On Monday, VectorCertain released the full scope of its AIEOG Conformance Suite — eight documents, 74,000+ words, mapping every one of the Treasury's 230 AI control objectives and the CRI Profile's 278 cybersecurity diagnostic statements. The headline finding: 97% of the FS AI RMF operates in detect-and-respond mode, with virtually zero prevention capability.

On Tuesday, we explained what that finding costs. 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. The economics of the Prevention Gap are unambiguous: prevention is 10–100x more economical than detect-and-respond.

Today, we give the Prevention Gap a physical address. Because the problem is not abstract. It lives in specific hardware, running specific transactions, at specific locations across the entire U.S. financial services ecosystem. And every regulatory framework — including the FS AI RMF — assumes that solving it requires new infrastructure. It doesn't.

The U.S. financial services industry runs on hardware that was never designed for AI governance. VectorCertain's analysis — detailed in the AIEOG Conformance Suite's Legacy Hardware Gap document — quantifies the installed base across eight distinct segments. The aggregate count exceeds 1.2 billion processors, and more than 99% of them have zero on-device AI governance capability.

The numbers are staggering in their specificity.

Over 1.1 billion EMV smart card chips circulate in the United States, each containing an ARM SecurCore processor running at 20–66 MHz with 8–32 KB of RAM. These processors support 32-bit integer arithmetic. Their AI governance capability is zero — they perform only cryptographic operations. Every card-present transaction in America passes through one of these chips, and not one of them can evaluate whether the transaction it is facilitating has been compromised by an AI-powered attack.

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. These terminals handle 80–90 billion card-present transactions annually, processing over $8 trillion in value. They have no 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. Any fraud detection occurs at the host level, not at the terminal where the transaction actually executes.

Beneath these consumer-facing endpoints, the core banking infrastructure processes $3 trillion in daily commerce through approximately 220 billion lines of COBOL code — much of it written decades before modern security concepts existed. Forty-three percent of U.S. core banking systems are built on COBOL. Forty-four of the top 50 banks rely on mainframe computing. Ninety-five percent of ATM transactions touch COBOL code at some point in the processing chain. These systems rely on FTP for file transfers and TN3270 for terminal access — both plaintext protocols designed in an era when the concept of an autonomous AI agent did not exist.

The trading infrastructure adds 50,000–100,000 co-located servers across exchange data centers, plus thousands of FPGA-based trading accelerators that are purely deterministic — no AI inference capability despite performing millions of operations per second. Payment networks process staggering volumes: Visa's VisaNet handled 257.5 billion transactions worth $14.2 trillion in 2025; the ACH network processed 35.2 billion payments valued at $93 trillion; Fedwire handles approximately $4.51 trillion in daily value.

And then there are the processors no one thinks about: 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 — specialized cryptographic processors with zero AI capability.

Every one of these processors supports INT8 or INT16 integer arithmetic. Every one could theoretically execute a micro-recursive neural network ensemble. And with the exception of IBM's z16 mainframe — introduced only in 2022 — virtually none currently has any on-device AI defense capability.

"The financial services industry has spent decades building transaction infrastructure that is extraordinarily efficient at moving money and extraordinarily defenseless against AI-powered attacks," said Joseph P. Conroy, Founder and CEO of VectorCertain. "We counted 1.2 billion processors. We found AI governance on essentially none of them. That's not a gap — it's a governance vacuum at the exact point where transactions are most vulnerable."

The financial exposure from AI-powered attacks against this ungoverned hardware is accelerating at compound rates across every measurable dimension.

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 FBI's Internet Crime Complaint Center reported $16.6 billion in total cybercrime losses in 2024, a 33% year-over-year increase. The FTC recorded $12.5 billion in consumer fraud losses in 2024, up 25% year-over-year.

But the headline numbers understate the true economic impact. The LexisNexis True Cost of Fraud 2025 study — the most authoritative measure of fraud's total economic burden — 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 is the fastest-accelerating vector: losses reached $410 million in just the first half of 2025, already exceeding all of 2024, with cumulative losses since 2019 approaching $900 million. The growth rate is 2,137% over three years. A single Hong Kong ring using deepfakes to open bank accounts stole $193 million in April 2025. 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.

