadership
Review exceptions	Process owner	Weekly	Exception log	Controller
Review agent changes	AI/system owner	Per change	Change record	Risk/IT
Reconcile AI-posted activity	Finance	Per close	Reconciliation	Controller

Is your SaaS finance team ready? A practical checklist

With the right governance structure in place, the next step is assessing whether your finance function has the people, processes, and controls needed to support an AI agent safely.

Use this checklist to identify gaps before expanding an agent’s access or autonomy.

People

• Is there a named business owner?
• Is human accountability clearly defined?
• Have affected finance users been trained?

Process

• Is the use case documented and approved?
• Are risk tiers and approval thresholds defined?
• Is there an incident-response process?

Data

• Are approved data sources documented?
• Are data boundaries and retention rules defined?
• Can inputs and outputs be traced?

Technology

• Are role-based permissions and service identities configured?
• Are read/write boundaries enforced?
• Are agent, model, and workflow versions recorded?

Controls

• Are preventive, detective, and corrective controls defined?
• Are segregation-of-duties requirements preserved?
• Are high-risk actions subject to human approval?

Evidence

• Are activity and approval logs retained?
• Are changes documented and tested?
• Can finance reconstruct an AI-supported transaction from input to outcome?

If several boxes remain unchecked, the next step may be strengthening the control environment, rather than increasing the agent’s autonomy.

How can financial management software support governed AI workflows?

As AI takes on more finance work, underlying financial systems play an important role in keeping that work controlled and traceable. Finance teams need visibility into what an agent can access, which actions it can take, and what happens when those actions affect financial records.

For SaaS finance teams exploring agentic AI, the right financial management software can enable automation while helping finance maintain visibility and control. Explore how Sage Ai can help finance teams automate routine work and gain more actionable insight or learn more about SaaS accounting software designed for growing subscription businesses.

Frequently asked questions

What evidence will auditors expect for AI-supported finance workflows?

Finance should be prepared to produce risk assessments, access records, testing results, change history, activity logs, approvals, exception records, and reconciliations. The important question is whether the evidence lets an independent reviewer understand what happened, why it happened, and who was accountable.

How often should finance review and recertify AI-agent permissions?

Higher-risk agents should generally be reviewed more frequently, with event-driven reviews whenever permissions, connected systems, use cases, or risk profiles change.

A quarterly review may be appropriate for some environments, while a higher-risk workflow may require additional monitoring or approval.

How should a SaaS company govern third-party AI agents and embedded AI features?

Treat third-party AI as part of your governance inventory rather than assuming the vendor’s controls are sufficient.

Review the vendor’s security and privacy practices, data-use terms, access model, audit capabilities, change-management practices, and incident-response commitments. Also consider what happens if the service changes materially or needs to be replaced.

What should an AI incident-response plan include for finance?

Define how finance will contain the issue, preserve relevant logs, assess materiality, stop or restrict the agent, reverse transactions where possible, reconcile affected records, and document remediation.

The plan should also identify who has authority to suspend the agent and who must be notified when a financial or privacy incident occurs.

How should finance document model, prompt, or workflow changes that affect financial outputs?

Treat material AI changes like other controlled changes to financial systems. Record the version, what changed, who approved it, testing performed, the effective date, and the rollback approach.

This creates a connection between a change in the AI workflow and any resulting change in financial output.

Which Canadian privacy and AI requirements should SaaS finance leaders monitor?

Requirements vary based on an organization’s activities, location, data, and systems. Federally, organizations subject to PIPEDA should consider accountability, appropriate data collection and use, safeguards, and breach obligations when implementing AI.

The Office of the Privacy Commissioner of Canada’s AI guidance covers areas such as transparency, explainability, privacy safeguards, and privacy by design. Provincial requirements may also apply; for example, Quebec’s Law 25 includes requirements related to automated decisions involving personal information.

Because requirements continue to evolve, finance leaders should consult privacy, legal, and risk specialists to determine which rules apply to their organization.

Ready to bring more control to AI-powered finance?

As AI agents take on more finance work, strong governance can help your team maintain visibility, accountability, and a clear record of every action. Explore Sage Ai to see how Sage can help you move toward more confident, connected finance.

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