Over the next three to five years, both governments and the private sector will need to rapidly adapt identification and mitigation protocols as adversaries move from AI-assisted to AI-enabled sanctions evasion and proliferation financing (PF), a new research paper warns.
The ), a UK-based defense and security think tank, defines PF as the use of funds or financial services to acquire, develop or otherwise deal in weapons of mass destruction (WMD). It states, “North Korea and Iran are now developing and deploying AI models to aid with sanctions evasion activities.”
Key findings include the fact that AI is now capable of mass producing high-quality fraudulent documents, as well as automating what the report describes as “the administrative minutia of managing extensive shell company networks.” AI powered systems, it states, can also “analyze blockchain patterns in real time to dynamically adjust cryptocurrency mixing strategies, effectively evading detection tools.”
In addition, it says, “[tools such as generative AI] which can produce sophisticated fraudulent identification documents, for example, have helped North Korea perpetrate phishing attacks against Western companies.”
, chief analyst at Greyhound Research, said that the RUSI report matters “because it names the right structural shift. AI is not creating sanctions evasion from thin air, it is compressing and scaling methods that already work.”
He pointed out that none of the sanction-evading techniques such as fraudulent documents, synthetic identities, shell companies, hidden beneficial ownership, crypto laundering, and others are new. “What changes is the speed, quality, volume and coordination with which these methods can now be assembled,” he said.
According to Gogia, “the distinction between AI-assisted and AI-enabled activity is central. AI-assisted evasion uses AI for discrete tasks: writing a better email, producing a cleaner document, generating a stronger false profile, translating a pitch, summarizing regulations or preparing a plausible job application. AI-enabled evasion is more serious.”
A ‘structural asymmetry’
This tactic, he said, “begins to coordinate the system itself. It links identity, documents, ownership structures, payment routes, cloud access, crypto wallets, API calls and timing. The difference is not whether AI helps someone fake a document. The difference is whether AI begins to orchestrate the deception.”
That is why the report’s findings should worry enterprise leaders, he noted: “Many organizations still assume the bad actor is mostly human, mostly linear and mostly slow. That assumption is expiring. AI lets adversaries run more attempts, with fewer errors, across more channels, in more languages, with better paperwork and greater patience than most enterprise review processes can absorb. This is not a tale of genius criminals discovering magic. It is the story of ordinary controls meeting industrialized plausibility.”
The evidence today, he pointed out, is strongest around tactics such as identity fraud, document fraud, synthetic personas, remote-worker deception, phishing, social engineering, crypto obfuscation and workflow abuse. “Fully autonomous evasion networks sit on the horizon,” he said. “They are serious, but they are not yet the everyday baseline.”
This distinction matters, said Gogia: “If enterprises obsess over cinematic autonomous agent scenarios while leaving remote hiring, vendor onboarding, payment approvals, and document review full of holes, they will lose in the most prosaic way imaginable.”
The report, he said, also gets the “asymmetry” right. “Offensive actors can learn across the ecosystem,” he said. “They can scrape open information, reuse leaked records, study enforcement patterns, test onboarding forms, inspect public procurement data, watch court filings, probe compliance thresholds and [use the information to] refine their behavior.”
Defenders, by contrast, are hemmed in by privacy rules, fragmented data, explainability requirements, jurisdictional boundaries, conservative operating models and siloed technology estates. “Offensive AI learns broadly,” he said. “Defensive AI often learns from fragments. That is the structural asymmetry.”
He explained that the regulatory landscape also amplifies the problem, in that regulatory bodies “still speak in separate dialects. [For example] the EU AI Act pushes organizations toward stronger obligations for high-risk AI. NIST-style frameworks push risk management, transparency, and governance.”
A trust architecture problem
Financial Action Task Force (FATF) expectations push national risk assessment and counter-proliferation controls, he noted, while banking regulators focus on model risk, accountability and operational resilience. “None of these streams is irrelevant. The trouble is that criminals do not organize themselves around regulatory workstreams. They organize around outcomes.”
What that means, said Gogia, “is that enterprise cannot wait for a clean global rulebook. It will not arrive in time. CIOs, CISOs, compliance officers and boards need a working governance model now. They need privacy-preserving analytics, controlled data environments, audit trails, legal safeguards and clear model-risk accountability.”
He said that enterprise IT managers should treat the situation as a trust architecture problem rather than a narrow sanctions-screening problem. “The uncomfortable truth is that AI is not simply helping bad actors write better phishing emails or forge tidier documents,” he noted. “It is helping them manufacture legitimacy across a chain of enterprise workflows.”
Likely outcome an ‘AI arms race’
Report author Arnold also noted that there are signs that cyber criminals have discovered new AI technologies and abilities that legitimate enterprises could adopt for legitimate applications.
History, he said, “is replete with [criminals] developing novel solutions to tough problems, [which are] later adopted by law enforcement. Much of our anti-financial crime policy is effectively a response to bad actors exploiting systems or using technology in novel ways to perpetrate crimes. In this scenario, I think an ‘AI arms race’ between enforcement authorities and bad actors is the most likely outcome.”
Gogia added, “the baddies are not teaching enterprises how to invent AI. They are teaching enterprises where trust is leaking. That is the lesson worth taking seriously.”
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