Opinion of: Danor Cohen, Co-Founder and Chief Technology Officer of Kerberus
In 2025, crypto risk is a river. AI is turbocharging scam. Deepfake pitches, voice clones, synthetic support agents – all these are no longer fringe tools but frontline weapons. Last year, crypto scams probably hit a record high. Crypto fraud profits reached at least $ 9.9 billionPartially driven by Generative AI powered methods.
Meanwhile, in 2025, more than $2.17 billion was stolen – and that’s in the first half of the year. Personal wallets are now compromised for nearly 23% of stolen funds cases.
However, the industry has essentially responded with the same stale toolkit: audits, blacklists, refund promises, user awareness drives and post-incident write-ups. They are reactive, slow and ill-suited for a threat that evolves at machine speed.
AI is Crypto’s alarm bell. Tells us how weak the current structure is. Unless we move from patchwork reaction to baked-in-resilience, we risk a collapse not in price, but in confidence.
The AI has reshaped the battlefield
Scams involving deepfakes and synthetic identities have gone from making headlines to mainstream tactics. Generative AI is used to measure lures, clone voices and trick users into sending funds.
The most significant shift is not just a matter of scale. It’s speed and personalization that do the trick. Attackers can now replicate trusted environments or people almost instantly. The move toward real-time defense must also be accelerated—not just as a feature but as an integral part of the infrastructure.
Outside of the crypto sector, regulators and financial authorities are waking up. The monetary authority of Singapore Published A Deepfake risk advisory to financial institutions, signaling that systematic AI fraud is on its radar.
The threat has changed; The industry’s security mindset is non-existent.
Reactive security leaves users as walking targets
Crypto security has long relied on static defenses, including audits, bug bounties, code audits and blocklists. These tools are designed to identify code vulnerabilities, not behavior fraud.
While many AI scams focus on social engineering, it is also true that AI tools are increasingly being used to find and exploit vulnerabilities in code, automatically scanning thousands of contracts.
The risk is twofold: technical and human.
When we rely on blocklists, attackers simply spin up new wallets or phantom domains. When we rely on audits and reviews, exploitation lives on. And when we treat every incident as a “user error,” we absolve ourselves of responsibility for systemic design flaws.
Related: Crisis management for CEX during cybersecurity threats
In traditional finance, banks can block, reverse or freeze suspicious transactions. In crypto, a signed transaction is final. And this end is one of the crowning features of Crypto and becomes its heel when the fraud is immediate.
Moreover, we often advise users: “Do not click unknown links” or “verify addresses carefully.” These are acceptable best practices, but today’s attacks usually come from trusted sources.
No amount of caution can keep up with an opponent who constantly adapts and personalizes attacks in real time.
Embedded transaction logic fabric protection
It’s time to change from defense to design. We need transaction systems to react before the damage is done.
Consider wallets that detect anomalies in real time and not only flag suspicious behavior but also intervene before damage occurs. This means requiring extra confirmations, temporarily holding transactions or checking intent: is it with a known counterparty? Is the pattern worth it? Does the address indicate a history of past scam activity?
The infrastructure must support shared intelligence networks. Wallet services, nodes and security providers must exchange behavior signals, threat reputations and anomaly scores with each other. Attackers should not hop across silos relentlessly.
Also, fraud detection frameworks at the contract level analyze contract bytecode to flag phishing, ponzi or honeypot behavior in smart contracts. Again, these are retrospective or layered tools. What is critical now is the migration of these capabilities to user workflows – to wallets, signing processes and transaction verification layers.
This method does not require heavy AI everywhere; This requires automation, distributed detection loops and coordinated risk consensus, all embedded in transaction lines.
If the crypto doesn’t act, the narrative is lost
Let the regulators define the fraud protection architecture, and we’ll be done. But they don’t wait. Regulators are effectively preparing to regulate financial fraud as part of algorithmic oversight.
If crypto doesn’t voluntarily adopt systemic protections, regulation will impose them – likely through strict frameworks that prevent innovation or enforce centralized controls. Industry can lead its own evolution or it can legislate for it.
From defense to certainty
Our job is to restore trust. The goal is not to make hacks impossible but to make the loss irreversible and very rare irreversible.
We need “insurance level” behavior: transactions that are effectively monitored, with fallback checks, pattern fuzzing, anomaly pause logic and shared threats built in.
We must challenge the dogmas. Self-custody is necessary but not sufficient. We should stop treating security tools as optional – they should be the default. Education is important, but design is decisive.
The next frontier is not speed or yield; This is fraud resilience. Innovation should flow not from how fast blockchains are, but from how reliably they prevent malicious flows.
Yes, AI has exposed weak spots in Crypto’s security model. But the threat isn’t smarter scams; It is our refusal to change.
The answer is that AI cannot be embedded in every wallet; It’s to build systems that make AI-powered deception useless and undeniable.
If defenders remain reactive, issuing postmortems and blaming users, deception will continue to outpace defense.
Crypto doesn’t need to outsmart AI in every battle; It should increase this by embedding trust.
Opinion of: Danor Cohen, Co-Founder and Chief Technology Officer of Kerberus.
This article is for general informational purposes and is not intended to be and should not be construed as legal or investment advice. The views, thoughts, and opinions expressed herein are those of the author alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.
