Responsible AI Practices for Auditors

Dear AI Audit and GRC Professionals, AI Security Audit Checklist AI is transforming organizations at record speed, but without the right controls, it can also introduce risks faster than most teams can detect. Through my experience in IT Audit, cybersecurity, and controls testing, I’ve seen how quickly AI can amplify value and how easily it can introduce new, unseen vulnerabilities when governance and security aren’t built in from the start. That’s why I created a practitioner‑grade AI Security Audit Checklist, a comprehensive, testable guide designed to help auditors, security teams, and GRC professionals evaluate AI systems with the same rigor we apply to traditional technology, but with controls tailored to the unique risks of AI. This checklist goes beyond theory. It provides clear audit questions, validation steps, evidence requirements, and references to leading standards like ISO 42001, NIST AI RMF, GDPR, SOC 2, and OWASP for LLMs. If your organization is building, deploying, or relying on AI, this resource will help you strengthen oversight, reduce blind spots, and ensure responsible, secure AI adoption. I'm curious to hear, what’s the biggest AI‑related risk you see emerging in organizations today? ♻️ Download, share, and/or repost this to your teams and other professionals. 👉Follow Nathaniel Alagbe for more. #AISecurity #AIAudit #AIGovernance #CyberVerge #CyberSecurity #ITAudit #RiskManagement #ISO42001 #NISTAI #ModelSecurity #MLOps #ResponsibleAI #GRC

If you’re stepping into AI governance and wondering where to begin, this is your first map. 📘 The Artificial Intelligence Auditing Framework by The The Institute of Internal Auditors Inc. is foundational — and that’s exactly why it works. It doesn’t assume expertise. It doesn’t skip steps. It builds a shared understanding of how auditors can engage with AI in a responsible, structured way. 🛠️ Why is it worth your time? It translates AI risks into familiar audit terms — roles, controls, governance, assurance. It offers basic but comprehensive coverage of the most critical areas: Where and how to start mapping AI use in your organization What questions to ask across departments (especially when no formal AI policy exists) How to build a central AI inventory What to include in an acceptable use policy How to integrate AI into enterprise risk management Where the auditor fits in both advisory and assurance roles What the C-suite and Board need to hear — and how to say it 🔍 Inside the framework you'll find: ✅ Part 1 – Overview Covers AI history, adoption levels, and common architectures Explains key concepts like Reactive vs Limited Memory AI, and why they matter to auditors ✅ Part 2 – Getting Started Practical tools for identifying AI use across your organization Prompts for conversations with IT, legal, data teams, and the C-suite Tips on building AI inventories, classifying risks, and using the Three Lines Model ✅ Part 3 – AI Auditing Framework Outlines roles for Governance, Management, and Internal Audit Breaks down strategy, data governance, cybersecurity, vendor risk, and internal controls Links to COBIT, COSO, ISO 42001, and NIST AI RMF Aligns with assurance goals without overwhelming practitioners ✅ Part 4 – Practitioner’s Guide & Checklist A plain-language checklist to scope, assess, and document AI maturity and gaps Includes concrete action items like reviewing audit logs, inventorying use cases, and evaluating explainability === Did you like this post? Connect or Follow 🎯 Jakub Szarmach Want to see all my posts? Ring that 🔔.

AI is being deployed faster than it is being audited. Many organizations are deploying AI into critical processes without a clear method to evaluate security, risk, and accountability. That gap creates exposure most teams do not see until it becomes a problem. I created an AI Security Audit Checklist to bring structure and clarity to how AI systems are assessed in real environments. It is built from a practitioner’s perspective and designed to help auditors, security leaders, and risk professionals evaluate AI across governance, model development, data security, infrastructure, monitoring, and regulatory expectations. This is not a theoretical framework. It focuses on audit execution. What to review. What evidence to request. What controls matter. How to align with standards such as ISO 42001, GDPR, SOC 2, OWASP for LLMs, and the NIST AI Risk Management Framework. AI introduces risks traditional audits were never designed to address. If your audit approach has not evolved, important risks remain unchecked. I am sharing the checklist to support professionals working to bring trust and assurance into AI adoption. What is the biggest challenge you face when auditing AI systems today? #AISecurity #AIAudit #AI Governance #CyberSecurity #ITAudit #RiskManagement #AICompliance #GRC #ResponsibleAI #DigitalTrust

