{
  "id": 189137,
  "title": "Trusted AI data becomes the missing link as enterprises push models into production",
  "url": "https://urgent.news/2026/08/05/trusted-ai-data-becomes-the-missing-link-as-enterprises-push-models",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-05T20:20:27.000Z",
  "source": {
    "name": "SiliconANGLE",
    "slug": "siliconangle",
    "url": "https://siliconangle.com/2026/08/05/trusted-ai-data-enables-enterprise-production-ai-blackhat/"
  },
  "original_language": "en",
  "account": "Trusted AI data is becoming a critical factor for enterprises aiming to scale their AI models from pilot projects to production. As companies move beyond experimentation, they are realizing that the biggest hurdle is ensuring the trustworthiness of the data that feeds their models. This challenge is compounded by the scattered nature of data found in various software-as-a-service applications, cloud platforms, on-premises systems, and even employee laptops. To overcome this, establishing a governed foundation for data is crucial for moving AI into production, according to Robin Braun, vice president of AI business development and hybrid cloud at Hewlett Packard Enterprise Co. Braun emphasizes that data is the bedrock of AI, and organizations must trust their data to successfully deploy models at scale. Organizations often have data spread across SaaS applications, the cloud, on-premises systems, and even employee laptops, raising questions about how a model was trained, the parameters used, governance, and guardrails. Ian Williamson, senior vice president of global alliances at BigID Inc., highlights the growing issue of shadow AI deployments that evade compliance oversight. BigID’s scanning is uncovering rogue models and shadow AI, which highlights the need for visibility and governance. Once sensitive data is classified, protecting it becomes increasingly important, especially as regulated industries are moving AI workloads to on-premises environments rather than relying on shared cloud services. Fortanix Inc.'s Patrick Conte explains how their solution secures data at rest, in motion, and during processing within GPU memory, providing encryption to protect sensitive information. Enterprises are witnessing a shift back to on-premises infrastructure due to the stringent data protection requirements in sectors like banking, security agencies, and healthcare. Hewlett Packard Enterprise addresses the complexity of managing data across distributed environments through its unified platform, HPE GreenLake. This platform offers a hybrid cloud control plane that connects on-premises infrastructure with cloud services under a single governance model, simplifying policy application and enabling consistent management across different environments. Braun stresses the importance of partnering with trusted vendors to streamline the process and avoid the challenges of constructing a comprehensive infrastructure from scratch. The discussion took place at Black Hat USA, where Braun, Williamson, and Conte shared insights on the role of trusted AI data in governance, discovery, and encryption strategies as enterprises scale their AI deployments into production. The interview is part of SiliconANGLE Media's and theCUBE's coverage of the event, highlighting the critical role of trusted AI data in the successful implementation of AI models in enterprise settings.",
  "summary": "Trusted AI data is emerging as the deciding factor between organizations that successfully scale artificial intelligence and those still stuck cycling through pilots. As companies move beyond experimentation, many are discovering that the biggest obstacle isn’t building models — it’s knowing whether the data feeding those models can be trusted. That gap between ambition and […] The post Trusted…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}