NOMINATION HIGHLIGHTS

Polygraf’s AI usage control layer directly solves the most significant blocker to enterprise and government AI initiatives: sensitive data risk. Organizations in finance, healthcare, defense, and government face mounting concerns around data leakage, synthetic manipulation, and opaque AI decisions. These risks prevent deployment in regulated and classified environments and create friction at the intersection of risk, compliance, and AI usage. Polygraf focuses on this unsolved layer and enables AI adoption where it was previously prohibited.

Unlike reactive monitoring tools that audit exposure after it occurs, Polygraf eliminates sensitive data risk in near real-time.. Sensitive data is masked before AI interaction, not audited after exposure. The platform prevents regulated data from ever leaving the trust boundary and protects against both inbound and outbound AI threats. Acting as a single enforcement layer across all AI interactions, it enables compliant AI use under GDPR, HIPAA, CCPA, FERPA, the EU AI Act, NIST-RMF, IL2-IL6, and other applicable frameworks. Every decision in its 17 purpose-built Small Language Model stack is traceable and auditable, providing full auditability across prompts, uploads, and outputs.

Rather than relying on resource-heavy LLMs for governance, Polygraf uses 17 purpose-built Small Language Models and an AI-on-AI approach that governs general AI with specialized AI. Its containerized, premise-agnostic deployment works on-prem, air-gapped, edge, or cloud environments, including classified setups where cloud-reliant competitors cannot operate. This delivers superior accuracy while reducing AI security compute and energy costs by 95% compared with LLM-based governance tools. Benchmarks show approximately 24% improvement in detection accuracy over commercial LLM-only and rules-based alternatives.

With more than 27,000 users across finance, insurance, healthcare, the public sector, and defense, Polygraf demonstrates sustained value and product-market fit at the point of highest enterprise friction. It reduces regulatory exposure, infrastructure cost, and operational risk while enabling AI efficiency. It delivers a production-ready governance layer while others are still whiteboarding or prototyping.

AI sustainability, transparency, and governance are becoming regulatory mandates. Polygraf advances responsible AI through privacy-by-design, explainability, and 95% less compute and energy than LLM-based enforcement models. Polygraf’s preemptive, real-time AI usage control layer redefines the category by integrating governance directly into AI workflows and delivers the trust required to use generative AI safely, securely, and compliantly.