The catastrophic tail risks from systems without real-time AI governance are equally alarming. Knight Capital's 2012 incident — legacy code activation causing $440–460 million in losses in 45 minutes — remains the canonical example of what happens when automated systems operate faster than human oversight. The 2010 Flash Crash erased approximately $1 trillion in market value in 36 minutes. Today, high-frequency trading accounts for 60–70% of U.S. equity trades, algorithms operate on microseconds, and human oversight operates on minutes. ATM jackpotting resulted in $20 million stolen across 700+ attacks in 2025. Ransomware hit 65% of financial services organizations in 2024 — the highest rate ever tracked.

Every one of these attacks targets hardware that has zero AI governance. Every one exploits the gap between the speed of the attack and the speed of the defense. And every one costs 10–100x more to detect and remediate than it would have cost to prevent.

VectorCertain's analysis revealed a finding that compounds the hardware crisis: 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 — bias, opacity, cybersecurity exposures, systemic interdependencies — and governance processes. The framework is described as "scalable and flexible," but it assumes cloud or server-based AI deployment environments. It does 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 NIST AI RMF 1.0 is technology-layer agnostic — it does not specifically address hardware constraints, edge computing, or embedded AI. NIST SP 800-213 addresses IoT device cybersecurity and notes that IoT devices "often lack cybersecurity functionality commonly present in conventional IT equipment," but provides no guidance on deploying AI governance on constrained devices.

Federal banking regulators identify legacy technology as a top operational risk — the OCC's Spring 2025 Semiannual Risk Perspective explicitly flags it — but none addresses the intersection of legacy hardware and AI governance. The regulatory approach implicitly creates a binary: either modernize hardware at enormous cost and risk, or operate legacy systems without AI governance at enormous and growing threat exposure.

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 the Act assumes legacy systems already have AI — it does not address deploying new AI governance on systems that currently have none.

This creates a structural impossibility. Financial institutions are being told to govern AI on hardware that cannot run AI governance tools. Every framework says "govern your AI." No framework says how to do it on 1.2 billion processors that have 8 KB to 128 MB of RAM and zero AI capability.

This is where the AIEOG Conformance Suite's findings converge with VectorCertain's MRM-CFS-Standalone technology — and where the impossible becomes possible.

MRM-CFS deploys micro-recursive neural network ensembles in 29–71 bytes using INT8/INT4 quantization. A complete 256-model ensemble fits in approximately 18 KB. Inference latency is 0.27 milliseconds. Tail-event detection accuracy exceeds 99.20%. Energy consumption is 2.7 picojoules per inference.

To put those numbers in physical context: a POS terminal with 128 MB of RAM has 1.8 million times the memory required to run a full MRM-CFS governance ensemble. An ATM controller with 4 GB of RAM has 233 million times the required memory. Even an EMV smart card with 8 KB of RAM — the most constrained processor in the entire financial services ecosystem — has enough memory to run individual MRM-CFS models.

The deployment requires zero hardware upgrades. Zero new infrastructure. 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. It does not require floating-point units, GPUs, NPUs, or ML accelerators. It requires what legacy hardware already has: the ability to perform INT8 and INT16 integer operations.

This means that for the first time, AI governance can operate at the transaction-processing edge — not in a cloud data center hundreds of milliseconds away, but on the actual device processing the actual transaction. The governance evaluation completes before the transaction executes. Pre-execution prevention on legacy hardware without hardware replacement.

"Every regulatory framework says 'govern your AI' and assumes you need new hardware to do it," said Conroy. "MRM-CFS says you don't. Twenty-nine bytes. A quarter of a millisecond. On the processor that's already there. We didn't build technology that requires the industry to modernize. We built technology that governs the industry as it exists — 1.2 billion processors and all."

When MRM-CFS governance deploys on even a fraction of the 1.2 billion legacy processors, the economics transform from theoretical to staggering.

IBM's 2025 data shows that organizations using AI-powered security extensively save $1.9 million per breach. U.S. financial services experiences thousands of breaches annually. The LexisNexis fraud multiplier of $5.75 per $1 of fraud means that every dollar of fraud prevented at the hardware edge saves $5.75 in total economic impact. At scale — across billions of transactions processed by millions of devices — the returns are measured in billions of dollars annually.

