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TIGTA faults IRS on data security, cloud security in separate reports

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2 years ago

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The Treasury Inspector General for Tax Administration, in two reports, critiqued the IRS on cybersecurity for both its data warehouse and its cloud infrastructure.

Data warehouse security

One report specifically pertained to the IRS’s Compliance Data Warehouse, effectively a massive data warehouse containing multiple years of federal tax information and personally identifiable information consolidated from multiple sources, internal and external to the IRS. The CDW offers a broad range of databases that research analysts may access through a variety of data analytic tools. This includes things like Individual Master File data, Business Master File data, tax return data, taxpayer contact information, conversations between a taxpayer and an IRS agent, and actions that took place on behalf of the IRS. As one might imagine, the IRS considers it very important for this data to remain secure. This is why it is required to record audit trails in the system’s security documentation for indications of inappropriate or unusual activity. However, TIGTA said the tools used to visualize audit trails associated with the Event ID data field, specifically CDW logins, failed to accurately display the login data field, with the result that the available login data were both incomplete and unreliable. For example, TIGTA found that from March 2023 to July 2023, the repository was not displaying any audit trails that contained CDW login information at all.

TIGTA said this can be attributed to two root causes. First is that, within the CDW Platform Audit Worksheets, the coding script used to identify system logins in the CDW logs was referencing an incorrect file name. Upon recognizing the error, IRS alerted the appropriate cybersecurity officials and continued to collaborate to identify and implement a resolution. Second, when the login information search period is greater than 90 days, the search does not return complete and accurate login information. As of April 3, 2024, the exact cause of this error was still unknown; however, cybersecurity officials are continuing to troubleshoot the issue. The IRS reports that restricting the search to 90 days or fewer helps manage performance and response time, given the sheer volume of CDW log data. The IRS plans to add a note to the audit trail repository to advise about the 90-day limitation and noted that multiple searches for 90 days or fewer may be run.

Further, TIGTA said that while actionable events require timely review to determine if additional escalation or notifications are needed, the Compliance and Audit Monitoring team is not reviewing any of them. A management official stated that CDW’s actionable events are not being reviewed because of a miscommunication between the Compliance and Audit Monitoring team and CDW personnel, and that the team began the review of all required actionable audit events in March 2024. Further, TIGTA said the monitoring that is being done is highly inefficient, as the IRS’s audit trail repository does not permit users to export or download multiple auditable or actionable events at the same time. As a result, the team is restricted to reviewing, analyzing and reporting on singular audit events. 

TIGTA did, however, concede that all 1,173 CDW users as of April 2024 completed each of the four mandatory training courses. However, mandatory training requirements for unpaid hires (academic researchers and student volunteers) were not managed via the Integrated Talent Management system. According to management officials from the IRS’s Human Capital Office, this limitation was due to an integration issue within the agency’s human resources system. While TIGTA found that the current manual process for tracking training requirements for unpaid hires is functional, it does not afford any type of verification that the training was actually completed. 

TIGTA recommended that: 1) the IRS’s chief data and analytics officer ensure the agency’s audit trail repository accurately displays and reports all CDW login information; 2) the chief information officer ensure that all required actionable audit events for the CDW are reviewed; 3) the CIO ensure that automated mechanisms are incorporated into the actionable audit event escalation process; 4) the CIO and chief data and analytics officer ensure that identified vulnerabilities are timely remediated; and 5) the chief data and analytics officer ensure that all CDW servers are included in configuration compliance scans. The IRS agreed with all five recommendations. 

Cloud infrastructure security

TIGTA, in another report, faulted the IRS for its cloud security assessment, approval and monitoring process, saying it was not maintaining appropriate separation of duties for certain roles related to cloud systems, and did not follow guidance meant to prevent conflicts of interest, increasing the risk of erroneous and inappropriate actions.

Specifically, inspectors determined that 35 (70%) of the 50 cloud systems reviewed had the same individuals assigned as either the authorizing official or the AO’s designated representative and system owner. The remaining 15 (30%) of the 50 cloud systems reviewed demonstrated appropriate separation of duty with different individuals assigned as the AO or the AO-designated representative and system owner. 

While the National Institute of Standards and Technology guidelines recommend that organizations ensure there are no conflicts of interest when assigning the same individual to multiple risk management roles, there was no IRS policy statement that specifically prevented the roles from being occupied by the same person. After this issue was brought to management’s attention, IRS officials stated they will review the NIST guidance and work to ensure that updates are made as appropriate to have different individuals occupy these roles. 

TIGTA also noted that the IRS was not preparing summary reports for 11 (22%) of 50 cloud systems every month as required. The Cloud Continuous Monitoring Strategic Operating Plan requires cloud  information system security officers to prepare a monthly summary report for each of their assigned systems and provide it to the system’s AO. Further, summary reports for 45 of the 50 cloud systems identified that the reports were missing required information. Also, 31 of the 45 cloud systems reviewed were missing the trackable Plan of Action and Milestones weakness identification number on the summary report. And security documents were missing approvals or were not properly approved within the Department of the Treasury data repository. Specifically, the repository was missing five (10%) of the 50 cloud systems’ Authorization-to-Operate memorandums. Finally, 15 of 50 cloud systems were missing required  Federal Risk and Authorization Management Program Security Threat Analysis Reports. 

TIGTA recommended that the IRS’s chief information officer ensure that: 1) separation of duty controls reflect guidance and require that all cloud systems have a unique System Owner and Authorizing Official; 2) an Authorization-to-Operate memorandum is approved for the system to remain in production; 3) summary reports are timely created; 4) procedures are updated; 5) management approvals are consistent and documented; and 6) the Cloud Security Assessment and Authorization process is completed annually. The IRS agreed with four recommendations and plans to ensure separation of duty controls reflect guidance; the system obtains authorization; that summary reports are timely created; and that management approvals are documented. The IRS disagreed with two recommendations, stating its weakness summary reporting is sufficient without unique identifiers and that cloud security assessments are completed in accordance with existing procedures.

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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5 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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2 weeks 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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