⚠️AI Auditors Need Professional Standards⚠️ AI has transformed most verticals, including finance, healthcare, hiring, and critical infrastructure, yet AI, more specifically AI algorithm, auditing remains unstructured and inconsistent. Without clear professional standards, AI audits risk being ineffective, biased, or performative compliance exercises. To ensure trustworthy and accountable AI, our community must establish independent, standardized, and enforceable AI audit practices, lest weak audits undermine the field entirely. 🛑The Problem: AI Algorithm Auditing Lacks Standardization AI auditors operate in a fragmented landscape, with varying approaches: 🔸Some focus on bias and fairness, others emphasize security vulnerabilities. 🔸Some use adversarial testing (MITRE #ATLAS), while others rely on documentation reviews. 🔸Conflicts of interest remain a concern, as auditors often lack independence from the AI systems they assess. Without structured audit frameworks, how can regulators, businesses, or the public trust that an AI audit is rigorous and unbiased? ➡️Professional AI Audit Standards Must Include 1. Independence & Ethics in AI Auditing 🔹AI auditors must be independent, like financial auditors, they should have no financial stake in the auditee. 🔹The California AI Auditors’ Registry bars auditors from working for an auditee within 12 months of an audit, a model that might make sense for global adoption. 🔹AI auditors must follow clear ethical codes, ensuring transparency, impartiality, and accountability. 2. A Standardized AI Audit Framework 🔹AI audits must be structured and repeatable, similar to #ISO19011 for management system audits. 🔹A comprehensive AI audit should include: ◽Risk Assessment – Aligned with #ISO42001 Clause 6.1.2 (AI Risk Assessment). ◽Technical Testing – Evaluating robustness, data integrity, and security vulnerabilities. ◽Bias & Explainability Analysis – Ensuring models produce fair and transparent outputs. ◽Governance & Compliance Review – Checking alignment with ISO42001, #NISTAIRMF, and the #EUAIAct. 3. Certification & Accreditation for AI Auditors 🔹Just as CPAs certify financial auditors, AI auditors need formal certification to verify technical expertise, regulatory knowledge, and audit methodology proficiency. 🔹The California AI Auditors’ Registry is a first step, but a global AI Auditor Accreditation Program is recommended for audit consistency and credibility. ➡️ The Risk of Doing Nothing 🚨Without professional standards, AI audits risk being meaningless. 🚨Inconsistent audits will create regulatory confusion and erode public trust in AI. 🚨Without clear independence rules, AI audits could devolve into self-regulated assessments. Governments, industry bodies, and standards organizations must act to formalize AI audit professionalism, before unreliable audits undermine AI governance. A group of us at #IAAA are working to address these concerns…we would love your participation and support.

