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As AI rises in importance, so too does governance

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1 year ago

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AI governance was a major theme of 2024, and as the technology continues to evolve, oversight and control—as well as ways to demonstrate it to others—will become even more important this year. 

This was the assessment of Danny Manimbo, a principal with Top 50 firm Schellman, who is primarily responsible for leading the firm’s AI and ISO practices. Speaking during the firm’s Schellmancon event today, he said that last year saw the release of a number of AI governance frameworks, including the National Institute of Standards and Technology’s AI Risk Management Framework, the International Standards Organization’s ISO 42001, and Microsoft’s revisions to its Supplier Security and Privacy Assurance Program to account for AI. Meanwhile, actual regulation is also gaining momentum, with Manimbo pointing to the EU’s AI Act, South Korea’s AI Basic Act, and a number of state-level regulations such as California’s recent AI laws. 

“That kind of set the tone for a lot of the inquiries and the interest that we saw, and for the trends on where GRC was going in 2024, maybe not so much immediately in the beginning of the year, because the frameworks were so new, but I think they were boosted by a number of things in the regulatory standpoint,” said Manimbo. 

The other panelist, Lisa Hall, chief information security officer for the trust platform SafeBase, added that, given the pace of AI advances, it is likely that last year’s measures were not the end but just the beginning, especially considering how widely used even the current generation of solutions is. 

“I think it’s only going to increase, and everyone seems to have some type of AI offering,” said Hall. “Regulations and standards will likely become more demanding, and even with the shadow IT capabilities we have now, I worry that we may be underestimating how often AI technologies are actually used by our employees. And also, on the flip side, how can we best leverage these to make our lives easier?”

Manimbo noted that, with this rise in control frameworks and regulation, this year will also see a rise in demand for ways to demonstrate that one is aligned and compliant with them. The ISO 42001 certification, for which Schellman recently became the first ANSI-accredited body allowed to audit and grant certification for compliance with the standard, is one example, but he anticipated other avenues will open this year. “For example, I sit on the [Cloud Security Alliance] AI Control Framework [board], and they are launching a program scheduled for the second half of this year which is going to be very similar to their [Security Trust Assurance and Risk] program for cloud security but specific to AI risk. That’ll be another avenue,” he said. He added that other standard setters, like the AICPA, might also decide to update their frameworks to account for AI risk. 

Such demonstrations are vital for establishing customer trust in a world that is increasingly connected. Hall noted that supply chains have grown much more complex, which has allowed attackers new opportunities to target vendors or third party software providers and compromise multiple downstream organizations at once. In such an environment, establishing trust with a customer is vital, but it can often involve lengthy and tedious audits filled with manual processes. While she has had success with some automation, such as using AI to reduce time on customer questionnaires and automate access controls, there remain many things that still need human intervention. 

“I’ve definitely struggled with that, like where an auditor is asking for data sets, you’re coming back with a sample set, you’re bouncing back and forth from a tool to gather evidence, and it becomes even more complex when you’re dealing with customer audits and you’re talking to more than one auditor, and you can only reuse evidence for so long that evidence goes stale,” she said. “And then a lot of times, auditors have competing platforms and tools that may not integrate with yours. So it’s still a manual process. There’s a ton of back and forth communication there. I’m still copying and pasting, I’m still downloading from here and uploading to here. So I’d love to see this process improve,”  

Manimbo noted AI has also been helping processes like this, noting that AI can itself help bolster an organization’s controls through automating routine processes and reducing dependence on manual processes. 

“On this front, some of the things that have plagued us in the past is the amount of context that we need as professionals to know if something is something that needs to be addressed immediately as part of a control failure that may be detected. And I think AI will help provide that context there… It may not necessarily be [about] what the controls may be, but how efficient are the models in augmenting existing automation to find those failures in a way that we can effectively address those findings in a way that we can again improve on those and so hopefully reducing additional burden on a team members,” he said. 

However, with all these different frameworks coming out, and with current ones being revised to account for AI, professionals may be challenged in keeping up with all the changes. Professionals need to not only know how to apply these frameworks but also how to scale them as time goes on. Hall said that, by maintaining a security-focused mindset and being proactive, so that the organization is more able to respond to change. 

“If we build and buy with security in mind and find ways to leverage automation and AI to enable us to quickly adjust, … we’re just going to be way better off,” said Hall.  “Instead of looking at ‘here’s the strict regulation, here’s what I have to do,’ [it is] kind of this afterthought, by being more proactive and just having these things in mind. .. I think it’s about us having that mindset of: How is the security built in? How can I be accountable and prove that I’m doing what I’m doing? And think about that before the auditors show up and before the regulations show up.”

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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2 days ago

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August 15, 2026

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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1 week ago

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August 9, 2026

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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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2 weeks ago

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August 5, 2026

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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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