Just because you think your client data is protected, and just because a company claims it keeps this data private, does not necessarily mean it’s true, according to Randy Johnston, the co-founder and principal at accounting tech consultancy K2.
Speaking during Woodard’s Scaling New Heights conference in Orlando, Johnston said he is very concerned about what is being done to the data people are feeding into AI models lately or, rather, he is concerned that he doesn’t know what is being done with it. People sell user data all the time and no one knows what it’s being used for: training new AI models, making ransomware, spoofing identities, targeted marketing?
“What really got me started on this was last year, the fourth quarter. I was in a holiday mood and I started thinking of the Naughty and Nice list, and I realized: there’s a lot of vendors who sell or give away client data. They do it all the time,” he said.
Papcut design – stock.adobe.com
While he did not name specific companies, he did talk about one of their key tells. He said people should read the terms of service, privacy policy and license agreement of any software they use, which will sometimes tell you whether or not they share data. Some will outright say they do, the confirmation buried deep in the document text. But he warned that the language can be slippery, and so even if a company does not directly mention selling or sharing user data, there are ways it can happen anyway.
“The tricky part is if they have ‘subprocessors.’ They don’t talk about that. So, they get the subprocessor in play, and the subprocessor can sell the data. If you see ‘subprocessor’ in the license agreement, time out, what does it do? … Really watch the subprocessors,” he said.
While privacy might be something that is handled through regulation, he said he is not confident that the U.S. will see any federal level regulation of AI in the near future. Even if the 10-year moratorium on AI regulation currently being discussed in Congress doesn’t pass, there seems to be little appetite right now to actually implement privacy regulations. This is unfortunate because the general public models used by most people, such as ChatGPT, have a very bad track record on privacy. Johnston said that, in ChatGPT’s case in particular, has a “stunning” amount of tracking despite ostensibly boasting an option to not transfer sensitive data.
“You think you are protected, but you’re not,” he said.
In the absence of U.S. federal regulation, Johnston recommended looking at what other jurisdictions have done in this area to protect data and promote ethical AI use cases. He named the EU AI Act as one example, and Canada’s Artificial Intelligence and Data Act as another. The EU’s act in particular, he said, offered good guidance as it defines unacceptable risk, high risk, limited risk and minimal risk applications.
“This is kind of a big deal, because I don’t want to be using anything that is any more than limited or minimal,” he said. “You’ll have to look at your apps and the relationships that define some of that stuff. It gets kind of nutzo,” he said.
For more forceful regulation, he said many states have introduced regulations of their own, and so in the U.S. it will be important to comply with those measures when applicable.
“There’s 14 privacy regulations in force right now in the U.S. and by the time we get to next year we’ll have at least 19, because the states are pulling them off state by state… So, I’d need you to be in compliance with state privacy regulations. I believe we would be better with a national one, but that ain’t happening, so we’ll have state [level regulation] for the moment,” he said.
And this isn’t even considering the other issues with AI models, such as bias and hallucinations (aka “making things up wholesale.”) Not only are these problems still there, Johnston felt they were getting worse as time goes on. However, these are public generative models made for general use. He expressed optimism that the rise of agentic AI (broadly, semi-autonomous bots capable of making decisions and taking action independently) would help mitigate some of these issues. This is because agents will be trained on specific data to perform specific tasks in specific industries, which he felt made the model less likely it would go awry.
“The main reason I want AI running agentically is because we can specialize in our workforce. If you’re still trying to verticalize your practice in the industry, you can have the agents think and act like you do in your industry, which I think is a marvelous step forward. Many of you have accounting backgrounds in one form or another. You’ve learned to think [in terms of] accounting rules: you can set accounting rules and judgments up with agents, which is another big breakthrough as we see it here, you get way better informational trustworthiness here, and you get far quicker innovation,” he said.
As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.
Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.
The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.
However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.
WASHINGTON — In a major escalation of cross-border trade friction, U.S. President Donald Trump has signed executive orders imposing new 50% tariffs on a wide selection of Canadian exports, citing discriminatory practices by Ottawa targeting American auto, dairy, and beverage industries.
