Daniel Whitenack is the founder and chief executive of Prediction Guard, which builds a self-hosted AI control plane for organizations handling sensitive information. His work addresses a fundamental enterprise problem: making AI useful without sacrificing data security, accountable decision-making, or control over consequential actions.
Whitenack earned a doctorate in physics at Purdue University, studying density functional resonance theory and unbound electronic systems. His subsequent data-science work explored how computational systems remain reproducible and understandable: in 2016, he described Pachyderm’s versioned data pipelines, which combine tracked datasets, containerized processing, and recoverable analytical histories.
At SIL International, he applied machine learning to underserved languages and contributed to Masakhane’s collaborative African-language translation research. He also investigated transfer learning for neural back translation in low-resource settings. As co-host of Practical AI, he has explored how machine learning, generative models, and agents operate outside controlled demonstrations.
Whitenack introduced an early Prediction Guard beta in 2023, initially focused on matching developers with suitable models and delivering reliable predictions through an API. The company expanded into private deployment, governance, and security across cloud, self-hosted, and air-gapped environments, announcing $3.7 million in seed funding in May 2025. Its infrastructure work has included deploying models on Intel Gaudi accelerators.
His approach to trustworthy AI
- Verify answers against their sources. Retrieval can supply accurate documents without guaranteeing that a generated answer represents them faithfully. Whitenack favors smaller factual-consistency models that compare answers with source material, limiting the cost and latency of additional large-model calls. Applications supporting field medics illustrate the consequences of confident errors.
- Treat prompt injection as an infrastructure threat. His defenses combine classification, semantic comparisons with known attacks, configurable thresholds, and policy enforcement around the model. Trusted model registries, vetted dependencies, server monitoring, and confidential computing extend protection across the deployment stack.
- Protect data throughout the application. Sensitive information can surface in prompts, retrieved documents, outputs, logs, caches, and server memory. Whitenack advocates PII detection or redaction, controlled security-event logging, and retrieval systems that preserve existing role-based permissions instead of exposing an organization’s entire knowledge base.
- Give agents bounded authority. Whitenack recommends least-privilege agent permissions, dry-run execution, and human approval before systems modify infrastructure or take other consequential actions. These safeguards make generated plans useful while keeping skilled operators responsible for what actually changes.
