ValidMind is now live on AWS Marketplace. Ship AI on AWS with confidence. ValidMind governs your models and agents so your teams don't have to slow down. Available now on AWS Marketplace ValidMind Inc is excited to announce that ValidMind is now available on the AWS Marketplace, making it faster than ever to bring rigorous AI governance to every model and agent your organization builds on AWS. ValidMind is natively integrated with the AWS AI and data ecosystem, including Amazon SageMaker and Amazon Bedrock AgentCore. That means your developers keep building where they already build, and your risk and compliance teams get the governance layer they need - automated, documented, and continuously monitored. See it in action: ValidMind x AWS integration walkthrough. Watch how ValidMind connects with AWS to automate governance workflows from model development through deployment. Native AWS AI governance Whether your team is fine-tuning models in SageMaker or orchestrating AI agents through Bedrock AgentCore, ValidMind plugs directly into those workflows. Governance, documentation, and monitoring happen automatically, not as an afterthought. Your developers don't change how they build. Your governance teams finally see everything they need. Automated documentation Model cards, risk assessments, and audit trails generated automatically as your models are built and deployed. Deep AWS integrations Native connectors to SageMaker pipelines and Bedrock Agent Core, no custom glue code required. Continuous monitoring Track model drift, performance degradation, and policy compliance in real time, across your entire AWS model fleet. Agent governance Extend governance to AI agents built on Bedrock, the same rigor you apply to models, now for autonomous systems. For financial institutions, insurance companies, and any regulated organization deploying AI on AWS, the Marketplace listing also means procurement is simple. ValidMind charges go through your existing AWS bill, with no separate vendor negotiation required. ValidMind Inc built ValidMind to meet AI teams where the work actually happens. Today, that work is happening on AWS. Get started on AWS Marketplace Subscribe in minutes. Governance starts on day one.

8 key insights from the AI Governance Symposium. AI governance is entering a new phase, and the organizations that succeed will be those that fundamentally redesign governance for scale, speed, and complexity. This fact was abundantly clear during yesterday's AI Governance Symposium in London. ValidMind produced the event, which operated under Chatham House Rules, in partnership with its co-host, the London Stock Exchange. Through a mix of keynote speakers, lightning talks, and a panel, the robust discussions provided a candid look at the challenges and solutions shaping the future of AI governance as it enters this next chapter. Here are some of the key insights drawn from the event: 1. AI Governance must shift from model-centric to business risk-centric thinking. A recurring insight was that traditional model-centric approaches are no longer sufficient. As generative and agentic AI systems are deployed across diverse business processes, risk increasingly manifests at the use case and operational level, not just within individual models. Rather than asking "Is this model valid?", organizations must ask: * What business process is being impacted? * What decisions are being automated or augmented? * What are the downstream consequences of failure? This reframing positions AI risk as a complex, interconnected business risk, requiring governance frameworks that extend beyond model inventories. 2. Model inventories remain essential, but must evolve. A lively debate emerged around whether the traditional model inventory is still fit for purpose in a world of universal foundation models. The prevailing view was not that inventories should be abandoned, but that what gets inventoried needs to change: organizations should be moving toward cataloguing business processes, use cases, and operational activities, not just models in the classical sense. At the same time, speakers were emphatic that a dynamic, continuously updated inventory remains the absolute cornerstone of effective model risk management. A static list that gets dusted off for regulators is no longer adequate. 3. Data-Centric governance is the new imperative. While institutions are accustomed to deeply analyzing numerical data for distributions and seasonality, they must now apply that same rigor to unstructured textual and synthetic data. How textual data is represented, formatted, and fed into Large Language Models (LLMs) significantly alters the outcomes. AI governance will increasingly require domain-specific data thinking and complex multimodal integration to ensure the data feeding these models is valid and secure. 4. Automation is no longer optional. A clear consensus emerged: manual governance cannot scale with AI adoption. As AI systems become more autonomous and widespread: * Validation must be increasingly automated * Monitoring must be continuous, not periodic * Governance tooling must integrate directly into AI workflows This mirrors lessons from other industries (e.g., software engineering, cybersecurity), where reliability is achieved through standardized, automated testing and controls, not manual oversight. 5. Governance is shifting from pre-production to post-production. With traditional statistical models, validators spent most of their time in pre-production testing. However, with the rise of autonomous agentic AI, pre-production validation can only cover a fraction of what the system might do in the wild. The argument was made that governance must now pivot heavily toward post-approval monitoring and real-time intervention. This includes engineering strict "escalation triggers" where an agent is forced to pause and request human approval before executing a high-risk action. 6. Proportionality is not the same as minimalism. Regulators have been clear that the principle of proportionality, or ensuring that controls are commensurate with the risk of a given model or use case, does not mean doing the minimum. It means doing the right things for the right cases. AI models frequently score highly on materiality (the impact if they fail) and complexity (the likelihood of failure), and firms should resist the temptation to use proportionality as a justification for lighter governance. Where AI amplifies risk through scale, opacity, or complexity, additional scrutiny is required. 7. Foundational model validation requires a new approach. Traditional validation methods are not feasible for large, externally hosted foundational models. Instead, organizations are shifting toward: * Outcome-based validation (performance, behavior, reliability) * Use-case-specific testing * Ongoing monitoring rather than one-time validation This represents a significant departure from classical model validation, reinforcing the need for lifecycle-wide governance. 7. "AI teaming" is required to govern AI at scale. Because AI adoption is growing exponentially, traditional governance teams are struggling to keep up using manual documentation and validation processes. The solution is "AI teaming" - equipping human governance experts with AI tools to automate the documentation, regulatory checks, and testing of low-to-medium risk models. By automating the governance of lower-tier models, organizations free up their human experts to focus 80% of their time on the riskiest AI deployments. 8. The future of AI Governance is dynamic, not static. Perhaps the most important takeaway is that governance itself must evolve continuously. Static frameworks will not keep pace with rapid model evolution, new AI capabilities (e.g., agents, multimodal systems), or expanding regulatory expectations. Instead, organizations should build: * Flexible, principle-based frameworks * Feedback-driven governance systems * Adaptive controls that evolve with use cases In conclusion... The shift from models to systems, from validation to monitoring, and from manual to automated control represents a fundamental transformation. Organizations that succeed will be those that embrace this shift early. It will require a rethinking around the tools tools and the underlying assumptions about risk, accountability, and control. At ValidMind, ValidMind Inc see this as a defining moment for the industry and an opportunity to build governance frameworks that are not only robust, but truly scalable in the age of AI. Presentations. Keynote: market focus: AI Governance. Sidhartha Dash, Chief Researcher, Chartis Research Loading... Lightning talk: scaling AI for Financial Services. Sayantan Biswas, Senior Partner Development Specialist - Financial Services and Insurance, Amazon Web Services Page 1 / 8 AI Governance at scale. Kristof Horompoly, Head of AI, ValidMind David Asermely, Head of Growth Strategy & Development, ValidMind Loading...