Your AI can be 100% compliant and still be unsafe. This has happened more than a few times in recent months, and it’s worth surfacing: AI launch meetings treating compliance as the finish line… when it should be the starting point. On paper, the project looked perfect. 🔸 Documentation? Complete. 🔸 Legal sign-offs? Secured. 🔸 Regulatory boxes? All ticked! But here’s the problem, the compliance review never asked: 🔸 How were training datasets sourced and validated? 🔸 Could patients understand how the AI reached its conclusions? 🔸 Who’s accountable when the AI gets it wrong? Here's the thing, Compliance checks boxes, Responsible AI earns trust. 🔹 Compliance is like passing a driving test 🔹 Responsibility is how you drive when no one’s watching 🔹 Compliance protects you from penalties 🔹 Responsibility protects people. With AI tools moving from pilot to frontline faster than policies can catch up, the gap between compliant and responsible is where harm happens. A compliant AI might flag a patient as low-risk, but without transparency, the clinician can’t see it missed a crucial symptom. One missed symptom → delayed care → worse outcomes → mistrust that can last years. Responsible AI starts with three pillars: 🔹 Ethical frameworks: Ground decisions in fairness, accountability, and beneficence, not just legal allowances. 🔹 Transparency: Let clinicians, patients, and regulators see how the AI works, its limits, and its data sources. 🔹 Oversight: Ensure a human is always answerable for AI actions, with mechanisms to detect and correct harm quickly. The real test of AI in healthcare isn’t whether it passes an audit, it’s whether it can earn and sustain trust. If you’re leading AI in healthcare today, this is the question your patients would want you to answer - which are you building? 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡The ongoing series and additional resources are available at www•enabler•xyz 💡Repost if this message resonates with you!

Your AI governance framework looks great on paper. Then the auditor walks in. 𝐀𝐧𝐝 𝐚𝐬𝐤𝐬 𝐟𝐨𝐫 𝐭𝐡𝐢𝐧𝐠𝐬 𝐧𝐨𝐛𝐨𝐝𝐲 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝 𝐟𝐨𝐫. I’ve been on both sides of that table. What follows is my practitioner synthesis - not a universal standard. Your auditors may ask differently. But these patterns come up more often than most companies expect. Here’s what they’re 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 asking for in 2026. ━━━━━━━━━━━━━━━━━━ 𝐑𝐎𝐔𝐍𝐃 𝟏 - 𝐀𝐈 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 Most companies show up with a spreadsheet of tools. Auditors ask: ‘Show me the risk tier classification for each tool.’ ‘Who validated it and when?’ ‘What changed since the last review?’ 𝐀 𝐥𝐢𝐬𝐭 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐨𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 𝐚𝐧𝐝 𝐭𝐢𝐞𝐫 𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥𝐞 𝐢𝐬 𝐧𝐨𝐭 𝐚𝐧 𝐢𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲. ━━━━━━━━━━━━━━━━━━ 𝐑𝐎𝐔𝐍𝐃 𝟐 - 𝐏𝐨𝐥𝐢𝐜𝐢𝐞𝐬 Most companies show up with an AI Use Policy signed off by Legal. Auditors ask: ‘Where is the control that enforces this policy?’ ‘What happens when the policy is breached?’ ‘Show me the last test result.’ 𝐀 𝐩𝐨𝐥𝐢𝐜𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐚 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐢𝐬 𝐚 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐨𝐟 𝐢𝐧𝐭𝐞𝐧𝐭. ━━━━━━━━━━━━━━━━━━ 𝐑𝐎𝐔𝐍𝐃 𝟑 - 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭 Most companies show up with a governance chart. Auditors ask: ‘Which line reviewed this AI output?’ ‘Where is the evidence of that review?’ ‘Is this documented or just someone’s memory?’ 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐭𝐫𝐚𝐢𝐥 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐚𝐧 𝐨𝐫𝐠 𝐜𝐡𝐚𝐫𝐭. ━━━━━━━━━━━━━━━━━━ 𝐑𝐎𝐔𝐍𝐃 𝟒 - 𝐑𝐢𝐬𝐤 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 Most companies show up with an operational risk register. Auditors ask: ‘Where is model drift in this register?’ ‘How is third-party AI dependency assessed?’ ‘What is your control for AI decision bias?’ 𝐈𝐟 𝐲𝐨𝐮𝐫 𝐑𝐂𝐒𝐀 𝐰𝐚𝐬 𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝟐𝟎𝟐𝟑, 𝐭𝐡𝐚𝐭 𝐠𝐚𝐩 𝐢𝐬 𝐧𝐨𝐰 𝐚 𝐟𝐢𝐧𝐝𝐢𝐧𝐠. ━━━━━━━━━━━━━━━━━━ 𝐑𝐎𝐔𝐍𝐃 𝟓 - 𝐄𝐯𝐢𝐝𝐞𝐧𝐜𝐞 Most companies show up with confirmation that AI is monitored. Auditors ask: ‘What does monitored actually mean here?’ ‘How often? By whom? Against what standard?’ ‘Show me the last three monitoring outputs.’ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐞𝐝 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. 𝐈𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐞𝐠𝐢𝐧𝐧𝐢𝐧𝐠 𝐨𝐟 𝐚 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐛𝐞 𝐫𝐞𝐚𝐝𝐲 𝐭𝐨 𝐟𝐢𝐧𝐢𝐬𝐡. ━━━━━━━━━━━━━━━━━━ 𝐀𝐮𝐝𝐢𝐭-𝐫𝐞𝐚𝐝𝐲 𝐢𝐬 𝐚 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐦𝐚𝐝𝐞 𝐥𝐨𝐧𝐠 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞 𝐚𝐮𝐝𝐢𝐭𝐨𝐫 𝐚𝐫𝐫𝐢𝐯𝐞𝐬. ━━━━━━━━━━━━━━━━━━ 📎 Frameworks and series links in the first comment 👇 💾 Save this before your next audit cycle. ♻️ Repost if your team is still confusing monitoring with evidence. 👇 Which round hit closest to home? 𝐓𝐞𝐥𝐥 𝐦𝐞 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫. #AIGovernance #GRC #Compliance #DORA #EUAIAct #InternalAudit #RiskManagement

