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EU AI Act likely not a hassle for most use cases

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While the EU AI Act—which officially went into force about a year ago—creates a number of new obligations regarding AI, its more stringent mandates apply only to the minority of organizations involved with high-risk use cases, with the rest able to mostly get by with what they already do to comply with the EU’s General Data Protection Regulation. 

This is according to Dr. Rafae Bhatti, chief information officer of Thunes Financial Services and a speaker at the Governance, Risk Management and Control conference in New York, hosted annually by the Institute of Internal Auditors and ISACA (formerly the Information Systems Audit and Control Association.) While navigating the EU AI Act might seem intimidating to people, he said that, for the majority of organizations, the things they’re expected to do are better thought of as extensions of current regulations with which many are already complying. 

“It is not completely a situation where you need to start from scratch. You may already have certain cybersecurity controls, certain data privacy controls, and that is one of the important pieces of guidance that I’d like to share with you so that you can feel a little bit more comfortable about not having to start from scratch as it relates to cybersecurity and data privacy,” he said. 

EU AI Act

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Before anything, he said, understand the scope of the regulations, which vary based on the AI in question as well as the specific use case toward which it is applied. Certain use cases, such as social scoring or real-time surveillance, are outright prohibited. Below that are high-risk use cases that involve industries such as health care, employment, education and other sectors with major societal implications. After this are those who fall in the limited risk category, which includes chatbots and image generators, followed by those deemed minimal risk, such as AI-enabled video games or spam filters. 

Another way to think of these various risk levels and their consequences, said Bhatti, is in terms of “career ending, sleepless nights, committee meetings or PowerPoints.” 

The good news, he said, is that very few organizations are involved in prohibited use cases, and even those involved in high-risk ones will be uncommon considering they’re restricted to specific sectors. 

“If you are a company that is only creating an application which is a game, you are probably not subject to most of the requirements. If you’re just a shopping website and not doing anything to do with employment or health care or education, there is going to be very minimal you’re required to do,” he said. 

Anything of a limited or minimal risk, he said, doesn’t trigger the AI-specific requirements of the EU AI Act, meaning entities should just continue doing what they’re already doing to comply with existing regulatory frameworks, “and if you’re doing it well you should be OK,” said Bhatti, adding that generally, “the only thing you still have to worry about is GDPR principles.” 

If something is considered high-risk, however, not only is it subject to greater GDPR scrutiny—meaning “if previously you were not taking it seriously, now is the time to take it seriously because there will be a requirement for a conformity assessment.” But some of the AI-specific measures also kick in. Part of this is more stringent security requirements, such as controlling for AI-specific attacks such as data poisoning and prompt injections (broadly referred to as ‘adversarial robustness’ controls.) 

Beyond this, those involved in high-risk use cases must also consider fairness and nondiscrimination controls; transparency and explainability controls; accountability and human oversight. What exactly counts within these categories, though, can be a matter of debate, starting with whether the use case is even high risk or not. 

“Is this AI high risk? The lawyer might say, ‘legally yes.’ The engineer might say, ‘technically no.’  They are both at medium risk of losing their careers. This is going to be a back and forth. Just be prepared to have that argument,” he said. 

Then there are the other controls that, themselves, can rest on slippery definitions. For instance, the fairness and nondiscrimination control requirement ostensibly is to mitigate the effect of bias in AI models. But the definition of these things can be tricky. An engineer might ask what exactly is the definition of fairness; a lawyer might answer, “whatever keeps us out of court,” which he conceded was an unhelpful answer, but one that some will likely use. 

Similarly, while explainability might seem like a simple enough concept at first glance, the detail and granularity of these explanations can be a point of contention. Some people may go into exhaustive detail about how their AI works while others might try to say, “It works in mysterious ways.” Such an answer is not necessarily in the spirit of the rule, but some try to use it anyway. However, he said such questions are only required to be addressed by those using high risk use cases. 

Transparency controls will be more common, as they are required for those involved in limited-risk use cases. Generally, he said, people need to know that they’re interacting with AI, such as through a privacy clause that tells users the system uses it to process their data, or even a note in the interface. However one does it, following this regulation needs documentation as well as a conformity assessment. 

The last bucket is accountability. Who is responsible for the AI? He cautioned against taking a cavalier approach to this question. There needs to be real accountability, along with the ability to escalate further up the chain. 

“Your answer shouldn’t be that it leads to a voicemail. A 1-800 number is not going to cut it, an email is not going to cut it,” he said, though noted that only high risk cases require documentation. Still, even if it’s not strictly required, he said it’s a good idea to consider this anyway. 

He stressed that most of the time organizations will only need to account for transparency. This does not, however, mean they should ignore all other controls. While it may not be specifically required to control for fairness and explainability, he said it is likely still a good idea for any organization dealing with AI. 

Bhatti said AI itself can be a valuable tool in complying with the EU AI Act, as it can do things like perform initial risk analyses and gap assessments, as well as monitor and retrieve vast stores of organizational data. However, he cautioned against letting AI agents perform actual remediation steps, as he felt there is still too much risk (noting, for example, how an agent accidentally deleted a company’s entire codebase by accident).

“If someone is trying to convince you that automatic remediation using [AI agents] is happening now, and that you should adopt it, proceed with caution,” he said. He noted that a few years from now “we can get to a point where we have enough confidence with what automatic remediation is doing. But not today.” 

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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