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Keeping control: Best practices for AI governance in payroll 

April 14, 2026
Drew Maginn

When you are involved in managing payroll for your organization, the responsibility should never be taken lightly. Employees not only trust that their personal information will be kept secure, they also expect consistency in how and when they’re paid. However, when you start adding AI tools into the mix, the promise of greater efficiency can overshadow the need for AI controls for secure payroll processing. And if an organization ignores governance in favour of a flashy new payroll solution, it may not understand what’s at stake until it’s too late.

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What is AI governance in payroll and why is it so important?

According to IBM, AI governance is made up of the processes, standards and guardrails that help ensure AI systems and tools are safe and ethical, including research, development and application. However, while many organizations have plans to adopt “agentic” AI, referring to systems that take autonomous actions versus just generating content, their controls are severely lacking. In fact, Deloitte’s State of AI in Enterprise report highlights that while 74 per cent of organizations plan to integrate agentic AI tools within two years, only 21 per cent actually have a governance model for managing them. With the significant risks associated with delegating payroll tasks to AI with no guardrails, there are certain areas that simply cannot be ignored, including managing data, understanding pay bias and ensuring security. 

How to maintain data integrity in AI payroll systems

For any AI application, the solution is only as good as the data it uses. For payroll systems, this means ensuring the data is accurate and standardized or the tools can and will make mistakes. While this starts with accurate calculations of take-home pay and deductions, it also includes remaining up-to-date with provincial or federal standards and properly classifying employees under different work arrangements or during leaves of absence. For example, something as simple as categorizing “parental leave” as “personal leave” could unintentionally lead to AI analyzing data and making recommendations, such as employees deserving performance bonuses, that penalize new parents as unreliable or at high risk for absenteeism.      

Preventing bias: Understanding the risks of AI surveillance pay  

Another risk facing many organizations is shifting employee pay away from human interventions, such as manager recommendations, to AI systems using surveillance pay. Surveillance pay entrusts AI systems with using real-time data to direct compensation, including bonuses and incentives. While these recommendations may be useful as a reference tool to inform decisions, they can easily fall into patterns that reinforce unfair pay practices for organizations with a history of such behaviour. For performance-based jobs, it may unintentionally lead to reinforcing problematic behaviour, such as employees being rewarded for producing more units of a particular product, rather than for the actual quality of their work. 

AI payroll security: How to protect your employees from new threats

When you are responsible for managing payroll, you are automatically given access to extremely sensitive employee information. Aside from having the typical safeguards to protect this information (e.g., security software, limited employee access), new risks to employee data require constant oversight. For example, while payroll platforms may be secure, recent threats to employees include payroll pirates who take advantage of platforms without multifactor authentication to log into accounts and change banking information for payroll deposits. Without an up-to-date understanding of these emerging threats, an improperly trained AI tool might not see a trend in employees updating banking information as anything to flag for further investigation.  

What can be done? Best practices for AI governance

Keeping all this in mind, consider the following best practices to ensure that AI assists with your payroll processes only within an established governance framework.

  • Ensure governance documentation exists and is being followed: It would be naïve to assume that your organization has a governance framework in place, even if AI tools have started entering your payroll processes. If the framework does exist, make regular updates that clearly indicate where and how AI is being used and what parameters have been set. If none exists, prioritize its development before moving any further into the AI space. 

 

    • Start simple: While hype may surround large language models or more robust types of AI tools, it’s typically best to start with small defined tasks and monitor over time. For example, you may start by simply asking AI to flag payroll anomalies for further investigation before handing over more complex multi-step tasks. Always treat AI like a new, unproven employee. This means ensuring they can demonstrate accurate, reliable results in one area before moving them on to another.

    • AI use must be clearly explained and understood: Any and all uses of AI must be easily explained within payroll departments, as well as to the rest of the organization. If there isn’t a shared understanding of how and why AI is being used in payroll, employees could easily lose trust that these systems are acting in their best interests. If any employee raises a question that cannot be answered, that’s a clear indication that the system hasn’t been deployed with robust testing and employee training.

  • Audit, audit and audit some more: AI is constantly evolving and payroll professionals who become complacent in auditing its effectiveness put themselves and their colleagues at risk. If you want your processes to be grounded in reliable data, free from bias and secure, always ensure humans are taking the lead when it comes to oversight. 

Even with all the potential issues associated with AI payroll tools, it may be undesirable or unavoidable to keep them fully out of your workflow. While each organization may differ on how much they want to embrace AI, it is irresponsible to use it without committing the appropriate time and resources. For all of AI’s potential in payroll, it simply can’t add value if you aren’t always putting reliability, trust and accountability first.

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