Auditor trust in automated tools

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

Here's the most common question I get asked in customer calls: "𝗖𝗮𝗻 𝘆𝗼𝘂 𝘀𝗵𝗼𝘄 𝗺𝗲 𝗵𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀?" That's the first question every compliance person asks when we demo Parcha's AI. And it perfectly captures why so many compliance leaders are still hesitant about bringing AI into their programs. After dozens of conversations with compliance teams and BSA officers at banks and fintechs, I've noticed the same four concerns surface again and again. They're not worried about the technology itself – they're worried about trust, control, and accountability. 🎱 𝗧𝗵𝗲 "𝗕𝗹𝗮𝗰𝗸 𝗕𝗼𝘅" 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Traditional AI models are opaque by nature. They make decisions without showing their work. For BSA officers living in a world of audit trails and regulatory exams, "the algorithm said so" isn't an acceptable answer. If you can't explain why a suspicious activity alert was triggered (or wasn't), you're opening the door to regulatory scrutiny. 📋 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 "Will this system stand up to regulatory scrutiny?" Many AI systems lack the transparency and governance controls to meet regulatory standards. Just look at Evolve Bank's cease-and-desist order last year – automation without proper controls creates regulatory exposure, not efficiency. ⚖️ 𝗠𝗼𝗱𝗲𝗹 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Even transparent AI is only as good as its data. BSA officers know that unreliable models create risk: excessive false positives bury analysts in noise, while false negatives let real threats slip through. Both scenarios spell trouble during your next exam. 🧐 𝗛𝘂𝗺𝗮𝗻 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 This is the big one. What happens when automation overrides human judgment? Seasoned compliance officers bring context and nuance that machines miss. When something goes wrong, "the AI did it" won't fly with regulators. The responsibility still rests with your institution. These concerns are absolutely valid. They should be asked. They must be addressed. The good news? Agent Hub is built specifically to address every single one: • Compliance teams can quickly start testing Parcha's AI agents and seeing real world results in minutes • Detailed audit logs allow customers to understand how every decision is made • Accuracy is tracked in our dashboard so you can always see how AI agents are performing • Cases are easily reviewed and outputs evaluated in a familiar spreadsheet format making it easy for humans-in-the-loop to operate Parcha The most successful implementations start small, build trust, and keep humans firmly in control of critical decisions. That's why we built Agent Hub to make it easier than ever for compliance teams to create, customize, test and deploy AI agents. 𝗪𝗵𝗮𝘁'𝘀 𝗵𝗼𝗹𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻 𝗯𝗮𝗰𝗸 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲? I'd love to hear your thoughts – whether you're a skeptic, an early adopter, or in between. 👉 Link in comments to full article

🧩 What if someone gave you a step-by-step guide for auditing AI systems—with every legal reference already mapped out? That’s what Dr. Gemma Galdon Clavell, PhD has done with her AI Auditing Checklist, published under the EDPB’s Support Pool of Experts Programme. And I can’t overstate how helpful this resource is. 💡 Why it matters? We keep saying audits are central to trustworthy AI—but very few tools actually show how to do one in practice. This checklist breaks down every stage of the audit, mapping it directly to GDPR and AI Act requirements. It’s not just theory. It’s actionable. Here’s what makes it stand out: ✅ A clear structure across pre-processing, in-processing, and post-processing stages ✅ Templates for model cards, system maps, and documentation ✅ Legal hooks for every question—articles, recitals, and chapters already linked ✅ Detailed prompts for testing bias, fairness, and accountability ✅ Guidance on adversarial audits when internal access isn’t available Reading this, I finally felt like the gap between regulatory intent and practical implementation was being bridged. If you’re tasked with AI governance, compliance, or procurement, this checklist is worth bookmarking. Have you tried using a structured checklist like this in your audit work? What’s helped you most? #ResponsibleAI #AICompliance #GDPR #AIAudits #AIGovernance Did you like this post? Connect or Follow 🎯 Jakub Szarmach Want to see all my posts? Ring that 🔔 __________________________________ Did you like this post? Connect or Follow 🎯 Jakub Szarmach, AIGP, CIPM Want to see all my posts? Ring that 🔔

⚠️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.

At the FinTech Innovation Lab with Accenture and 39 leading partners in financial services and insurance, I saw firsthand that enterprises aren’t lacking tools or models. They’re lacking trust. To move from cool tech to meaningful adoption, enterprise AI must earn its way into regulated, high-stakes workflows. That means clear audit trails, transparent confidence scores, and well-defined process documentation — not just a black box making decisions. Agentic AI can and should operate with autonomy — but it needs built-in constraints, visibility, and feedback loops so that business operators, compliance teams, and regulators all stay aligned. If we want adoption, we have to start designing for change management — not just technical innovation. Let’s build AI that organizations can actually use. That starts with trust, auditability, and real workflows. See how we’re doing it at Multimodal: https://lnkd.in/eDwSepB8