How to Prove Trust in AI: A Governance Guide

We trust our AI… but can we prove it? That’s the question many leaders are quietly asking as new regulations like the EU AI Act and U.S. AI risk management frameworks come into play. You might have strong data practices and model documentation, but still lack the governance foundation regulators expect. We often see teams who have fairness principles written down but no accountability structure to enforce them. Or models being deployed without clarity on who’s responsible for ethical oversight. It’s not bad intent—it’s missing governance. Start by mapping your AI lifecycle to existing compliance processes. Make sure roles, sign-offs, and review checkpoints are explicitly defined—not just assumed. If your policies don’t yet cover bias testing or model monitoring, that’s your first governance gap to address. Discover where your organization really stands. It’s a quick 5-minute diagnostic that helps organizations gauge their readiness around AI policies, risk, and compliance: 👉 https://lnkd.in/ecrmHKgC

A key theme with our customers is the tenets of AI risk management, providing the foundation on which to build compliant and responsible AI. Part of our process is assessing the three pillars of AI risk management: technical excellent, operational control and ethical assurance. As part of our work, we're now supporting clients looking ahead to 2026: and what's coming from new EU, UK and US legislation (including the EU AI Act). To support this effort, we've got a brand-new whitepaper out that outlines the key legislation coming in 2026 that the entire C-Suite in enterprise businesses need to be aware of. We also outline how to work with this legislation most effectively, especially for businesses working across multiple jurisdictions and balancing conflicting guidance. Download the whitepaper here ➡️ https://lnkd.in/eNNUzP3A #AILegislation #AILaw #AI #AI2026 #AITrends #EnterpriseAI #AIGovernance #ResponsibleAI #AICompliance #EUAIAct

Managing AI risk is not optional. It’s is fundamental that the AI systems you build are safe and ethical for effective AI adoption in today’s complex digital and regulatory landscape. Sign-up below to see our whitepaper that allows you to take practical steps for AI risk management.

With Minister Gallagher’s recent announcement of the Australian Public Service AI Plan 2025, conversations about responsible and strategic AI adoption in government have never been more important.   Our IPAA ACT partner MinterEllison has published AI Governance Framework, a resource that explores key considerations for AI governance, risk management, and ethical implementation. It’s a practical guide for leaders navigating this fast-evolving space.   📖 Check it out here: AI Governance Framework: https://lnkd.in/gD89KNrd And if you haven’t yet, read the APS AI Plan (linked in the comments below 👇 )

Many organizations are finalizing their AI governance and compliance budgets right now — and discovering how much uncertainty still exists around what “good” looks like. Some teams need independent assurance on AI tools already in use — a clear view of whether systems meet current and emerging compliance expectations. Others are still defining governance frameworks and risk management processes, and need support translating policy goals into practice. At BABL AI, we’re working with clients on both sides of that spectrum — through audit and assurance engagements, as well as advisory and consulting support. If you’re navigating similar questions, we’re offering short, no-cost consultations through November — or come meet us in person at the IAPP Conference in Brussels in two weeks! Click the link in the comments to get in touch. #DPC25 #IAPP #AIGovernance #AI

#FraudAwarenessWeek | AI is revolutionising how organisations govern third-party relationships, elevating oversight from periodic reviews to continuous, intelligent risk governance. 🛡️ 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝘀𝗵𝗼𝘄 𝗵𝗼𝘄 𝗔𝗜 𝗳𝗹𝗮𝗴𝗴𝗲𝗱 𝗿𝗶𝘀𝗸𝘀 𝘁𝗵𝗮𝘁 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗵𝗲𝗰𝗸𝘀 𝗺𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 https://social.kpmg/dtb8we #KPMGForensic #DueDiligence #AI #YouCanWithAI #ExecutiveDueDiligence #FraudRiskManagement

#FraudAwarenessWeek | AI is revolutionising how organisations govern third-party relationships, elevating oversight from periodic reviews to continuous, intelligent risk governance. 🛡️ 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝘀𝗵𝗼𝘄 𝗵𝗼𝘄 𝗔𝗜 𝗳𝗹𝗮𝗴𝗴𝗲𝗱 𝗿𝗶𝘀𝗸𝘀 𝘁𝗵𝗮𝘁 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗵𝗲𝗰𝗸𝘀 𝗺𝗶𝘀𝘀𝗲𝗱 𝗶𝗻 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 https://social.kpmg/dtb8we #KPMGForensic #DueDiligence #AI #YouCanWithAI #ExecutiveDueDiligence #FraudRiskManagement

𝐀𝐮𝐝𝐢𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐢𝐧𝐠 𝐞𝐯𝐞𝐫𝐲 𝐚𝐠𝐞𝐧𝐭 𝐪𝐮𝐞𝐫𝐲 🤖 Across enterprise AI stacks, auditing every query and explaining the resulting actions is increasingly viewed as a baseline capability. Some teams adopt end-to-end tracing that attaches prompts, model decisions, data sources, and the rationale behind each step, creating auditable trails for governance and risk management. In one financial-services example, an agent’s query and its justification were logged alongside data provenance; this enabled rapid regulatory reviews and safer handling of sensitive information. The result was faster incident resolution, clearer accountability, and stronger trust among customers and regulators. The industry is watching how scalable explainability becomes a competitive differentiator. Interested readers are invited to share experiences or questions in the comments. #ArtificialIntelligence #MachineLearning #GenerativeAI #AIAgents #MindzKonnected

New AI rules are on the horizon — and they’re not optional.    From real-time oversight to risk management mandates, compliance will reshape how you build and deploy AI. We’re already helping clients prepare with AI governance frameworks that bake security and compliance into the stack. Start now, or scramble later 👉 https://lnkd.in/d3-NjN4F

Many teams fail their AI Impact Assessment by treating it as a checkbox exercise instead of a continuous AI Governance process under ISO 42001 AIMS. Ignoring ethical, societal, and human rights factors weakens Responsible AI outcomes and AI Risk Management maturity. True compliance comes from documenting mitigation actions, reassessing after model retraining, and involving experts across domains. A strong AIMS Implementation ensures transparency, accountability, and trust in every AI lifecycle stage. #ISO42001 #AIImpactAssessment #ResponsibleAI #AIGovernance #AICompliance #AIMSRisk #AIIA