VectorCertain's AIEOG Conformance Suite reveals that 97% of the FS AI RMF operates in detect-and-respond mode with virtually zero prevention capability, creating what the company calls the Prevention Gap. 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. IBM's 2025 data showed the U.S. average breach cost hitting an all-time high of $10.22 million, making prevention 10–100x more economical than detect-and-respond approaches.
The company's analysis identifies a physical manifestation of this problem across the U.S. financial services ecosystem. VectorCertain's Legacy Hardware Gap document quantifies an installed base exceeding 1.2 billion processors, with more than 99% having zero on-device AI governance capability. This includes over 1.1 billion EMV smart card chips circulating in the United States, each containing an ARM SecurCore processor running at 20–66 MHz with 8–32 KB of RAM that perform only cryptographic operations. More than 10 million POS terminals operate across the country—the world's largest installed base—running ARM-based processors with as little as 128 MB of RAM, handling 80–90 billion card-present transactions annually worth over $8 trillion without on-device AI defense capability.
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 with any fraud detection occurring at the host level rather than at the terminal. 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 includes 50,000–100,000 co-located servers across exchange data centers plus thousands of FPGA-based trading accelerators that are purely deterministic with no AI inference capability.
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. Additional processors include 1.5–3 million banking IoT sensor processors across 78,000 bank branches, 100,000–200,000 currency counting and sorting processors, 850,000–940,000 embedded ATM card readers and encrypting PIN pads, and 30,000–75,000 Hardware Security Modules—all with zero AI capability.
The financial exposure from AI-powered attacks against this ungoverned hardware is accelerating rapidly. The Deloitte Center for Financial Services projects GenAI-enabled fraud losses will reach $40 billion by 2027, up from $12.3 billion in 2023—a 32% compound annual growth rate. 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.
Deepfake fraud represents the fastest-accelerating vector, with losses reaching $410 million in just the first half of 2025, already exceeding all of 2024, with cumulative losses since 2019 approaching $900 million and a growth rate of 2,137% over three years. Synthetic identity fraud—which the Federal Reserve calls the fastest-growing type of financial crime in the United States—generates estimated losses of $6 billion or more annually. Historical incidents like Knight Capital's 2012 legacy code activation causing $440–460 million in losses in 45 minutes and the 2010 Flash Crash that erased approximately $1 trillion in market value in 36 minutes demonstrate the catastrophic tail risks from systems without real-time AI governance.
VectorCertain's analysis reveals that no regulatory framework governing AI in financial services addresses governance on edge, embedded, or legacy hardware. The FS AI RMF's 230 control objectives focus on software-level AI risks and assume cloud or server-based AI deployment environments without addressing how constrained devices implement AI governance. The NIST AI RMF 1.0 is technology-layer agnostic and does not specifically address hardware constraints, edge computing, or embedded AI. Federal banking regulators identify legacy technology as a top operational risk but none addresses the intersection of legacy hardware and 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 assumes legacy systems already have AI rather than addressing deploying new AI governance on systems that currently have none. This creates what VectorCertain describes as a structural impossibility where financial institutions are told to govern AI on hardware that cannot run AI governance tools.
VectorCertain's MRM-CFS technology addresses this gap by deploying 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 of 0.27 milliseconds, tail-event detection accuracy exceeding 99.20%, and energy consumption of 2.7 picojoules per inference. The technology executes on the integer arithmetic units that every one of these 1.2 billion processors already possesses, requiring zero hardware upgrades, zero new infrastructure, and zero changes to existing transaction processing logic.
IBM's 2025 data shows that organizations using AI-powered security extensively save $1.9 million per breach, while the LexisNexis fraud multiplier of $5.75 per $1 of fraud means every dollar of fraud prevented at the hardware edge saves $5.75 in total economic impact. Financial services AI spending reached $35 billion in 2023 and is estimated to hit $97 billion by 2027, with Visa investing $3.3 billion in AI and data infrastructure over the past decade and Mastercard investing $7 billion in cybersecurity and AI over five years. Yet 44% of North American financial institutions still primarily rely on manual fraud prevention processes, with the vast majority of AI capability existing only in centralized cloud environments rather than at the transaction-processing edge.
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 company's 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 existing hardware without replacement.