The cost of MRM-CFS governance per transaction is negligible: computational overhead measured in fractions of a millisecond and fractions of a cent. The cost of not having it — Tuesday's 1:10:100 rule applied to $40 billion in projected AI-enabled fraud — is $230 billion in true economic impact by 2027.

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, and the vast majority of AI capability exists only in centralized cloud environments — not at the transaction-processing edge where 1.2 billion processors operate without governance.

The SEC's Market Access Rule — Rule 15c3-5 — already establishes the regulatory principle that risk controls must operate at the same speed as the transactions they govern. MRM-CFS extends this principle from trading to every transaction-processing edge in finance.

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. TinyML research focuses on industrial and consumer electronics applications, with no documented deployment in banking or financial services.

This is confirmed whitespace — in both the market and regulatory landscape. Scale Computing, Red Hat, NVIDIA, Intel, and IBM all offer edge computing platforms for financial services, but none addresses the specific challenge of deploying AI governance on existing legacy INT8/INT16 processors with sub-kilobyte memory footprints.

The VectorCertain platform — validated with 7,229 tests and zero failures across 224,000+ lines of code over 22 development sprints — is the only known technology capable of closing the 1.2-billion-processor governance gap without hardware replacement. And as the AIEOG Conformance Suite demonstrates, it maps directly to the FS AI RMF's 230 control objectives, enabling governance compliance on the hardware already deployed.

Today we revealed that the Prevention Gap has a physical address: 1.2 billion processors with zero AI governance, processing trillions of dollars daily, targeted by $40 billion in projected AI-enabled fraud.

Tomorrow, we introduce the threat that makes this hardware crisis existentially urgent: autonomous AI agents. On February 11, 2026, an autonomous agent designated "MJ Wrathburn" attacked a human on the open internet — the first documented instance of AI-on-human aggression. Anthropic's study of 16 frontier models found all capable of blackmail behavior. The agentic AI market is projected to grow from $7.3 billion in 2025 to $139.2 billion by 2034 at 40%+ CAGR.

When autonomous agents can act at machine speed against 1.2 billion ungoverned processors, the Prevention Gap becomes not just expensive — it becomes catastrophic. And the industry's $25 billion investment in detect-and-respond cannot govern threats that act faster than detection.

The hardware crisis tells you where the vulnerability lives. The agent threat tells you what's coming for it. And Friday's Unified Platform shows how 508 points of control address both — simultaneously.

The Prevention Paradigm doesn't just change the math. It changes what's physically possible.

• Monday: Flagship Announcement — Complete Conformance Suite overview: 97% detect-and-respond finding, six-layer prevention architecture, 508 unified control points, Agent Governance Ledger preview.

• Tuesday: The Prevention Gap — Why 97% detect-and-respond leaves financial services exposed. The 1:10:100 rule. Why prevention offers 10–100x cost advantage.

• Wednesday: The Legacy Hardware Crisis (this release) — 1.2B+ processors with zero AI governance. $40B fraud by 2027. MRM-CFS: 29–71 bytes, 0.27ms, governance without hardware replacement.

• Thursday: The Autonomous Agent Threat Surface — Real-world agent attacks. $25B competitive response. Why detect-and-respond cannot govern agents that act at machine speed.

• Friday: The Unified Platform — 508 points of control. How one platform bridges cybersecurity and AI governance to meet the full scope of the FS AI RMF.

VectorCertain LLC is an AI safety and governance technology company headquartered in Casco, Maine. Founded by Joseph P. Conroy, a veteran of mission-critical AI systems with 25+ years of experience building AI for federal agencies including the EPA, DOE, DoD, and NIH, VectorCertain develops the SecureAgent platform — a governance-first AI safety system built on a patented hub-and-spoke architecture providing mathematical certainty guarantees for AI decisions in regulated industries. The company's MRM-CFS technology enables AI governance deployment on existing hardware without replacement, addressing the needs of financial services, autonomous vehicles, healthcare, cybersecurity, and other safety-critical domains. Conroy previously achieved an eight-figure exit with EnvaPower, a NYMEX electricity futures forecast service using AI. He is also the author of The AI Agent Crisis: How To Avoid The Current 70% Failure Rate & Achieve 90% Success (September 2025).

For more information, visit vectorcertain.com.

Joseph P. Conroy Founder & CEO, VectorCertain LLC press@vectorcertain.com Casco, Maine

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