VectorCertain LLC today disclosed its comprehensive 55-patent intellectual property portfolio, described as the first AI safety architecture built on a governance-first, permission-to-act paradigm. The portfolio spans autonomous vehicles, cybersecurity, healthcare, financial services, blockchain/DeFi, energy infrastructure, manufacturing, satellite systems, content moderation, and government AI certification.
Of the 55 patents, 21 have been filed — seven in December 2025 and 12 in January 2026 — with the remaining 18 in active development and scheduled for filing through 2026. The portfolio encompasses over 500 claims, with every filed application scoring 10.0/10 on independent quality assurance review.
“Artificial intelligence systems do not self-authorize. All AI decisions are subject to independent, runtime governance determining whether they may be trusted, relied upon, or acted upon. This is the core paradigm that unifies our entire 55-patent ecosystem,” said Joseph P. Conroy, Founder & CEO of VectorCertain LLC.
Unlike bolt-on safety layers or post-hoc auditing frameworks, VectorCertain’s patents are architected around a single principle: AI must earn permission to act, every time, through mathematically verifiable independent governance. This paradigm replaces model-centric safety, optimization-centric AI, and retrospective validation with governance-first, permission-to-act safety.
The ecosystem is organized in a three-layer hub-and-spoke architecture. Layer 1 consists of core safety governance hubs that define what is allowed, establishing the mathematical and epistemic foundations for AI trust, numerical safety, and execution permission. Layer 2 is a domain governance sub-hub for blockchain safety, extending core hubs under adversarial conditions. Layer 3 comprises 22 application spokes spanning 12 industry verticals, each applying governance defined by the hubs without redefining safety.
Key patents include HCF2-SG (Epistemic Trust Governance), which determines whether an AI decision is trustworthy through four-layer independence verification; TEQ-SG (Numerical Admissibility Governance), which monitors reduced precision effects and achieves consensus-preserving compression at 3.92–4.12X while maintaining ASIL-D compliance; and MRM-CFS-SG (Execution Governance), which runs 256 models in less than 50KB with greater than 99% tail-event accuracy.
VectorCertain validated its technology against more than 50 catastrophic failures spanning 2000–2024 across 11 industries, demonstrating that $1.777 trillion in losses were preventable. Examples include $476 billion in autonomous vehicle losses, $557 billion in financial fraud, $300 billion in manufacturing quality control, and $93 billion in energy grid failures.
The company’s architecture natively addresses 47+ regulatory frameworks, including ISO 26262 (ASIL-D) for autonomous vehicles, FDA 21 CFR Part 11 for healthcare, OCC SR 11-7 for financial services, and NIST AI RMF for government AI. Compliance is a continuous, real-time property of system operation, with every inference generating auditable evidence automatically.
Analysis of 1,600+ AI governance patents from IBM, 5,000+ AI patents from automotive OEMs, and portfolios from Google/DeepMind, Microsoft, and NVIDIA reveals consistent gaps where VectorCertain’s governance-first ensemble claims are novel. The hub-and-spoke structure prevents terminal disclaimer sprawl and obviousness collapse while enabling flexible licensing.
VectorCertain LLC is a Delaware corporation headquartered in Maine, founded by Joseph P. Conroy, a 30-year AI systems veteran who previously built mission-critical AI systems for the EPA, DOE, and Boeing. More information is available at vectorcertain.com.
