VectorCertain released the full scope of its AIEOG Conformance Suite, consisting of eight documents mapping every one of the Treasury's 230 AI control objectives and the CRI Profile's 278 cybersecurity diagnostic statements. The analysis found that 97% of the FS AI RMF operates in detect-and-respond mode with virtually zero prevention capability. According to the 1:10:100 rule, for every dollar spent preventing an AI governance failure, organizations spend ten dollars detecting it and a hundred dollars remediating it.
The company's analysis quantifies what it calls "The 1.2-Billion-Processor Governance Deficit" across the U.S. financial services industry. Over 1.1 billion EMV smart card chips circulate in the United States, each containing an ARM SecurCore processor with zero AI governance capability. More than 10 million POS terminals operate across the country, running ARM-based processors with as little as 128 MB of RAM, handling 80–90 billion card-present transactions annually. The ATM network adds another 520,000–540,000 controllers running Intel x86 processors with 4–8 GB of RAM, processing 10–11 billion transactions annually.
Core banking infrastructure processes $3 trillion in daily commerce through approximately 220 billion lines of COBOL code, with 43% of U.S. core banking systems built on COBOL and 44 of the top 50 banks relying on mainframe computing. Trading infrastructure adds 50,000–100,000 co-located servers across exchange data centers, plus thousands of FPGA-based trading accelerators. Payment networks process staggering volumes, with Visa's VisaNet handling 257.5 billion transactions worth $14.2 trillion in 2025, the ACH network processing 35.2 billion payments valued at $93 trillion, and Fedwire handling approximately $4.51 trillion in daily value.
The financial exposure from AI-powered attacks against this ungoverned hardware is accelerating at compound rates. The Deloitte Center for Financial Services projects GenAI-enabled fraud losses will reach $40 billion by 2027, up from $12.3 billion in 2023. The LexisNexis True Cost of Fraud 2025 study found that U.S. financial institutions now lose $5.75 for every $1 of direct fraud, up 25% from $4.00 in 2021. Applied to the Deloitte $40 billion projection, the true economic impact of AI-enabled fraud by 2027 reaches approximately $230 billion.
VectorCertain's analysis revealed that no regulatory framework governing AI in financial services addresses governance on edge, embedded, or legacy hardware. Every framework implicitly or explicitly assumes cloud-based or server-based AI deployment environments. The FS AI RMF's 230 control objectives focus on software-level AI risks but do not address how a POS terminal with 128 MB of RAM or an EMV smart card with 8 KB of RAM implements AI governance. The EU AI Act classifies AI systems used in credit scoring, fraud detection, risk assessment, and automated trading as high-risk, with compliance required by August 2026 for financial services use cases, but does not address deploying new AI governance on systems that currently have none.
VectorCertain's MRM-CFS technology deploys micro-recursive neural network ensembles in 29–71 bytes using INT8/INT4 quantization, with a complete 256-model ensemble fitting in approximately 18 KB. Inference latency is 0.27 milliseconds, tail-event detection accuracy exceeds 99.20%, and energy consumption is 2.7 picojoules per inference. The deployment requires zero hardware upgrades, zero new infrastructure, and zero changes to existing transaction processing logic. MRM-CFS executes on the integer arithmetic units that every one of these 1.2 billion processors already possesses.
IBM's 2025 data shows that organizations using AI-powered security extensively save $1.9 million per breach. Financial services AI spending reached $35 billion in 2023 and is estimated to hit $97 billion by 2027. Visa has invested $3.3 billion in AI and data infrastructure over the past decade, with its Advanced Authorization system preventing an estimated $28 billion in fraud annually. Mastercard invested $7 billion in cybersecurity and AI over five years, stopping over $35 billion in fraud losses. Yet 44% of North American financial institutions still primarily rely on manual fraud prevention processes.
VectorCertain's analysis across regulatory databases, commercial vendors, academic literature, and industry publications found no company explicitly providing AI governance frameworks specifically for edge or embedded hardware in financial services. The VectorCertain platform, validated with 7,229 tests and zero failures across 224,000+ lines of code over 22 development sprints, maps directly to the FS AI RMF's 230 control objectives, enabling governance compliance on the hardware already deployed.