The new duties, announced Monday, will take effect in 30 days. They target a broad spectrum of consumer and industrial goods—ranging from wine, liquor, and milk products to commercial cement, furniture, clothing, and hockey equipment.
Untested Legal Mechanism
To enact the sweeping measures, the administration invoked Section 338 of the Tariff Act of 1930—a rarely used legal provision allowing the executive branch to levy additional tariffs of up to 50% on foreign nations deemed to discriminate against U.S. commerce.
White House officials noted that Section 338 addresses trade discrimination rather than national security or economic emergencies. The move comes months after prior global emergency tariffs faced legal challenges in domestic courts, signaling Washington’s pivot toward alternate statutory authorities to maintain import duties.
Senior administration officials briefed reporters that the measure directly responds to Canadian provincial bans on U.S. alcohol, restrictions on American vehicle exports, and import quota disparities affecting U.S. dairy and cheese producers relative to third-party trading partners.
“While the administration continues to secure reciprocal trade agreements globally, Canada retaliated against efforts to protect domestic industry,” U.S. Trade Representative Jamieson Greer stated.
USMCA Impact and Carve-Outs
Significantly, the newly ordered 50% duties will apply to designated items even if they otherwise comply with the United States-Mexico-Canada Agreement (USMCA).
However, the administration confirmed key targeted exemptions:
Energy products (including oil and natural gas)
Potash and critical minerals
Fish and seafood
Goods already governed by sector-specific duties (such as existing steel and aluminum tariffs)
Administration representatives emphasized that the tariffs do not stem from recent disputes concerning drifting Canadian wildfire smoke, noting that policy options regarding environmental spillover remain under separate review.
Canadian Response and Market Reaction
Following the White House announcement, the Canadian dollar experienced a sharp decline against the U.S. dollar, falling approximately 0.4% during evening trading.
Canadian Prime Minister Mark Carney issued a statement emphasizing that Canada’s earlier counter-duties had merely matched previous U.S. trade actions. “Canada stands ready to engage intensively to address outstanding issues with the U.S. to the mutual benefit of our citizens,” Carney stated, pointing to detailed proposals Ottawa submitted to modernize the USMCA framework.
Ontario Premier Doug Ford took a firmer stance, urging a “dollar-for-dollar” reciprocal response if the measures go into effect on August 19.
With a 30-day implementation window before the duties officially lock in, industry associations and trade groups on both sides of the border are calling for urgent bilateral negotiations to avert further supply chain disruption across North America.
The traditional accounting paradigm—defined by periodic monthly closures and post-hoc annual audits—is rapidly giving way to continuous, automated financial oversight. As of July 2026, forward-thinking accounting practices and multinational corporate finance departments are leveraging continuous auditing systems powered by advanced machine learning models. These systems monitor operational transactions in real time, shifting audit methodologies from sample-based post-analysis to absolute, 100% transaction-level verification.
The operational advantages of continuous auditing are transformative. Standard auditing procedures historically relied on statistical sampling, which, despite rigorous methodology, inherently left gaps where anomalies or fraudulent transactions could go undetected for months. Modern continuous auditing platforms integrate directly with enterprise resource planning (ERP) databases, instantly cross-referencing purchase orders, invoices, bank feeds, and tax records. Any deviation from established control parameters or unusual transaction behavior triggers immediate flags for internal audit teams, dramatically reducing detection lag from quarters to seconds.
Beyond fraud prevention, continuous auditing fundamentally alters internal reporting and decision-making. Executive leadership no longer has to wait weeks after the close of a quarter to evaluate precise financial standing; real-time verified ledger data provides an uninterrupted view of operating margins, tax liabilities, and cash flow dynamics. This real-time visibility enables corporate controllers to adjust capital allocation strategies dynamically, mitigating liquidity constraints and capitalizing on emerging commercial opportunities far more efficiently than competitors bound to legacy reporting cycles.
However, implementing continuous auditing requires accounting professionals to acquire new analytical capabilities. The role of the auditor is evolving from manual data reconciliation toward system validation, algorithmic model governance, and strategic risk interpretation. Accounting firms and corporate finance departments must invest in continuous technical education, ensuring that audit staff possess the data engineering skills necessary to design, maintain, and evaluate complex automated compliance systems.