KPMG is demanding its own auditor passes on AI cost savings: a watershed moment for professional services. This isn't just about pricing. It signals that AI is no longer optional; it's expected - internally and externally. Employees and now clients both demand it. But here's what can't be compromised: trust. In highly regulated industries like audit and finance, mistakes have outsized consequences. 99% accurate isn't good enough; it's still wrong. A single error can destroy credibility, trigger regulatory action, or expose massive liability. This is why AI adoption in these fields requires more than efficiency gains. It demands: 1. Verifiability: every AI output must be traceable to source data 2. Transparency: clear audit trails showing how conclusions were reached 3. Human oversight: AI augments judgment, it doesn't replace accountability The firms that win won't just be the fastest to adopt AI. They'll be the ones who deploy it responsibly, maintaining the trust that's essential to their existence. https://lnkd.in/eCiArkbh #AI #Audit #ProfessionalServices #Trust #Regulation

Introducing ALICE™: A Practical Framework for AI Governance As AI systems transition from experimentation to core business processes, governance, risk, audit, and compliance professionals face the challenge of not just governing AI, but doing so in a practical and repeatable manner. This is why ALICE™ – An AI Governance Framework – was developed. ALICE offers a straightforward, memorable lens for AI Governance professionals to identify risks, evaluate AI models, and establish accountability throughout the AI lifecycle, from design and deployment to ongoing monitoring. ALICE stands for: - Auditability – Can the AI model be traced, tested, and independently verified? - Liability – Is accountability for AI outcomes clearly defined? - Integrity – Are ethics, fairness, security, and data controls embedded? - Confidence – Can stakeholders trust the system’s outputs and reliability? - Explainability – Are decisions understandable, transparent, and defensible? What makes ALICE powerful is its practical alignment with global standards such as the EU AI Act, NIST AI RMF, and ISO 42001, while remaining accessible for boards, practitioners, and delivery teams. For AI Governance professionals, ALICE aids in: - Identifying model, data, and control risks early - Evaluating AI systems using clear governance criteria - Supporting regulatory readiness and audits - Bridging the gap between technical teams and risk stakeholders AI governance does not need to be complex to be effective; it must be clear, defensible, and actionable. Share your thoughts... #AIGovernance #ResponsibleAI #RiskManagement #InternalAudit #AIControls #Compliance #ModelRisk #TrustworthyAI