Role of Adaptive Systems in Business Auditing

Most Internal Audit Plans Are Built for a World That No Longer Exists While the business has shifted gears, we are still drafting audit plans like it is business as usual. Static audit universes, predictable rotations, and neatly defined units. All designed to chase yesterday’s risks, not tomorrow’s realities. Here's the hard truth: - If your audit plan still starts with "auditable units" instead of "strategic risks," you are already behind. - If your planning process assumes predictability, it is probably misaligned with the business reality. - If your audits are designed to revisit known problems instead of focusing on what’s emerging, you’re probably delivering assurance, but not value. Strategic risks don’t care about audit cycles. They don’t wait patiently on your heat map. They cross functions, evolve overnight, and often come dressed as opportunities. It’s time we start designing audit strategies for the world we actually operate in. What does a relevant Internal Audit plan look like when the map keeps changing, and the old rules no longer apply? - It is adaptive. It is rooted in forward-looking risk sensing. - It doesn’t just ask, what could go wrong? It asks what’s changing, and are we ready for it? - It embeds agility into planning, not just in execution. To me, the greatest risk is about clinging to a plan that no longer matches the organization’s trajectory. - Maybe the goal of planning is no longer certainty, but resilience. - Not auditing what’s “on the list,” but what’s keeping leadership up at night. - Not rotating across the business, but rotating around its biggest bets. I welcome your thoughts. #InternalAudit #AuditPlanning #RiskManagement #StrategicRisks #internalauditors #theiia

“Do you think automation and AI are a threat to Internal Audit roles?” AI isn’t just transforming tech - it’s reshaping how risk, control and assurance are understood in organizations. For internal auditors, this shift presents both opportunities and new responsibilities. Great power always comes with great responsibilities. AI can meaningfully impact the below areas (it already is) - 1. From Sampling to Full Population testing - Traditionally, we auditors relied on sample-based testing to identify exceptions. But with AI-powered analytics, we can now analyze entire data populations across transactions, helping predict potential risk manifestations in real time rather than retrospectively. What’s the win? Earlier detection of potential control failures, fraud, or process breakdowns. 2. Smarter Audit Planning - AI can process past audit results, historical issues linked to the process, analyse linked risks to scope-in high-risk areas automatically. What’s the win? We can now instrinsically know our “what could go wrongs” and focus our testing resources on those areas, hence making the testing results more impactful. 3. Evolving role of the Auditor - AI won’t replace auditors altogether - but it will certainly redefine their skill sets. Auditors must now blend data literacy, business understanding, critical thinking with effective communication skills to deliver critical insights of the risk and control environment, that are continuously evolving. What’s the win? Instead of playing catch up, Auditors are now using data and analytics to actively interpret AI outputs and even use predictive analyses to help the process owner understand the potential risks that could manifest. 4. New Risks, New Controls - As organizations adopt AI, auditors must also understand Algorithmic bias and model risks, Data governance and explainability and Compliance requirements to be met. What’s the win? Evolving audit function expanding its toolkit beyond traditional frameworks. 5. Collaboration is the Future - Audit, IT, and Ops teams will have to increasingly collaborate - better by choice rather than by force. The win? Cross-functional understanding and judgment on common risk and control situations that impact the overall organisation. But since the basic objectives of a process manager and the auditor are going to be fundamentally different, better to focus energies on possible areas where goals can overlap between business growth and stronger control environment. To summarize - While AI is here to stay, it isn’t here to totally replace internal auditors. It’s here to elevate the function - making it more analytical, forward-looking, and value-driven. What’s your take? 😊 #InternalAudit #RiskManagement #AI #DataAnalytics

Dear AI Auditors, Auditing AI-Driven Decision Systems AI-driven decision systems are no longer experiments. They approve loans, screen job candidates, and flag suspicious transactions. Yet, many organizations still approach auditing these systems with frameworks built for legacy IT. This gap leaves serious risks untested. 📌 Evaluate algorithmic transparency Traditional audits verify system configurations. With AI, the real risk lies in opaque models. Can you trace how an algorithm reached a decision? Auditors must demand documentation of training data, model logic, and explainability features. Without this, bias and unfairness slip through. 📌 Test for ethical and compliance risks Bias is not theoretical. Hiring AI tools have rejected qualified candidates due to skewed data. Financial AI has denied loans unfairly. Audit scope must cover fairness metrics, compliance with EEOC, GDPR, or local regulations, and whether human oversight exists where required. 📌 Assess data governance in the AI lifecycle AI performance depends on the data feeding it. Weak governance around training, labeling, and updating datasets creates systemic risk. Auditors should validate data lineage, quality controls, and whether retraining is monitored to prevent model drift. 📌 Review continuous monitoring of AI outcomes AI does not stay static. Models evolve as data changes. Auditors must verify whether organizations consistently track accuracy, false positives, and adverse outcomes over time. Strong governance requires alerts when models degrade or drift from compliance thresholds. 📌 Translate AI audit findings into business impact Executives do not need technical deep-dives into algorithms. They need clarity on exposure. Could the AI tool expose the company to regulatory fines? Could biased outputs damage brand trust? Translate findings into clear business risks that leaders can act on. AI audits demand a mindset shift. Traditional ITGC and application audit frameworks are not enough. Auditors who adapt quickly will position themselves as strategic advisors in a market where AI accountability is becoming a board-level priority. #AIAudit #ITAudit #GRC #AIethics #RiskManagement #InternalAudit #CyberSecurity #AIgovernance #CyberVerge #CyberYard

🤖 Internal Audit & Artificial Intelligence: A New Era of Assurance The Internal Audit of Artificial Intelligence report by The IIA Spain’s Thought Factory explores how AI is transforming business processes and how internal auditors can provide assurance on AI governance, risks, and controls. 💡 Key Insights: ✅ 📊 AI in Business & Regulatory Landscape – Companies are rapidly integrating AI, while global regulations like the EU AI Act are shaping governance. ✅ ⚖️ Internal Audit’s Role in AI – Auditors must assess AI risks, ensure compliance, and verify model integrity to build trust in AI-driven processes. ✅ 🚀 AI Risk & Control Framework – Leveraging COSO & ISO 31000 to design effective AI governance models and mitigate bias, cybersecurity, and ethical risks. ✅ 🔍 Auditing AI Models – A structured approach to evaluating data integrity, algorithmic transparency, performance monitoring, and human oversight. ✅ 📢 Upskilling Internal Audit Teams – Auditors must develop AI literacy, data analytics skills, and regulatory knowledge to remain relevant in the digital age. 📌 Why It Matters: As AI adoption accelerates, internal auditors must evolve their methodologies to ensure AI systems are trustworthy, ethical, and aligned with business objectives. 🔎 Is your internal audit team ready to audit AI-driven processes? Let’s discuss best practices for AI assurance! 👇 #InternalAudit #ArtificialIntelligence #RiskManagement #AIgovernance #AuditInnovation #AIRegulation #COSO #ISO31000