Datavant to bring expertise from working with 80,000 healthcare providers, 100% of U.S. payers, and the top 20 life sciences companies to extend the AIUC-1 trust framework to healthcare
Agentic AI is entering healthcare at a pace that the industry’s existing regulatory and security frameworks were not designed to absorb. The compliance structures that govern how data moves, how decisions get audited, and how accountability is assigned in clinical environments were built around human workflows and relatively predictable software systems. AI agents that act autonomously across patient data, administrative processes, and clinical decision pathways do not fit cleanly into those structures – and the gap between what the technology can do and what existing governance can reliably certify is widening with each new deployment.
Datavant, which has spent years building the data collaboration infrastructure that underpins a significant portion of healthcare’s data sharing ecosystem, has joined the AIUC-1 Consortium to help close that gap from the standards side. The Consortium, which now spans more than 200 security leaders across technology, financial services, cybersecurity, and healthcare, is developing the certification framework that organizations will use to assess whether AI agents are safe, reliable, and governable enough to deploy in high-stakes environments. Datavant’s role is to ensure that the framework accounts for the specific demands of healthcare – data privacy obligations, regulatory alignment, and the clinical context that makes a poorly governed AI agent in a hospital a categorically different risk than one running in a back-office enterprise workflow.
For security and clinical engineering leaders already working through how to evaluate AI and connected systems in healthcare environments, the vendor and framework assessment challenge is real and immediate – the kind of structured evaluation guidance that covers risk-scoring, governance capabilities, and deployment accountability has become genuinely necessary as procurement decisions in this space carry consequences that extend directly to patient outcomes.
What the AIUC-1 Framework Is Built to Do
The AIUC-1 standard is technically grounded and focused specifically on agents rather than AI systems broadly – a distinction that matters because agents introduce governance challenges that static models do not. An agent that queries a database, makes a decision, triggers a downstream action, and logs its own activity creates an accountability chain that needs to be verifiable at every step, not just at the output. The AIUC-1 framework is designed to give organizations a certification basis for the full chain, making confident enterprise adoption possible in environments where trust is not optional.
Rajiv Dattani, co-founder of AIUC, was direct about why healthcare representation in the Consortium matters: “There is no sector where trust is more paramount than healthcare. Datavant’s deep experience managing data in the field is invaluable. By joining the Consortium, they are helping us ensure that the AIUC-1 standard is as effective in a hospital or clinic as it is in a financial institution or a tech enterprise.“
That last point reflects a real risk in how AI governance standards tend to develop. Frameworks built primarily around financial services or general enterprise use cases get applied to healthcare environments without adequate translation, and the gaps that result are not theoretical. Healthcare data carries different privacy obligations under HIPAA and state-level regulations. Clinical workflows have different tolerance thresholds for errors and audit requirements. AI agents operating in those environments need governance standards written with those specifics in mind rather than retrofitted from frameworks designed for different risk profiles.
What Datavant Brings to the Table
Datavant‘s contribution to the AIUC-1 standard is grounded in the position it has occupied in healthcare data infrastructure for years. As the platform through which a large portion of healthcare data collaboration moves – connecting providers, payers, researchers, and life sciences organizations – Datavant has developed governance practices for data privacy, access control, and regulatory compliance that are specific to the healthcare ecosystem rather than adapted from general enterprise models.
Dan Walsh, Chief Information Security Officer at Datavant, described the stakes clearly: “As enterprise adoption of agentic AI moves faster than traditional security frameworks can adapt, executives are seeking a unified trust infrastructure. By collaborating with the Artificial Intelligence Underwriting Company and leaders across the industry, we are ensuring that as the speed of AI expansion increases, healthcare-specific risk controls scale at the same rate.“
The phrase worth holding onto there is “scale at the same rate.” The historical pattern in healthcare technology has been that security and governance frameworks lag behind adoption – sometimes by years – leaving organizations to deploy systems under regulatory and risk conditions that have not yet been fully defined. The AIUC-1 effort is an attempt to break that pattern specifically for agentic AI, building the certification infrastructure before widespread deployment rather than after the first wave of incidents forces the conversation.
Why This Matters Beyond the Consortium
Healthcare organizations evaluating agentic AI deployments right now are making decisions without a settled framework for what good governance looks like in this context. The AIUC-1 standard, with Datavant’s healthcare-specific input, is working toward changing that – giving procurement teams, CISOs, and clinical engineering leaders a certification reference point that reflects the actual demands of clinical environments rather than a generalized enterprise baseline.
The broader implication is that the organizations shaping these standards in their early stages carry disproportionate influence over what the governance landscape looks like once it matures. Datavant joining as an early adopter and contributor rather than a late adopter ratifying decisions made by others gives healthcare a meaningful voice in an outcome that will affect how AI agents operate across every corner of the industry.
Research and Intelligence Sources: AIUC-1, Datavant
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